Understanding autonomous vehicles: A systematic literature ...
LITERATURE REVIEW: UNDERSTANDING THE CURRENT STATE …€¦ · literature review: understanding the...
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LITERATURE REVIEW:
UNDERSTANDING THE CURRENT STATE OF AUTONOMOUS TECHNOLOGIES TO
IMPROVE/EXPAND OBSERVATION AND DETECTION OF MARINE SPECIES
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
THISREPORTISTOBECITEDAS:VERFUSSU.K.,ANICETO,A.S.,BIUW,M.,FIELDING,S.GILLESPIE,D.,HARRIS,
D.,JIMENEZ,G.,JOHNSTON,P.,PLUNKETT,R.,SIVERTSEN,A.,SOLBØ,A.,STORVOLD,R.ANDWYATT,R.2016.
LITERATURE REVIEW: UNDERSTANDING THE CURRENT STATE OF AUTONOMOUS TECHNOLOGIES TO
IMPROVE/EXPANDOBSERVATIONANDDETECTIONOFMARINESPECIES.REPORTNUMBERSMRUC-OGP2015-
015PROVIDEDTOIOGP,July2016.
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1 Contents 1 Contents........................................................................................................................................................3
2 Figures...........................................................................................................................................................7
3 Tables............................................................................................................................................................8
4 ExecutiveSummary.....................................................................................................................................11
4.1 Objectives...........................................................................................................................................11
4.2 Approach............................................................................................................................................12
4.3 Outcome.............................................................................................................................................13
4.3.1 UAS.................................................................................................................................................13
4.3.2 AUVandASV..................................................................................................................................14
4.3.3 Futurework....................................................................................................................................16
5 Quickguideforprospectiveusers...............................................................................................................18
6 Projectframeworkandapproach................................................................................................................22
7 Introduction.................................................................................................................................................24
7.1 Monitoringtypes................................................................................................................................25
7.1.1 Mitigationmonitoring....................................................................................................................25
7.1.2 Animalpopulationsurveys.............................................................................................................26
7.1.3 Focal-follows...................................................................................................................................27
7.2 Autonomousplatformtypes...............................................................................................................27
7.2.1 Poweredaircraft(UAS)...................................................................................................................27
7.2.2 Motorizedgliders(UAS).................................................................................................................28
7.2.3 Kites(UAS)......................................................................................................................................28
7.2.4 Lighter-than-airaircraftsystems(UAS)..........................................................................................29
7.2.5 Propellerdrivenunderwatercraft(AUV).......................................................................................30
7.2.6 Buoyancygliders(AUV)..................................................................................................................31
7.2.7 Poweredsurfacecrafts(ASV).........................................................................................................33
7.2.8 Self-poweredsurfacevehicles(ASV)..............................................................................................35
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7.2.9 Drifter(ASVandAUV).....................................................................................................................36
7.3 Sensortypes........................................................................................................................................37
7.3.1 Electro-opticalimagingsensors......................................................................................................37
7.3.2 PAMsystems..................................................................................................................................39
7.3.3 AAMsensors...................................................................................................................................42
7.3.4 Animal-bornetags..........................................................................................................................44
8 Evaluationofautonomoussystems.............................................................................................................44
8.1 Criteriaandmetrics............................................................................................................................44
8.1.1 Collectionofsurveydata................................................................................................................45
8.1.2 Mitigationmonitoring....................................................................................................................46
8.1.3 Operationalaspects........................................................................................................................47
8.1.4 Datarelay.......................................................................................................................................48
8.1.5 Furtherinformationgathered........................................................................................................49
8.2 Evaluationresults...............................................................................................................................50
8.2.1 UASplatforms.................................................................................................................................50
8.2.2 UAS-sensors....................................................................................................................................55
8.2.3 AUV/ASVplatforms......................................................................................................................57
8.2.4 AUV/ASV-sensors..........................................................................................................................61
8.3 Discussion...........................................................................................................................................65
8.3.1 How to use the evaluation results to find the appropriate autonomous platform / sensor
combination................................................................................................................................................65
8.3.2 Strengthandweaknessesofautonomousvehicletypes...............................................................71
8.3.3 Platformtypesformitigationmonitoring......................................................................................73
8.3.4 Platformtypesforpopulationmonitoringandfocal-follows.........................................................75
8.3.5 Technologiesthatmightbeusedinfuturefieldtrials....................................................................77
8.3.6 Datagapsandrecommendations...................................................................................................79
9 Furtherinformation.....................................................................................................................................86
9.1 Requirementsofautonomousvehiclesformarineanimalmonitoring..............................................86
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9.1.1 Mitigationmonitoring....................................................................................................................87
9.1.2 Animalpopulationmonitoring.......................................................................................................89
9.1.3 Focalanimalstudies.......................................................................................................................92
9.2 Currentknowledgeonautonomousvehicles.....................................................................................93
9.2.1 Independentsystems.....................................................................................................................93
9.2.1 Cooperativesystems....................................................................................................................116
9.3 Operationalaspectstoconsider.......................................................................................................117
9.3.1 TechnicalReliability......................................................................................................................118
9.3.2 MissionDuration–Powerconsiderations....................................................................................119
9.3.3 Logistics........................................................................................................................................119
9.3.4 RemoteOperation........................................................................................................................119
9.3.5 Interactionwithothermarine/airspaceusers..............................................................................120
9.4 Regulatory/politicalbarriers...........................................................................................................120
9.4.1 Regulationsfortheairspace.........................................................................................................120
9.4.2 Regulationsforthesea.................................................................................................................122
9.5 Technicalchallengesofmanaging,storingandanalysinglargeamountofdata..............................124
9.5.1 Electro-opticalimagingsensors....................................................................................................126
9.5.2 PAMsystems................................................................................................................................127
9.5.3 AAMsensors.................................................................................................................................129
10 Acknowledgements...............................................................................................................................130
11 Appendix...............................................................................................................................................131
11.1 Listofcriteriaandmetricsforplatform,sensoranddatarelay.......................................................131
11.1.1 Platform...................................................................................................................................131
11.1.2 Sensor......................................................................................................................................132
11.1.3 Datarelay.................................................................................................................................134
11.2 Shortlistofplatformsandsensors....................................................................................................135
11.2.1 Poweredaircrafts.....................................................................................................................135
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11.2.2 Kites..........................................................................................................................................137
11.2.3 Lighter-than-airaircraftsystems..............................................................................................137
11.2.4 Propellerdrivenunderwatercraftandpoweredsurfacecraft................................................138
11.2.5 Autonomousunderwaterbuoyancygliders.............................................................................143
11.2.6 Self-poweredsurfacevehiclesanddriftingsensorpackages...................................................145
11.2.7 PAM..........................................................................................................................................146
11.2.8 AAM.........................................................................................................................................148
11.3 Comparisonmatrices........................................................................................................................151
11.3.1 Listofplatforms.......................................................................................................................151
11.3.2 Platformtechnicaldimensions.................................................................................................155
11.3.3 Platformothertechnicaldetails...............................................................................................163
11.3.4 Platformcosts..........................................................................................................................167
11.3.5 Platformsurveycapabilities.....................................................................................................171
11.3.6 Platformoperationalinformation............................................................................................176
11.3.7 Platformmanningrequirements..............................................................................................184
11.3.8 Listofsensor............................................................................................................................188
11.3.9 Sensortechnicaldetails............................................................................................................190
11.3.10 Sensorcosts.............................................................................................................................193
11.3.11 SensorsurveycapabilitiesI......................................................................................................195
11.3.12 SensorsurveycapabilitiesII.....................................................................................................198
11.3.13 Sensoroperationalrequirements............................................................................................200
11.3.14 Sensormanningrequirements.................................................................................................203
11.3.15 Sensorfurtherinformation......................................................................................................204
11.3.16 ListofDataRelaysystems........................................................................................................207
11.3.17 Datarelaytechnicaldetails......................................................................................................209
11.4 Evaluationmatrices..........................................................................................................................211
11.4.1 UASsensorversusplatformmatrix..........................................................................................211
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11.4.2 ASV/AUVsensorversusplatformmatrix.................................................................................212
11.5 Suppliercontacts..............................................................................................................................216
11.6 Surveyingwildlifepopulations..........................................................................................................220
11.6.1 Estimatingabsolutepopulationdensityandabundance–anoverview.................................220
11.6.2 Methodstoestimatedetectionprobability.............................................................................222
12 GlossaryofTerms,AcronymsandAbbreviations..................................................................................225
13 Referencelist........................................................................................................................................228
2 Figures FIGURE1.DECISIONTOOLFORFINDINGTHEAPPROPRIATEPLATFORM/SENSORCOMBINATIONFORASPECIFICMONITORINGTASKAND
ANIMALTYPE.THEMONITORINGTYPESAREDIVIDEDINTOMITIGATIONMONITORINGINAREASEITHERCLEARFROMORBUSY
WITHOTHEROPERATIONALGEARORTRAFFIC,POPULATIONMONITORINGINTHESHORT-TERM(HOURS,DAYS)ORLONG-TERM
(WEEKS,MONTHS)ANDFOCAL-FOLLOWSCONDUCTEDWITHSTATICORMOBILESYSTEMS.ANSWERINGQUESTIONSQ1(WHICH
MONITORINGTYPESHOULDBECONDUCTED)WITHDIAGRAMAANDQ2(WHICHTYPEOFANIMALNEEDSTOBEMONITORED)
WITHDIAGRAMBWILLHIGHLIGHTTHEMOSTIMPORTANTSYSTEMREQUIREMENTSANDTHEANIMALBEHAVIOURPOTENTIALLY
TRIGGERINGDETECTION,LEADINGTOTHESENSOR/PLATFORMCOMBINATIONBESTSUITEDFORASPECIFICMONITORINGTASK.
......................................................................................................................................................................21
FIGURE2.EXAMPLESFORUAS:LEFTPICTURE:SKYPROWLERUAS©KROSSBLADEAEROSPACE,RIGHTPICTURE:LANDINGOF
CRYOWINGUAS.PHOTOBYNORUT..................................................................................................................28
FIGURE3.KITESUSI62TAKE-OFF.PHOTOBYTHAMM,2011..........................................................................................29
FIGURE4.THELIGHTER-THAN-AIRCRAFTSYSTEM"BLIMP-CAM"ANDCOMPONENTSNECESSARYFORFLIGHTS.PHOTOBYHODGSON,
2007..............................................................................................................................................................30
FIGURE5.PROPELLERAUVREMUS100INAFIELDSTUDY(LEFT,BYANREYSHCHERBINA,WHOI)ANDDETAILOFTHEMODEL
(RIGHT,KONGSBERGMARITIMEWEBSITE)..............................................................................................................31
FIGURE6.UNDERWATERBUOYANCYGLIDERSSEAGLIDER(LEFT)ANDSLOCUM(RIGHT)(IMAGES©DAMIENGUIHEN)..................32
FIGURE7.ANAUVBOATTYPEVIKNESDESIGNEDANDDEVELOPEDBYTHENORWEGIANUNIVERSITYOFSCIENCEANDTECHNOLOGYIN
CONJUNCTIONWITHMARINEROBOTICS(IMAGE©NORWEGIANUNIVERSITYOFSCIENCEANDTECHNOLOGY)....................33
FIGURE8.CATAMARANS:LEFTPICTURE:SPRINGER,ACATAMARANBASEDASVDEVELOPEDBYPLYMOUTHUNIVERSITY(IMAGE©
PLYMOUTHUNIVERSITY),RIGHTPICTURE:ASVC-ENDURO,ALARGESCALECATAMARANBASEDASV(IMAGE©ASV).........34
FIGURE9.THESEMI-SUBMERSIBLEASV-6300DESIGNEDBYC&CTECHNOLOGIESANDASV(IMAGEFROMWOLKING,2011)......35
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FIGURE10.UNDERWATERPHOTOGRAPHOFALIQUIDROBOTICSWAVEGLIDERSHOWINGTHESUBUNIT,WHOSEMOVEABLEFINS
CONVERTVERTICALMOTIONFROMWAVESINTOFORWARDPOWERANDTHESURFACEFLOAT(PHOTO,LIQUIDROBOTICS)......36
FIGURE11.THEUKNATIONALOCEANOGRAPHYCENTRESAUTONOMOUSVEHICLEFLEET......................................................96
FIGURE12.SUNFISHTRACKINGPROJECT(PINTO,2014)................................................................................................117
FIGURE13.CURRENTCOMMERCIALSMALLUASRULESINCOUNTRIESWITHSTRONGANDGROWINGMARKETSFORUASSYSTEMS
(BONGGAY,2016)..........................................................................................................................................122
FIGURE14.SCHEMATICDIAGRAMSOFFOURDIFFERENTSENSORDATAHANDLINGCOMBINATIONS.THESEAREINTENDEDPRIMARILYAS
EXAMPLESANDMANYMORECOMBINATIONSEXIST.A)SIMPLESENSINGANDDATASTORAGE,B)SENSOR,BASICPROCESSING
ANDDATASTORAGE,C)REAL-TIMETRANSMISSIONOFRAWDATAWITHPROCESSINGONSHORE,D)ONBOARDPROCESSINGTO
REDUCEDATAVOLUMEPRIORTOTRANSMISSIONOFAMINIMALAMOUNTOFDATA......................................................125
FIGURE15.EXAMPLESOFREALANDFALSEWHISTLEDETECTIONSBOTHWITHOUTANDWITHFULLSPECTROGRAMINFORMATION.A)A
FALSEDETECTIONCAUSEDBYTHEHYDRAULICSOFANEARBYENGINE.B)ADOLPHINWHISTLE.........................................129
3 Tables TABLE1.COMMONDESIGNFEATURESOFELECTRICBUOYANCYGLIDERS(FROMDAVISETAL.,2002ANDWOODANDMIERZWA,
2013)............................................................................................................................................................59
TABLE2.EVALUATIONMATRIXOFDETECTION/CLASSIFICATIONCAPABILITIESOFSENSORS(INCLUDEDINTHISREVIEW)CAPABLEOF
REAL-TIMEDETECTION.WITHTHEHELPOFTHESESENSORS,ONEISCAPABLEOFDETECTING(DETECT)ORNOTCAPABLEOF
DETECTING(NO)MARINEANIMALS.SOMEOFTHESENSORSDELIVERDATAWITHWHICHMARINEANIMALSCANBECLASSIFIED
(TOSOMEEXTENT)TOSPECIESORSPECIESGROUPS(CLASSIFY)...................................................................................68
TABLE3.PROPERTIESGENERALLYAPPLYINGFORAUTONOMOUSVEHICLESFORTHEDATARELAYSYSTEMSSATELLITELINKSAND
WIRELESSSYSTEMS............................................................................................................................................69
TABLE4.EVALUATIONMATRIXOFMONITORINGCAPABILITIESOFSENSOR/PLATFORMTYPES.GIVENTHEYMEETTHECRITERIAAS
OUTLINEDINSECTION8,THESPECIFICSENSOR/PLATFORMTYPESLISTEDAREINGENERALWELLSUITED(�),SUITED(X)ORNOT
APPLICABLE(-)FORTHECERTAINMONITORINGTYPES(EXCEPTIONSMAYBEPOSSIBLE).THEMONITORINGTYPESARE
FURTHERMOREDIVIDEDINTOMITIGATIONMONITORINGINAREASEITHERCLEARFROMORBUSYWITHOTHEROPERATIONALGEAR
ORTRAFFIC,SHORT-TERM(HOURS,DAYS)ORLONG-TERM(WEEKS,MONTHS)POPULATIONMONITORINGANDFOCAL-FOLLOWS
CONDUCTEDWITHSTATICORMOBILESYSTEMS.L-T-A=LIGHTER-THAN-AIRAIRCRAFT....................................................70
TABLE5.RECOMMENDEDTECHNOLOGYTYPESTHATMIGHTBEUSEDINFUTUREFIELDTRIALSFORTHEDIFFERENTMONITORINGTYPES.
THEMONITORINGTYPESAREFURTHERMOREDIVIDEDINTOMITIGATIONMONITORINGINAREASEITHERCLEARFROMORBUSY
WITHOTHEROPERATIONALGEARORTRAFFIC,SHORT-TERM(HOURS,DAYS)ORLONG-TERM(WEEKS,MONTHS)MONITORING
ANDFOCAL-FOLLOWSCONDUCTEDWITHSTATICORMOBILESYSTEMS.L-T-A=LIGHTER-THAN-AIRAIRCRAFT.......................79
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TABLE6.SHORTLISTOFRECOMMENDATIONSWITHMEDIUM(M)ORHIGH(H)PRIORITYANDWITHMEDIUM(M)ORHIGH(H)
URGENCY,ALONGWITHTHERELATEDTOPICANDDATAGAP.......................................................................................84
TABLE7:LISTOFPUBLISHEDSTUDIESINVOLVINGUASWITHAPPLICATIONINTHEMARINEENVIRONMENT................................104
TABLE8.LISTOFSTUDIESINVOLVINGAUVANDASVWITHPOSSIBLEAPPLICATIONINTHEMARINEENVIRONMENT.....................107
TABLE9.DATAVOLUME(GIGABYTES)FORDIFFERENTRECORDINGBANDWIDTHSANDRECORDINGDURATIONSFORASINGLE
HYDROPHONERECORDEDAT16BITACCURACY......................................................................................................128
TABLE10TECHNOLOGYREADINESSLEVELSUSEDINTHEEVALUATIONMATRICES*...............................................................134
TABLE11.HAZARDCLASSLEVELSRELATEDTOBATTERYORFUELTYPEOFANEVALUATEDSYSTEM.OILSOROTHERMATERIALUSEDIN
MANUFACTURINGOFSYSTEMSTHATMAYHAVEHAZARDSARENOTCONSIDERED..........................................................135
TABLE12.AUTONOMOUSPLATFORMSINCLUDEDINTHISREVIEW,THEIRSYSTEMCLASS(POWEREDANDUNPOWEREDASV,
PROPELLERANDGLIDERAUV,LIGHTER-THAN-AIRAIRCRAFTS(L-T-A)UASS,KITESANDPOWEREDFIXEDWINGUASS),SYSTEM
NAME,MANUFACTURERAND/ORDESIGNER/ORIGINATINGUNIVERSITYASWELLASTHEIRTECHNOLOGYREADINESSLEVELAS
DEFINEDINTABLE10.NOTETHATCELLSWITHTHESAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURER
MAYBEMERGED.............................................................................................................................................151
TABLE13.TECHNICALDIMENSIONSOFTHEAUTONOMOUSPLATFORMSLISTEDINTABLE12.GIVENARETHEIRMISSIONDURATION
ANDFACTORSLIMITINGTHEMISSIONDURATION,THEIRMINIMUM,MAXIMUMANDTYPICALSPEED,THEIRVERTICAL
OPERATIONALRANGE,THEIRENVIRONMENTALPERFORMANCELIMITS.THEIROPERATIONALSIZEANDWEIGHTASWELLAS
PACKING/FREIGHTWEIGHTANDSIZEAREGIVENANDTHERANGETHEYCANBECONTROLLEDWITHIN.NOTETHATCELLSWITHTHE
SAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:L-T-A:LIGHTER-
THAN-AIRAIRCRAFTS,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED,LWH:LENGTH,WIDTH,HEIGHT.*UNLESSOTHERWISE
SPECIFIED.......................................................................................................................................................155
TABLE14.OTHERTECHNICALDETAILSOFTHEAUTONOMOUSPLATFORMSLISTEDINTABLE12.NOTETHATCELLSWITHTHESAMEKIND
OFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:L-T-A:LIGHTER-THAN-AIR
AIRCRAFTS,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED,SSS:SIDESCANSONAR,CTD:CONDUCTIVITY,TEMPERATURE,DEPTH,
IR:INFRARED,SVTP:SOUND,VELOCITY,TEMPERATUREANDTEMPERATURE,SUNA:SUBMERSIBLEULTRAVIOLETNITRATE
ANALYZER,LND,COTS:COMMERCIALOFF-THE-SHELF,PAR:PHOTOSYNTHETICALLYACTIVERADIATION,RINKO:
PHOSPHORESCENTDOSENSOR..........................................................................................................................163
TABLE15.COSTDETAILSOFTHEAUTONOMOUSPLATFORMSLISTEDINTABLE12.GIVENISTHEPLATFORMPRICEFORPURCHASEOR
RENTAL,THECOSTSFOROPERATION,MAINTENANCE,BATTERY/FUELCOSTSANDCOSTSFORPILOTINGTELEMETRY.NOTETHAT
CELLSWITHTHESAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.PRICESAREFOR
VEHICLESONLYWITHOUTSENSORS.ADDITIONOFSENSORPACKAGESCANSIGNIFICANTLYINCREASETHEPRICE.ABBREVIATIONS:
L-T-A:LIGHTER-THAN-AIRAIRCRAFTS,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED.......................................................167
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TABLE16.SURVEYCAPABILITIESOFTHEAUTONOMOUSPLATFORMSLISTEDINTABLE12.GIVENISTHECAPABILITYOFTHEPLATFORMS
FORTRACKSETTING,INCLUDINGITSIMPLEMENTATIONDETAILSANDLIMITATIONS,THEIRABILITYFORSTATIONKEEPING,
AUTONOMOUSDECISIONMAKINGANDCLOCKSYNCHRONISATIONINCL.THEIRLIMITATIONS.NOTETHATCELLSWITHTHESAME
KINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:L-T-A:LIGHTER-THAN-
AIRAIRCRAFTS,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED.....................................................................................171
TABLE17.OPERATIONALINFORMATIONONTHEAUTONOMOUSPLATFORMSLISTEDINTABLE12.GIVENISTHEFUELTYPE,FAILSAFE
MODETYPE,TRANSPONDER,DETECTANDAVOIDCAPACITY,GROUNDSTATIONINTERFACE,ITSLEVELOFAUTONOMY,THE
PAYLOADCAPACITYINTERMSOFPOWER,SPACEANDWEIGHTASWELLASTHEDEPLOYMENTANDRECOVERYPROCEDURE.NOTE
THATCELLSWITHTHESAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.
ABBREVIATIONS:L-T-A:LIGHTER-THAN-AIRAIRCRAFTS,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED................................176
TABLE18.MANNINGREQUIREMENTSFORTHEAUTONOMOUSPLATFORMSLISTEDINTABLE12.GIVENISXX.NOTETHATCELLSWITH
THESAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:L-T-A:
LIGHTER-THAN-AIRAIRCRAFTS,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED................................................................184
TABLE19.SENSORSTHATMAYBEINTEGRATEDINTOAUTONOMOUSVEHICLESANDINCLUDEDINTHISREVIEW,THEIRSYSTEMCLASS
(AAM,PAM,VIDEO),SYSTEMNAME,MANUFACTURERASWELLASTHEIRTECHNOLOGYREADINESSLEVELASDEFINEDINTABLE
10).NOTETHATCELLSWITHTHESAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.
ABBREVIATIONS:AAM:ACTIVEACOUSTICMONITORING,PAM:PASSIVEACOUSTICMONITORING...................................188
TABLE20.TECHNICALDETAILSOFTHESENSORSLISTEDINTABLE19.GIVENARETHEENVIRONMENTALPERFORMANCELIMITSOFTHE
SENSORS,THEIROPERATIONALASWELLASPACKING/FREIGHTSIZEANDWEIGHT,HAZARDCLASSASDEFINEDINTABLE11,ANY
FACTORSINTERFERINGWITHTHEOPERATIONOFTHESENSOR,ANDTHESENSOR’STYPEOFINTERFACE.NOTETHATCELLSWITH
THESAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:AAM:
ACTIVEACOUSTICMONITORING,PAM:PASSIVEACOUSTICMONITORING,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED........190
TABLE21.COSTSSENSORSLISTEDINTABLE19.GIVENARETHEPURCHASEANDRENTALPRICE,ASWELLASOPERATIONAL,
MAINTENANCE,BATTERY/FUELASWELLASINTEGRATIONCOSTS.NOTETHATCELLSWITHTHESAMEKINDOFINFORMATIONFOR
SYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:AAM:ACTIVEACOUSTICMONITORING,PAM:
PASSIVEACOUSTICMONITORING,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED.............................................................193
TABLE22.FIRSTSETOFSURVEYCAPABILITYCRITERIAFORSENSORSLISTEDINTABLE19.GIVENISTHEDATATYPECOLLECTED,DATA
PROCESSINGDETAILS,IFSPECIESIDENTIFICATIONISPOSSIBLEANDIFSO,WHICHTYPE,ANDIFTHEBEARING,DIRECTAND/OR
HORIZONTALRANGETOTHEANIMALISESTIMABLEWITHTHESENSORDATA.NOTETHATCELLSWITHTHESAMEKINDOF
INFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:AAM:ACTIVEACOUSTIC
MONITORING,PAM:PASSIVEACOUSTICMONITORING,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED................................195
TABLE23.SECONDSETOFSURVEYCAPABILITYCRITERIAFORSENSORSLISTEDINTABLE19.GIVENISTHEREAL-TIMEDATA
TRANSMISSIONCAPABILITIESINCL.DETAILSWATISTRANSMITTED,THEABILITYFORCLOCKSYNCHRONISATIONANDITS
LIMITATIONSANDFORACOUSTICSENSORS,IFRECEIVINGLEVELSOFRECEIVEDSIGNALSISESTIMABLEASWELLASAMBIENTNOISE
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LEVELS,ANDIFCTDDATAAREOBTAINEDSIMULTANEOUSLY.NOTETHATCELLSWITHTHESAMEKINDOFINFORMATIONFOR
SYSTEMSOFTHESAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:AAM:ACTIVEACOUSTICMONITORING,PAM:
PASSIVEACOUSTICMONITORING,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED.............................................................198
TABLE24.OPERATIONALREQUIREMENTSOFTHESENSORSLISTEDINTABLE19.GIVENISTHEIRLEVELOFAUTONOMY,DEPLOYMENT
ANDRECOVERYPROCEDURE,THEIRPAYLOADPOWER,ANDINTERNALASWELLASEXTERNALPAYLOADCAPACITYINTERMSOF
SPACEANDWEIGHT.NOTETHATCELLSWITHTHESAMEKINDOFINFORMATIONFORSYSTEMSOFTHESAMEMANUFACTURER
MAYBEMERGED.ABBREVIATIONS:AAM:ACTIVEACOUSTICMONITORING,PAM:PASSIVEACOUSTICMONITORING,NA:NOT
APPLICABLE,N.P.:NOTPROVIDED......................................................................................................................200
TABLE25.MANNINGREQUIREMENTSFORTHESENSORSLISTEDINTABLE19.GIVENISTHEMINIMUMNUMBEROFPEOPLEFOR
OPERATION,THEVESSELREQUIREMENTS,THEMANNINGREQUIREMENTSFORDEPLOYMENTANDRECOVERYASWELLASTHE
TRAININGNEEDSFOROPERATINGTHESENSOR.NOTETHATCELLSWITHTHESAMEKINDOFINFORMATIONFORSYSTEMSOFTHE
SAMEMANUFACTURERMAYBEMERGED.ABBREVIATIONS:AAM:ACTIVEACOUSTICMONITORING,PAM:PASSIVEACOUSTIC
MONITORING,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED......................................................................................203
TABLE26.FURTHERINFORMATIONONTHESENSORSLISTEDINTABLE19.GIVENISTHERAWDATAVOLUME,THECAPABILITIESTO
REDUCEREAL-TIMEDATA,LINKSANDCOMMENTS.ABBREVIATIONS:AAM:ACTIVEACOUSTICMONITORING,PAM:PASSIVE
ACOUSTICMONITORING,NA:NOTAPPLICABLE,N.P.:NOTPROVIDED........................................................................204
TABLE27.DATARELAYSYSTEMSTHATCANBEUSEDFORAUTONOMOUSVEHICLES..............................................................207
TABLE28.TECHNICALDETAILSOFTHEDATARELAYSYSTEMS:THEIROPERATIONALSIZEANDWEIGHT,POWER,BANDWIDTH,RANGE,
PURCHASEANDOPERATIONALCOSTSASWELLASUSAGERESTRICTIONS,DATADELAYANDFURTHERCOMMENTS.................209
TABLE29.COMPILATIONOFWHICHVIDEOSENSORCOULDPOTENTIALLY(O)ORDEFINITIVELYBECOMBINED(X),WHICHCANNOTBE
COMBINED(-),ANDWHICHCOMBINATIONWILLWORKBUTWITHLIMITEDMISSIONDURATION(L).GREENMARKEDCELLS
INDICATESENSOR/PLATFORMCOMBINATIONSTHATAREALREADYINTEGRATED.NA:NOTAPPLICABLE..............................211
TABLE30.COMPILATIONOFWHICHPAMORAAMSENSORCOULDPOTENTIALLYORDEFINITIVELYBECOMBINED,SENSOR/PLATFORM
COMBINATIONSTHATAREALREADYINTEGRATED,WHICHCOMBINATIONSCANNOTBECOMBINEDANDWHICHCOMBINATION
WILLWORKBUTWITHLIMITEDMISSIONDURATION................................................................................................212
TABLE31.LISTOFSUPPLIERCONTACTSOFTHEPLATFORMSANDSENSORSINCLUDEDINTHISREVIEW.......................................216
4 Executive Summary 4.1 Objectives
Thisreporthasbeenconductedtoenhancetheunderstandingofhowautonomousvehiclesystemscanbeused
tomonitormarinemammalsandothermarineanimals intheframeof industrialactivitiesoftheoilandgas
(O&G) industry. The purpose of suchmonitoringwould be formitigationmeasures, to estimate population
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statusandtrendsinareasofinterest,ortoconductfine-scalebehavoiuralstudiestoevaluatepotentialimpacts
ofanthropogenicinducednoiseontheseanimals.Keyobjectivesofthereportinclude:
• An exhaustive review and evaluation of available platform and sensor technologies that are most
pertinenttooilandgasoperationsformarineanimalmonitoring;
• Information on the key data types that can be obtained, addressing the technical challenges of
managing,storingandanalysingthelargeamountofdatagatheredbytheautonomoussystems;and
• Recommendations of platform technologies, sensors and data characteristics that would be most
relevantfortheO&Gindustry.
4.2 Approach
Inordertoaddresstheobjectives,thereportprovidesacomprehensiveevaluationofthestatusandpotential
ofautonomousaerialandmarineplatformsandsensortechnologiesthataremostpertinenttoO&Goperations
for conductingmitigationmonitoring, population surveys and/or trackingofmarine animals such asmarine
mammals,seaturtlesandfish.Themainplatformsandsensorsconsideredinthisrevieware:
• UnmannedAerialSystems(UAS)includingpoweredaircraft,gliders,kitesandlighter-than-airaircraft;
• UASsensorsincludingthermalIR,Non-thermalIR,RGBandvideocameras;
• Autonomous Underwater Vehicles (AUV) and Autonomous Surface Vehicles (ASV) including
autonomouspropellerdrivencraft,autonomousunderwaterbuoyancygliders,autonomouspowered
surfacecraft,selfpoweredsurfacevehiclesanddriftingsensorpackages;
• AUV/ASVsensorsincludingPassiveAcousticMonitoring(PAM)andActiveAcousticMonitoring(AAM)
sensorsaswellasanimal-bornetranspondertags.
Evaluation criteria and metrics were defined, which were used to evaluate the autonomous systems’
applicabilityandsuitabilityforvariousmonitoringactivities.Furthercriteriaallowedtheevaluationofpotential
platformandsensorcombinationsandconsiderations tobe taken intoaccountduringprojectplanning.The
informationgatheredwascompiledintomatricestoallowacomparisonofthedifferentsystems’operational
capabilities,dataprocessingandtransferaspectsandtheirpotentialformitigationmonitoringandsurveydata
collection. Further informationwas compiled and summarised toprovide further insights into the following
topics:
• Requirementsofautonomousvehiclesformarineanimalmonitoring;
• Currentknowledgeaboutautonomousvehicles;
• Operationalaspectstoconsider;
• Regulatory/politicalbarriers;
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• Technicalchallengesofmanaging,storingandanalysinglargeamountofdata.
4.3 Outcome
The report illustrates that it is not possible to give simple ‘one-size-fits-all’ suggestions for choosing the
appropriateautonomousvehicletoconductdifferentkindsofmonitoringapplications,astherearemanyproject
dependentfactorsthatneedtobeconsidered.Thelargevarietyoftargetspeciesrequirestheselectionofthe
appropriatesensortype/platformtypecombination(ratherthanaspecificsystem).Thesame is truefor the
differentmonitoringtypes(mitigationmonitoring,densitymonitoring,focal-follow),combinedwiththechoice
ofdatarelaysystem.Largerandmorecomplexsystemsaregenerallymorecapable,butarelikelytobemore
expensiveandrequiregreatersupportinginfrastructure.Thetechnicalandoperationaldetailsofanautonomous
platformwillneedtobetailoredtothespecificneedsoftheexplorationandproduction(E&P)project,e.g.the
areaofinterest,thepropertiesoftheplatformthattheautonomousvehiclewilldeployedfrom,therequired
surveylength,projectbudgetandmore.Herewewillgiveanoverviewofwhichplatformtypesarebesttouse
for variousmonitoring typesby comparingeach classofplatformandhighlighting theirparticular strengths
relatingtothespecificmonitoringtypes.
OncethedetailsofanE&Pprojectareknown,thecomparisonandevaluationmatricesinthisreportwillthen
helptoselecttheappropriateplatform/sensorcombination,andthediscussionsectionwillhelptounderstand
howwellthatsystemwilladdressthetaskinhand.Theestablishmentofacomputer-basedexpertsystembased
onthisreviewandtheprecedingreviewonlowvisibilitymonitoringtechniquesaspublishedinVerfussetal.,
(2016)wouldbeahelpfulandappropriatetooltohandlethevastamountofinformationgathered.Thisexpert
systemshouldbebasedontheevaluationcriteriaestablishedinbothreviews,andshouldprovidealistofthe
mostappropriatesystemsandmethodsasanoutputbasedonthemonitoringrequirementsfedintotheexpert
system.Theestablishmentofsuchatoolwas,however,outsidethescopeofthisreview.
4.3.1 UAS
UAShaverecentlybeenintroducedasalternativeplatformstoovercomesomeofthelimitationsofmanned-
aerialsurveys,mainlyfortheirabilitytoperformthesametasksmoreefficiently,safely,andatafractionofthe
cost(NeiningerandHacker,2011).Recentdevelopmentshaveturnedpresent-dayUASintorealisticalternatives
tomannedsystems,suchaslongerflightdurations,improvedmissionsafety,flightrepeatabilityduetoimproved
autopilotsystems,andreducedoperationalcostswhencomparedtomannedaircraft.Thepotentialadvantages
ofanunmannedplatform,however,dependonfactorssuchasaircraftflightcapability,sensortype,mission
objective,andcurrentregulatoryrequirementsforoperationsoftheparticularplatform(Wattsetal.,2010a).In
recent years, UAS have been integrated into a variety of field studies involving a range of species and
applications.However,therehasbeenlittlefocusontheapplicationofthesesystemswithamoreO&Gindustrial
approach,concentratingontheirbenefitsfore.g.monitoringformitigationpurposesandtheirintegrationwith
other autonomous technologies (e.g. coordinationof unmannedaerial, surface, andunderwater vehicles to
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createacommunicationnetworkthatiscapableofsimultaneouslysurveyingthesameareausingavarietyof
sensorsinordertomaximisethechancesofdetectionandfordatacross-validationbetweenplatforms).
The variety of UAS configurations currently available is a great advantage for offshoremonitoring. Smaller
platformsallowcoverageofsmallerareasata lowcost(e.g.exclusionzonesduringseismicoperations),and
largerplatformsallowcoverageoflargerareas,providingvaluableinformationbothbefore,during,andafter
offshore operations. Hence, it is possible to cover a wide range of projects using different UAS models.
Unfortunately,thecomplexityofthelargersystemsrequiresmultipleexperiencedpersonneltooperatethem
andmanyofthesystemsarecurrentlyonlyavailableformilitaryuse.Largerplatformsmayalsorequirerunways
similartothoserequiredbymanned-airplanes,whichlimitsthepossibilityfordeploymentsatsea.Conversely,
theperformanceofsmallerplatformsmaybe limitedbyenvironmentalconditionsandpayloadsare limited,
meaningthattheymayonlybeabletocarrylesssophisticatedsensorequipmentand/oroperateforshorter
periodsoftime.
OfthethreeclassesofUASplatformsconsideredinthisreview,poweredaircrafthavemanyofthecapabilities
requiredforaerialsurveysofmarineanimals.Kitesandlighter-than-airaircraft,suchasballoons,arecost-saving
alternatives tosomefixedandrotarywingsystems, thoughtheyrelyheavilyonweatherconditionsandare
difficulttocontrolwhenfollowingapredefinedtrack.Intermsofindividualfocalfollows,however,lighter-than-
airaircraftshouldbeconsideredasapotentialcandidateplatform,thoughtheirabilitytofollowanimalswillbe
restrictedtothemanoeuvrabilityofthesupportvesseltowhichtheyaretethered.Poweredaircraftaregood
candidateplatformsforindividual-basedmonitoringastheycanbepilotedtofollowanimals(thoughfixed-wing
systemsmaystruggletohoverabovestationary,orslowmoving,focalanimals),whilekiteshavelimitedcontrol.
UAS are also suitable formitigationmonitoring provided they have real-time detection options.With long
operationalranges,theyareespeciallysuitedformonitoringtheareaaheadofaseismicvesselinordertodetect
marineanimalswithinanexclusionzonearoundtheanticipatedstartlocationofthesoundsource,assuggested
byVerfussetal.(2016).
ThisstudymakesthefollowingshortlistofrequirementsforaUASmonitoringsystem:stabilisedhigh-resolution
videofordetection,highresolutionimagesforidentification,real-timegeocodingofimagedataandacomputer
systemforefficientanalysisofrecordeddata.Ifthedataaretobetransmittedtotheground,asneededfor
mitigationmonitoring,ahighbandwidthdatalinkisalsorequired.
4.3.2 AUVandASV
AutonomousUnderwaterandSurfaceVehicles(AUVandASV)aredividedinthisreportintofivemaincategories:
propeller driven underwater craft, underwater buoyancy gliders, powered surface vehicles, self-powered
surfacevehiclesanddrifters.Thevehiclesrangefromrelativelysmallsize,whichcanbeliftedbyoneortwo
personsanddeployedfromshoreorasmall inflatable,tolargediesel-poweredsurfacevesselswithbespoke
launchandrecoverysystems(Griffiths,2002).Manysurfaceandunderwaterautonomousvehiclesarenowused
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on a regular basis by research institutions world-wide. Primarily, they are used for the collection of
oceanographicdata(e.g.seasurfaceorwatercolumntemperatureandsalinityforinstance)whilethedetection
of largerorganisms,suchasmarinemammalsandfish,generallyremainsanicheareaofresearch.Powered
underwaterandsurfacevehicleshavetheadvantageofgreatercontrol,butgenerallyhavelimiteddeployment
times(oftenhourstodays).Buoyancydrivensystemsaregenerallysmallenoughtobedeployedmanuallyfrom
asmallvesselandarecapableofmulti-monthmissions,givingthemgreatpotentialforlongtermpopulation
monitoring.Wavedrivensystemstendtobeslightlylargerbutcanstilloftenbedeployedandrecoveredusing
modestfacilities.Thelowestcostsystemsaredrifters,whichhavenocontroloverwheretheygo,butcouldbe
acost-effectivewayofcollectinglargedatasamples.
Avarietyofactiveandpassiveacousticsensorsareavailable,whicheitherhavebeen,orhavethepotentialto
be, integratedintotheseplatforms.Whilenoteverysensor issuitableforeveryplatform,foreachplatform,
thereisatleastonesensorwhichmightbeincorporatedintoitandvice-versa.AnumberofPassiveAcoustic
Monitoring (PAM) sensors are able to run automatic detection algorithms to automatically pick outmarine
mammal calls. However, these generally need checking by a human operator before they can be used in
managementdecisionmaking,soconsiderablequantitiesofPAMdatamusteitherbestoredonthedevicefor
futureanalysisortransmittedtoanoperatoriftheyarerequiredinreal-time.Solutionsexistwhichcantransport
sufficientdataovershortrangesforreal-timeoperationevenforhighfrequencydata.Mostsystemsreviewed
hereonlydeployasinglehydrophoneandareincapableofdirectlymeasuringbearingsorrangestodetected
sounds.
AutomaticprocessingforActiveAcousticMonitoring(AAM)islessadvancedthanforPAM,withdatagenerally
being stored for future analysis. As with PAM, it should be possible to transmit sufficient data over short
distancesforreal-timedecisionmaking.Itisalsopossibletoattachacoustictagstotargetanimalstoidentify
andtrackindividualsinrealtimewithareceivingPAMsystemonanASVorAUV.Obviously,akeyrequirement
isthattheanimalistaggedinthefirstplace.
ThemainrestrictionsgoverningtheuseofAUVandASVformitigationmonitoringinthevicinityofseismicsurvey
vesselsareoperational.Thedeploymentofanautonomousvehicleclosetoanoperationalseismicsurveyvessel
carriestheriskthatthevehiclemayinterferewithseismicoperationsandtheindustrywouldneedtobewilling
toacceptthisriskwhendeployinganautonomoussurfaceorunderwatervehicleaheadofthesurveyvessel.It
isthereforeunlikelythatAUVandASVwillhavearoleinmonitoringaroundseismicvesselsinthenearfuture.
Ontheplusside,seismicvesselshavemanycompetenttechnicalpersonnelonboardandthepotentialtodeploy,
serviceandoperateautonomousplatforms;however,dependingonthecircumstancestheremaynotberoom
onboardeitherforadditionalequipmentorpersonnelrequiredtooperateautonomousplatforms.AUVand
ASVmayhavearoletoplaymonitoringfurtheraheadofseismicvesselsasanaidtoforwardplanning.Thismay
be important in sensitiveareaswherePAMon theASV /AUVcouldenhancedetectioncapabilityofbaleen
whalesandpromptmitigationmonitoringby,forinstance,thermalimaging.Additionally,ASVandAUVmaybe
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important when mitigating around static sources, such as rig operations where space for equipment and
personnelmaybelimited.Thismightbeofparticularinterestwithregardtotheuseofexplosives(suchasfor
well-headdecommissioningorremovalofunexplodedordnanceUXO)duetotheclearrequirementtoremove
personnelfromthevicinity.
Thelongmissiondurationsofferedbyself-poweredsurfacevehiclesandbuoyancyglidersmakesthesesystems
particularly appealing for long term population monitoring. The choice of vehicle would depend on the
characteristics of the area to be surveyed (e.g. size, water depth and current strength). For population
monitoring, data need not be transmitted in real time, but can be archived to storagemedia. A particular
advantageoflowpowervehiclesforPAMisthelownoiseproducedbythesystemitself,andmoreimportantly,
theconsistencyofnoisebetweenplatforms.Themainlimitationstotheuseofthistechnologyforpopulation
monitoring are the same as for vessel based acoustic surveys, namely the need to understand detection
probabilityfordifferentspeciesasafunctionofrangefromthedetectorandtheneedforrequiredbehavioural
data(e.g.groupsizesorcueproductionrates)sothatcountsofanimaldetectionscanbetranslatedintoanimal
densityestimates.Thelowernoiseandmissiondurationcapabilitiesoflowpowersurfacevesselsalsosuggest
anadditional future role,not inmitigation,but inunderwatersoundmeasurement forverificationof sound
levelsemittedfromsoundsourcesintheframeofE&Pactivities.
4.3.3 Futurework
Autonomoussystemshavethepotentialtodetectawidevarietyofspeciesindifferentoperationalscenarios.
Theprimarylimitationsontheuseofautonomoustechnologiesduringmitigationinthevicinityofseismicvessels
are operational. If there is a desire on the part of industry to overcome these problems, then suitable
engineeringandenvironmentalscienceteamsboth,insideandoutsideofindustry,willneedtofurtherassess
andfindsolutionstothesequitesignificantproblems.Forpopulationmonitoring,technologiesexistwhichcan
collect data for awide rangeof species anddevelopment shouldbe tailored to specific species groups and
operationalareassincethiswillguidethechoiceoftechnology.Forspecificscenarios,thecostandbenefitof
using autonomous vehicles should also be comparedwithmore traditionalmethodsof data collection (e.g.
vesseloraerialvisualsurvey).
Wheremultipletechnologieshavethepotentialtodetectaparticularspecies,werecommendthatside-by-side
comparisons should be conducted between current monitoring methods (e.g. visual vessel based or aerial
surveys) and different autonomous solutions. This is valuable for evaluating the degree to which the data
acquired by these technologies can be complementary. Particularly in the case of PAM, simultaneous
deploymentsofvarioussystemscanhelptoestimatethedetectionprobabilitiesofagivensystembyusingthe
otherplatformstoconduct“trial-based”detectionprobabilityestimation.Thiswould,however,demandahigh
degreeofcontrolovertheconditionsinwhichtheequipmentisdeployedandastudymayneedtorunforsome
timetoobtainstatisticallyusefulsamplesizes;nevertheless,itwouldberequiredtofullyunderstandtherelative
capabilitiesofallthesesystems.
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Real-time decisionmaking and analysis of large data sets inevitably require the use of automatic detection
algorithmsforallsensortypes.Automaticdetectionbothreducestheamountofdatathatneedtobechecked
byahumanoperator,andmakesitmorepracticaltosendthedatathroughlowerbandwidthdatarelaysystems.
The development of detection algorithms is further advanced for PAM than for video and AAM, in most
situations.Therefore,AAMandvideofutureresearchshouldfocusondevelopingdetectionalgorithmsanddata
compression techniques for returning summarised and/or raw data over low bandwidth communications.
However,whiledetectionandclassificationalgorithmswillalwayscontinuetoimprove,itmustbeunderstood
thattheywillneverbeperfectandwillcontinuetorequireahumanoperatoratsomepointintheprocessing
chain.This fundamental limitationshouldbe taken intoaccountaswemove forwardwithdeveloping these
systems.While we would not suggest that further research into algorithms for automatic detection is not
worthwhile, research into both the magnitude and the effects of mis-detection andmis-classification, and
investmentintosystemsthataidhumanobserversinthedecisionprocessesisalsoofhighimportance.
Finally,behaviouralinformationonmanymarineanimalspeciesformsahugedatagap.Auxiliarybehavioural
data relating to the study species (e.g. group size, dive duration or call production rate) are required for
abundanceestimationand/ormitigation(inbothcaseswhereunderstandingprobabilityofdetectioniscrucial).
Therefore,continuedbehaviouralstudiesofallspeciesofinterestwillbeimportantforsuccessfulsurveyingand
monitoring.
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5 Quick guide for prospective users Anyone wishing to use the information provided in this review to find an appropriate platform/sensor
combination should start by answering the two questions below to narrow down the list of candidate
technologies(seealsoFigure1).Beneatheachquestion,wepointtothesections,figuresandtablesthatcontain
backgroundinformation,theimportantcriteriaandmetricsaswellaswheretofindinformationtoaddressthe
most importantpointsthatneedtobeconsideredwhendecidingontheappropriateplatformorsensor.An
overviewofthestrengthsandweaknessesofthedifferentplatformsisgiveninsection8.3.2.
When themost suitableplatformand sensor typehasbeen identified, anda short-listof the systemsmost
suitable for the specific monitoring task is specified, the possible platform / sensor combination is mainly
determinedbythepayloadcapacityoftheplatform(Table17)andtheoperationalsizeandweightofthesensor
(Table20).Anoverviewofpotentialplatform/sensorcombinationsisgiveninTable29andTable30.Thedata
relayclassandsystemthatshouldbeusedcanbedeterminedbyreadingsection8.1.4andconsultingTable27
andTable28,bearinginmindthatoperationalsizeandweightneedtofitthepayloadoftheplatform.
Fromthisfinalshort listofplatforms,sensorsanddatarelaysystems,thefinaldecisionisdependentonthe
users demand and limitations, e.g. limited manning requirements (manning requirements information for
platforms=>Table18, forsensors=>Table25)or limitedbudget(costsofplatforms=>Table15,sensors=>
Table21,relaysystems=>Table28).Oncetheplatform,sensoranddatarelaysystemshavebeenselected,itis
advisabletocontactthecorrespondingsuppliers(=>Table31)toconfirmthesuitabilityofthechosensystem
forthedemandedtasksandtoretrievepotentialupdatesontheinformationgatheredinthisreview,asthisis
arapidlydevelopingfield.
Q1:Whatkindofmonitoringshouldbeconducted?
a) Mitigationmonitoring
• Mostimportantpointstoconsider:
o Real-timedetectionof theanimalof interest isobligatory, thereforeonlysensorswith
real-timeoptionsareappropriate(=>Table23)
o Ahighdetectionlikelihoodoftheanimalisrequired,whichinfluencesthechoiceofthe
sensor(=>Q2).Concurrentuseofdifferentmethodsincreasesthedetectionprobability
o Operational period and range need to be sufficient tomeet the temporal and spatial
requirementsofthemonitoring(=>Table13)
o Manoeuvrability is important to keepwithin themonitoring area (=> Table16),which
excludesdriftersfortheuseofmitigationmonitoring
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o Inoperationallybusyareas,AUVandASVarenotrecommendedduetotheriskofcollision
andentanglementwithoperationalgear
o Synchronisedoperationofa fleetwidensthefieldofviewandallowstocovera larger
area(section9.2.1)
• Requirements:section9.1.1
• Whichplatformtypetouse:section8.3.3,Figure1,Table4
• Criteriaandmetricstoconsider:section8.1.2
• Backgroundinformation:section7.1.1
b) Populationsurvey
• Mostimportantpointstoconsider:
o Operational period, speed and range need to be sufficient tomeet the temporal and
spatialrequirementsofthesurvey(=>Table13),whichexcludesmostpoweredsystems
forlong-termmonitoring
o Trackkeepingisimportant,atleastforshort-termsurveys(=>Table16)
• Requirements:section9.1.2
• Whichplatformtypetouse:section8.3.4,Figure1,Table4
• Criteriaandmetricstoconsider:section8.1.1
• Backgroundinformation:section7.1.2
c) Focal-animalstudy
• Mostimportantpointstoconsider:
o Trackingofthefocalanimalisrequired.Systemchoicedependsonthesensorcapabilities
toestimatebearingandrangetotheanimalduringstationaryfocalfollows(i.e.animalis
trackedwithinsensor’sview)(=>Table22)andthetrackingabilityoftheplatformduring
mobilefocalfollows(i.e.platformfollowsanimal)(=>Table16)
o Manoeuvrabilityisimportantduringmobilefocalfollows(=>Table16)
o Operational period, speed and range need to be sufficient tomeet the temporal and
spatialrequirements(=>Table13)
• Requirements:section9.1.3
• Whichplatformtypetouse:section8.3.4,Figure1,Table4
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• Criteriaandmetricstoconsider:section8.1.1
• Backgroundinformation:section7.1.3
Q2:Whatkindofanimalneedstobemonitored?
a) Speciesregularlyseenatorneartheseasurface
• Sensor:Electro-opticalimagingsensors(section7.3.1)
• Animal types: Whales, dolphins/porpoises, seals, turtles, large solitary fish (e.g. basking
sharks),swarmfish(specifiedforsensorsystemsinTable22)
• Detectionprobability:increaseswithtimespentatornearseasurface
• Mostsuitableplatform(s)=UAS
b) Speciesinthewatercolumn
• Sensor:AAMsensors(section7.3.3)
• Animal types: Whales, dolphins/porpoises, seals, turtles, large solitary fish (e.g. basking
sharks),swarmfish(specifiedforsensorsystemsinTable22)
• Detectionprobability:increaseswithsizeofanimaloranimalschool
• Mostsuitableplatform(s)=AUV/ASV
c) Specieswithfrequentanddistinctivevocalisations
• Sensor:PAMsensors(section7.3.2)
• Animaltypes:Whales,dolphins/porpoises(specifiedforsensorsystemsinTable22)
• Detectionprobability:increaseswithincreasedvocalisationrateand/orvocalisationduration
• Mostsuitableplatform(s)=self-poweredAUV/ASV,poweredAUV/ASVwithlowself-noise
d) Animalssuitablefortagging
• Sensor:Animal-borntranspondingacoustictags incombinationwithahydrophone(section
7.3.4)
• Animal types: Whales, dolphins/porpoises, seals, turtles, large solitary fish (e.g. basking
sharks),swarmfish(specifiedforsensorsystemsinTable22)
• Note:animalneedstobeactivelytagged;onlysuitableforfocal-animalstudies
• Mostsuitableplatform(s)=self-poweredAUV/ASV,poweredAUV/ASVwithlowself-noise
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Figure1.Decisiontoolforfindingtheappropriateplatform/sensorcombinationforaspecificmonitoringtaskandanimaltype.ThemonitoringtypesaredividedintoMitigationmonitoringinareaseitherclearfromorbusywithotheroperationalgearor traffic,Populationmonitoring in the short-term(hours,days)or long-term(weeks,months)andFocal-followsconductedwithstaticormobile systems.AnsweringquestionsQ1 (whichmonitoring typeshouldbeconducted)withdiagramAandQ2(whichtypeofanimalneedstobemonitored)withdiagramBwillhighlightthemostimportantsystemrequirementsandtheanimalbehaviourpotentiallytriggeringdetection, leadingtothesensor/platformcombinationbestsuitedforaspecificmonitoringtask.
Whales,dolphins,porpoises
Frequentdistinct
vocalisation
Frequentlynearoratsea
surface
Passiveacousticmonitoringsensors
Animaltype
Animalstatus/behaviour
Suitablesensortype
Mostsuitableplatformtype
Seals,turtles,fish
Yes
Yes
Yes
No
Yes
Belowseasurface
Electro-opticalimagingsensors
Activeacousticmonitoringsensors
No
Self-poweredAUV/ASVPoweredlowself-noise
AUV/ASV
PoweredUAS,Kites,Lighter-than-airaircrafts
Self-poweredAUV/ASVPoweredAUV/ASV
Suitablefortagging
Yes
No
Animal-borntransponding tags
Self-poweredAUV/ASVPoweredlowself-noise
AUV/ASV
Mitigation Area
Clear
Busy
Length
Short-term
Range Static
Mobile
PoweredUAS,kites,lighter-than-airUAS,poweredandself-
poweredASV/AUV
PoweredUAS,kites,lighter-than-airUAS
PoweredUAS,(kites,lighter-than-airUAS),poweredandself-poweredASV/AUV,Drifters
Self-poweredASV/AUV,Drifters
Poweredandself-poweredASV/AUV,Animalbornetags,(poweredUAS,kites,lighter-
than-airUAS)
PoweredUAS,(kites,lighter-than-airUAS),poweredandself-
poweredASV/AUV
Real-timedetection,
Highdetectionprobability
Reliablevehicle
navigation
Speedampleforsurveycompletion
Longmissionduration
Trackingcapability
Stationkeepingcapability
Long-term
Monitoringtype
Monitoringconditions
Suitableunmannedvehicles
Systemrequirements
Population
Focal-animal
Yes
Yes
Yes
No
No
A
B
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6 Project framework and approach This report was funded by the International Association of Oil & Gas Producers following the Request for
Proposals (RFP)Number JIP III-15-02“AutonomousAerialandMarineTechnologyUnderstanding -Literature
review on understanding the current state of autonomous technologies to improve/expand observation and
detection ofmarine species” from the Joint Industry Programmeon E&P Sound andMarine Life - Phase III,
releasedon24thFebruary2015.
TheobjectivesoutlinedintheRFPweretoenhancetheunderstandingofhowautonomousvehiclesystemscan
beusedformitigationandimpactmonitoringandforestimatingpopulationstatusandtrends.
KeyobjectivesoftheRFPincluded:
• An exhaustive review of available UAS and AUV platforms and sensors technologies that aremost
pertinenttooilandgasoperationsformonitoringofmarinespecies,includingdetailsoncostofsystems
andsensors,currentstateof technologymaturation, sensorperformance,system lifespan, limits to
operating conditions (weather, depth, currents, etc.), alongwith relevant references to field tests,
sourcesofciteddataandkeypointsofcontactforeachsystem.
• Key data types (e.g. digital photographs, digital sound files, oceanographic data, e.g. temperature,
salinity,depth,currents,bathymetry,GPSlocations,etc.)andthedata’spotentialusesforassessingthe
effectsofoilandgasindustryexplorationandproduction(E&P)aswellasgeophysicaloperations(e.g.
seismic surveys) on marine species. Technical challenges of managing, storing and analysing large
amountofdataproducedbytheautonomoussystemsshouldbeaddressed.
• Recommendations of platform technologies, sensors and data characteristics that would be most
relevantfortheE&Pindustrytopursue,eitherviafurtheradvancementoftechnologydevelopmentor
fieldtrialsofexistingpromisingsystems, includingcombinationsofsensors/platformsbestsuitedto
particularspecies,geographic,andoperationalconditions.
SMRUConsulting teamed upwith the SeaMammal ResearchUnit (SMRU), Akvaplan-niva AS, theNorthern
Research Institute (NORUT), theCentre for Research into Ecological andEnvironmentalModelling (CREEM),
SeicheLtdandBritishAntarcticSurvey(BAS)toformateamofhighlyexperiencedexpertsinthefieldofmarine
autonomoustechnologiesandmarineanimalmonitoringtoundertakeacomprehensivereviewonthestatus
and potential of aerial and marine autonomous technologies for conducting marine animal mitigation
monitoringand/orpopulationsurveysaswellasmonitoringanimalmovementandbehaviouralreactions.
Theprojectteamconductedanextensiveliteraturereviewandinformationsearchcombinedwithcontacting
suppliers and developerswhere the relevant informationwas not publically available. The literature search
focusedonspecificinformationasrequestedbytheRFPandoutlinedinthisreport,andcriteriaimportantfor
evaluatingkeyfeaturesofthesystems,suchas
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• platformapplicabilityduringE&Poperations,
• operationalrequirements,
• dataprocessingandtransfercapabilities,
• platformabilityto
o conductmitigationmonitoring,
o collectappropriatesurveydataforanimalpopulationmonitoring,
o monitoranimalmovementandbehaviouralreactions.
Asubsequentevaluationofthegatheredmaterialprovidedareviewofthestateoftheartofautonomousaerial
andmarineplatformsandsensortechnologies.Operationalissuesanddataprocessingandtransfercapabilities
were reviewed and a comprehensive evaluation of the systems for impact monitoring and survey data
acquisitionwas undertaken. This review identified knowledge gaps and areas for further development and
researchleadingtorecommendationsonfuturestudies.
Themainplatformsandsensorsconsideredwere:
• UnmannedAerialSystems(UAS)includingpoweredaircraft,gliders,kitesandlighter-than-airaircraft
(e.g.balloons);
• UASsensorsincludingthermalIR,Non-thermalIR,RGBandvideocameras;
• Autonomous Underwater Vehicles (AUV) and Autonomous Surface Vehicles (ASV) including
autonomouspropellerdrivencraft,autonomousunderwaterbuoyancygliders,autonomouspowered
surfacecraft,selfpoweredsurfacevehiclesanddriftingsensorpackages;
• AUV/ASVsensorsincludingPassiveAcousticMonitoring(PAM)andActiveAcousticMonitoring(AAM)
sensorsaswellasanimal-bornetags.
Thereportprovidesashortintroductionintothedifferentmonitoringtypesconsideredinthisreview(section
7.1)aswellastheconsideredautonomousplatforms(section7.2)andsensortypes(section7.3).Thereport
thendefinesandexplainsthedifferentcriteriaandmetricsfortheevaluationofthesystems(section7.1),gives
theresultsoftheevaluation(section7.2)anddiscussesthese(section7.3).
Inadditiontotheevaluationof thedifferentsystems, further informationwascompiledandsummarisedto
provideinsightsintothefollowingtopics:
• Requirementsofautonomousvehiclesformarineanimalmonitoring(section8.1);
• Currentknowledgeonautonomousvehicles(section8.2);
• Operationalaspectstoconsider(section8.3);
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• Regulatory/politicalbarriers(section8.4);
• Technicalchallengesofmanaging,storingandanalysinglargeamountofdata(section8.5).
Theappendixcontains:
• Tableswiththedefinitionsofthecriteriaandmetrics(section10.1);
• Shortlistsofplatformsandsensors(section10.2);
• Comparison(section10.3)andevaluation(section10.4)matrices;
• Suppliercontacts(section10.5);
• Anexplanatorysectiononsurveyingwildlifepopulations(section10.6).
7 Introduction Industrial activities that involve pile driving (such as the construction of offshorewind farms or oil-rigs) or
activationof soundsources (suchasseismicsurveys)often involve the introductionofanthropogenicsound
energyintothewater.Concernhasgrownthatunderwatersoundhasthepotentialtoimpactmarinemammals
and othermarine animals, whichmay result in auditory injury and/or behavioural changes (Gordon, 2003;
Ketten,1995;Lucke,2009;Pirotta,2014).Thereisademandtoincreasemonitoringeffortsinordertocontribute
tobaselinedatafortheregionofinterestaswellastoassessandminimiseanthropogenicimpactonmarine
animals.Methodsarerequiredthatcaneffectivelybeusedforreal-timeornearreal-timemonitoringfortimely
decision making during operational environmental risk management and permit compliance (mitigation
monitoring), formonitoring individual animal's response to direct impacts (focal-follows), and for exploring
populationsizeanddistribution(populationmonitoring).Soundmayinfluencemarineanimalsatrangesthat
cannotbemonitoredfromtheimmediatevicinityofanoperationalsite.Theuseofautonomousvehiclesfor
mitigationmonitoring,populationmonitoringorfocal-followsthereforerepresentpotentialadvantagesforsuch
applications.Suchvehiclescanbedeployedintheair(UAS)orinthewater(AUV,ASV),andarethereforesuitable
forthedetectionofmarineanimalsattheseasurfaceandunderwater;Vehicles’applicationsreducehealthand
safetyriskstohumanoperators;andtheycanoperateatlongrangesfarbeyondtheanimaldetectionrangesof
humanobservers.UASplatformsincludepoweredaircraft,gliders,kitesandlighter-than-airaircraft,whichwill
mostlybeusedwithsensorssuchasthermal infra-red(IR),non-thermal IR,RedGreenBlue(RGB)andvideo
cameras.AUVsincludeunderwaterbuoyancyglidersandpropellerdrivencraft,butexcluderemotelyoperated
vehicles(ROV),whicharetetheredunderwatercraft,fromourdefinitionofAUV.ASVincludepoweredsurface
craftaswellasself-poweredsurfacevehiclesanddriftingsensorpackages.ForAUVandASVtherelevantsensors
formarineanimalmonitoringwithregardstoE&PactivityincludePassiveAcousticMonitoring(PAM)andActive
AcousticMonitoring(AAM)sensorsaswellasanimal-bornetags.IR,RGBandvideocamerascanbedeployed
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onAUV andASV, however are not useful for the kind ofmonitoring considered in this reviewdue to their
extremelylowunderwatervisiblerange(unlessinveryclearwaters).
Inthefollowing,thedifferenttypesofmonitoringmethodsrelevantformarineanimalmonitoringwithregards
toE&Pactivitiesaswellasthedifferenttypesofplatformsandsensorsaredescribedtoprovidebackground
informationonthetopicsdiscussedwithinthisreview.
7.1 Monitoringtypes
7.1.1 Mitigationmonitoring
Mitigationmonitoringisoftenrequiredwhereanthropogenicactivitiesinvolvetheemissionofsoundthatmay
impactmarinevertebrates.Itaimsforatimelydetectionofmarineanimalswithinapre-definedareaarounda
soundsourcetoallowformitigationmeasurestobetaken.Boththeanimalspeciesandthesizeoftheareato
bemonitoreddependontheregulationsthataretobemetbythespecificoperation.Forexample,guidelines
fortheimplementationofmarinemammalmitigationduringanthropogenicactivitiessuchasseismicsurveys
are often country specific.Most guidelines requiremonitoring for allmarinemammals, though sometimes
monitoringrequirementsareonlyinplaceforspecificsub-groups(e.g.baleenwhales,largertoothedwhales,
cetaceans)orspecificspecies.Seaturtles,polarbearsandwalrusmayalsobeofconcern.Theareathatrequires
mitigationmonitoringrangesfrom500mto3,000mandbeyond,andalsodependsontheregulations.This
areamayincludetheexclusionzoneonly.Theexclusionzoneistheareawithinwhichmitigationmeasureshave
to be taken upon an animal sighting. Some regulations request themonitoring of a larger area around the
exclusionzoneforensuringatimelydetectionofananimalbeforeitenterstheexclusionzone.Thetotalareato
bemonitored(includingtheexclusionzone)ishereafterreferredtoasthemonitoringzone.Anotherpointto
consideristhedurationofthemitigationmonitoring.Monitoringneedstobestartedaminimumof30minutes
beforetheactualoperationstarts.Someguidelinesspecifyarequirementformonitoringof60minutesor120
minutesbeforetheoperationstarts,dependingonthesituationorspeciesofconcern.Foracomprehensive
reviewoftheguidelinespleaseseeVerfussetal.(2016).
Marine mammal (and other animal) mitigation monitoring is traditionally performed by Marine Mammal
Observers (MMOs)orProtectedSpeciesObservers (PSO)visuallyscanningtheseasurfaceof themonitoring
zonetodetecttargetspecies.TheMMO/PSOsarelocatedeitherontheoperatingvesseloronsatellitevessels
accompanyingtheoperation.Toincreasetheprobabilityofdetectingsomespecies,visualmonitoringisoften
accompanied by acoustic monitoring with PAM systems. Thermal imaging systems have also been used,
especiallyforthedetectionofanimalsinsituationsoflowvisibility,whenMMO/PSOswillnotbeabletoconduct
visualmonitoringe.g.duetolackoflight.Imagingsystemstodatehavebeenoperatedfromvessels.
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7.1.2 Animalpopulationsurveys
Surveyscanaimtoanswerawiderangeofquestionsaboutapopulationofinterest.Inthisreport,wefocuson
a few (linked) applications of relevance to theO&G industry: estimating absolute population abundance or
density, assessing the spatial distribution of populations and how this may change over time and, finally,
assessingbehaviouralresponsestoanthropogenicimpacts.Theseapplicationsarelinkedbecausespatialand/or
temporalchangesinthesizeand/ordistributionofapopulation(whichmaybecausedbyanthropogenicfactors)
are typically best quantified by investigating changes in absolute abundance or density. Therefore, survey
requirementstoestimateabsoluteabundanceordensitywillbeinitiallyconsideredbeforeoutliningwhatwould
alsoberequiredtodetectspatialandtemporalchangesinthesemetricsoveranareaofinterestforaspecies
ofinterest,andidentifywhetheranydetectedchangesareduetoanthropogenicimpacts.
Marinemammalpopulationsizeshavebeentraditionallyestimatedusingsightingsdatacollectedfromvessel-
oraerial-basedsurveys(e.g.Garneretal.,1999).Inrecentyears,passiveacousticdatahavebeenincreasingly
collected fromboth vessel-towed instruments and static instruments (either fixed to a surface buoy or the
seafloor),whichhavebeenusedtoestimateanimaldensityorabundance(Marquesetal.,2013b).Approaches
to estimate fish population sizes include (1) conductingdedicated stock assessment surveys, (2)monitoring
catch-per-unit-effortdataor(3)taggingindividualswithactiveacoustictagsthatcanbedetectedusingpassive
acoustic receivers (Hilborn andWalters, 1992). However, the latter abundance estimationmethods rely on
catchingfish.Activeacousticmonitoringusingechosounders(SimmondsandMacLennan,2005)isanalternative
approachforusingdistancesamplingmethodologythatdoesnotrequireanimalstobecaughtandthecollected
datacanreadilybeanalysedinasimilarframeworktovisualandpassiveacousticsurveydata(e.g.seeCoxet
al.,2011forakrillexample).Largerfish,suchaswhalesharksandtuna,canalsobesurveyedfromaircraft(e.g.
Rowatetal.,2009;Bauer,2015).Turtlesarecommonlyvisuallysurveyedfromvesselsandaircraft(e.g.Benson
etal.,2007;Bresetteetal.,2010),thoughland-basedcountsatnestingbeachesandin-watermethodsusing
snorkellingobserversalsoexist(e.g.Weberetal.,2013;Stadleretal.,2014).Inrecentyears,theuseofactive
acousticstomonitorturtleshasbeeninvestigated.Turtleshavebeenfittedwithactiveacoustictransmitting
tags(Thumsetal.,2013)andtheabilitytouseechosounderstodetectturtleshasalsobeenrecentlyexplored
(Pérez-Arjona,2013).
Whethercollectingsightingsoracoustic(eitheractiveorpassive)dataduringwildlifesurveys,theaimisoften
toestimatethetotalnumberofanimalsinagivenareabasedonsomeformofencounterwithananimal,or
groupofanimalsandthemovementsoftheseanimals.Numbersofvisualoracousticencountersaresometimes
presentedasindicesofrelativeabundanceordensity.However,inordertolinkanyspatialortemporalpatterns
presentinsuchindicestochangesintheunderlyingabundanceordistributionofanimals,itmustbeassumed
thatanequalproportionofanimalsremainundetectedacrossspaceandtime.Thisisaverystrongassumption
tomakeand,therefore,statisticaldataanalysismethodshavebeendevelopedthatcorrectobserveddatafor
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undetectedanimals,leadingtoabsoluteestimatesofanimalabundanceordensity(Borchersetal.,2002)(see
section11.6.1fordetailedinformation).
Surveystypicallytakeplacealongplannedsurveytransectsorfromstaticmonitoringpoints.Insomesurveys,it
maybepossibletodetectallanimalswithinthetransectlinesorpoints.Suchsurveytypesareknownasstrip
transectsampling(usingtransectlines)orplotsampling(usingpoints).Whenanimalsaresuspectedtobemissed
within surveyedareas, thereare severalmethods that canbeused toestimate theprobabilityofdetection
(detailedinsection11.6.2).
Onceestimated,densityandabundanceestimatescanbeusedtoinvestigatespatialandtemporalchangesin
animal distribution and population size. Spatial maps of animal abundance can be produced using density
surfacemodelling(Milleretal.,2013;BorchersandKidney,2014),whichcanbeusedtoassesspotentialimpacts
ofanthropogenicactivity(Scott-Haywardetal.,2013;Bucklandetal.,2015).Timeseriesofdensityorabundance
estimatescanbeusedintrendanalyses,ifsufficientdataareavailable(Thomasetal.,2004).Poweranalyses
canbeconductedtoassesstheamountofdatarequiredtodetectsignificanttemporalandspatial trends in
densityorabundanceacrosstime(Thomasetal.,2004).
7.1.3 Focal-follows
There may be a requirement to assess potential anthropogenic impacts at a finer resolution by studying
individualanimals.Focalanimalsurveyshavedifferentattributestopopulation-focussedsurveys.Conducting
individual-basedsurveysofmarinemammals,turtlesandfishrequiresfocalanimalstobedetected,assessed
andtracked.Reactionsoftrackedanimalstospecificsounds(e.g.inplaybackexperiments)orotherstimulican
thenbeassessed(e.g.Antunes,2014l;Miller,2014).Marinemammals,turtlesandfishhaveallbeentracked
usingtelemetrydevices(e.g.marinemammals:Curéetal.,2015;turtles:Kobayashietal.,2008;fish:Blocket
al.,2005),andvisualfocalfollowsofmarinemammalsareregularlyconductedfromvessels(e.g.Karniskietal.,
2014).Marinemammals can also be tracked using passive acousticmonitoringwhilst they are acoustically
detectable(e.g.Gassmannetal.,2015),andunderwatervideohasbeenusedtomonitorindividualturtlesand
fish(turtles:Smolowitzetal.,2015;fish:Mellody,2015).
7.2 Autonomousplatformtypes
7.2.1 Poweredaircraft(UAS)
Poweredaircraftcaneitherflyautonomouslyorbepilotedremotely(Figure2).Theequipmentcanbelaunched
fromdifferentplatformsusing,forexample,catapultsystemsandrecoveredbyusingahookandnetsystem
whendeployedfromaship,orbylandingonaflatsurfacewhendeployedfromlandorship.Theaircraftwill
autonomouslyfollowthedesignedtrackbasedonprioruploadedflightplans,oftenrelyingonpilotsfortake-off
andlandingonly.Agroundstationplaysakeyroleinmissionexecutionandplanning,asitallowsforcontroland
monitoringoftheaircraftandpayload,anduploadingandupdatingwaypointsandflightplansbeforeandduring
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the flight. A typical ground station will have a telemetry link to the aircraft for monitoring and control, a
visualisationoftheaircraftandtheflightplanandtheabilitytoaltertheflightplanduringthemission.Here,we
include fixed-wing systems and Vertical Take-Off and Landing (VTOL) systems. VTOL systems are gaining
attentionfortheirflexibilityindeploymentandrecovery,whichovercomesomeofthelimitationsoffixed-wing
systems. VTOL unmanned aircraft categories includeMulti-copters/Multi-rotors, Quadcopters, Hexacopters,
Octocopters,andspecialisedFixed-wingsystemswithmulti-rotorsinstalledwhichcanbeusedforbothtake-off
and landing. In general, the system payload is dependent on the size of the aircraft, with larger aircraft
supportingheaviersensorsystemssuchassinglelensreflex(SLR)camerasand/ormulti-spectralandthermal
sensors.ThesesystemscanbevaluableduringanimalsurveysforE&Pactivitieswhichheavilyrelyonthecamera
quality,maximumaltitudeandflighttime(Koskietal.,2009a).
Figure2.ExamplesforUAS:Leftpicture:SkyprowlerUAS©KrossbladeAerospace,rightpicture:LandingofCryoWingUAS.PhotobyNORUT.
7.2.2 Motorizedgliders(UAS)
Motorizedglidersarea special classof fixed-wingaircraft capableof turningoff theengineandperforming
(parts) of the flight through soaringon rising air.Unmannedgliders are rare, andareonlyused for specific
scientificresearchinmeteorologyoratmospherescience,ormilitarypurposes(glidingbombs).Asthecolumn
ofairaboveanoceancanberegardedasasinkzone(whichistheabsenceofweakatmosphericthermalsto
pushtheaircraftupwardsormaintainaltitude),motorisedglidersareconsideredimpracticalformarineanimal
studiesandmonitoringinrelationtoE&Pactivities.
7.2.3 Kites(UAS)
Kites,whichareessentiallysmallparachutes(Figure3),consistofasteelframewithanengineandparachute
wing.Thesesystemsareeasytotransportandareabletocarrystill,videoand/ormulti-spectralandthermal
sensors. They can also operatewith auto-pilot and conduct full coverage of the surveyed areawith image
overlap. However, this equipment can only be operated in wind speeds under 6m/s, whichmay limit the
locationsinwhichtheycanbeused.Addingtothedependencyinenvironmentalconditions,thisequipmentis
often not stabilised, resulting in difficulties in hovering over an object or location of interest. This type of
platformhasbeensuccessfullyusedinEurope,AfricaandAustraliamainlyformapping,agricultureandforest
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management(Thamm,2011).Thelimitedamountofscientificliteratureconcerningtheapplicationsofthese
systems inmonitoring during offshore operationsmay indicate the lack of tests conducted. This is possibly
associated with the weather limitations of this equipment. However, combined kites with lighter-than-air
aircraftseemtohavebecomeapopularalternative(e.g.MacKellaretal.,2013;Verhoevenetal.,2009).These
combinedsystems,socalled"kitoons"havethequalitiesofbothsystemswithfewoftheirlimitations.Though
tethered,theyareabletoendurestrongerwindswhilekeepingarelativelystablepositionduetotheirballoon
bodyandattachedsail,whichhighlightstheirpotentialformarinedataacquisition.
Figure3.KiteSUSI62take-off.PhotobyThamm,2011.
7.2.4 Lighter-than-airaircraftsystems(UAS)
Lighter-than-airaircraftsystems(blimpsorballoons,Figure4)consistofabuoyantgas-filledballoon(e.g.helium
orhydrogen)withaconnectiontosupport imagerypayloadwithstraight-downortiltedviews.Thetethered
balloonisthenconnectedandtowedbyavessel(Hodgson,2007).Themaindifferencebetweenablimpanda
balloonisthelackofatetheringsysteminblimps,whichmakesthemlessstableinharshweatherconditions
butenablesthemtoconduct independent flightswithoutrelyingonasupportvessel.Therequiredsizeofa
blimpisdependentonthepayloadweightneeded.Operatingasmallerandlighterpayloadreducesthecostsby
minimisingtheamountofheliumrequired,butmayresultinlowerimagequality.Amechanismtodetermine
distance is not yet incorporated in these pieces of equipment (Hodgson, 2007). However, the systems are
capableofcarryingGPS technology thatmay improvepositionaccuracyandestimatedistance to the target
object.
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Figure4.Thelighter-than-aircraftsystem"blimp-cam"andcomponentsnecessaryforflights.PhotobyHodgson,2007.
7.2.5 Propellerdrivenunderwatercraft(AUV)
Propellerdrivenunderwatercraftaregenerallyrelativelysmallautomatedvehicles(seeFigure5),manoeuvrable
in the water column in three dimensions by an on-board computer. Although these systems are normally
deployedfromavessel,theyarenotphysicallylinkedtoit(untethered)andperformthedatacollectioninan
autonomousmanner(Wynnetal.,2013).
MostpropelledAUVfollowatraditionaltorpedoshape,whichoffersthebestcompromisebetweensize,usable
volume,hydrodynamicefficiencyandeaseofhandling(Vijay,2011).OthershapesfoundinpropelledAUVare
teardrop, oblate, rectangular or open space frame (typically twin-hull). They range in size from a portable
lightweightAUVtolargediametervehiclesover10mlength(Vijay,2011).Thevehicletypicallyfollowsaprecise
pre-programmed course and canoperate forperiodsof a fewhours to several days inmost environmental
conditions.Somenavy-developedmodelshavebeencontrolledviabidirectionalradiooracousticdatalinksand
somehavebeenprogrammedtomake“smart”navigationdecisionsbasedontheirownsensordata.Propelled
AUVoperateincruisemode(automated),collectingdatawhilefollowingapre-plannedrouteatspeedsbetween
1and4knots(Vijay,2011).TravellingspeedsofthepropelledAUVarecomparabletotidalcurrents,whichcan
producenavigationaldriftandtherebyaffectthedataquality.ThismakespropelledAUVlesssuitedtoshallow
wateroperations (Wynnetal.,2013).MostpropelledAUVcanoperateatdepthsofover200m,withsome
modelsabletoreach6,000mdepth.PropelledAUVkeepalineartrajectorythroughthewater,whichisessential
forseabedmappingandsub-bottomprofiling.However,foroptimalmanoeuvrability,theweightandbalance
ofagivenAUVcraftisasignificantconsideration.
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Figure5.PropellerAUVREMUS100inafieldstudy(left,byAnreyShcherbina,WHOI)anddetailofthemodel(right,KongsbergMaritimewebsite).
Propeller based thrusters, particularly Kort nozzles, are themost commonpropulsion systemsused inAUV.
Thesethrustersareusuallydrivenbyelectricmotors,poweredbyrechargeablebatteries.Batteriescanbeused
forextending theendurance (e.g.manganesealkaline, lithium),but thisalso increases thecostpermission.
Somelargervehiclesmaybepoweredbyaluminiumbasedsemi-fuelcellsforahigherenduranceandpower
output.Silver-zincbatteriesallowtheAUVtocovera longerdistance,but lithium-ionbatteriesarethemost
cost-effective(longerlife).Othertypesofrechargeablebatteriesaresealedleadacid,nickelcadmiumandnickel
hydride(Fernandesetal.,2003).Mostoftheinternalspaceisusedbythepowersource(e.g.batteries)andthe
navigationcontrolinstrumentation.However,AUVtypicallyfollowmodulardesigntoenablecomponentstobe
changedeasilybyoperators(Vijay,2011).
7.2.6 Buoyancygliders(AUV)
Autonomous underwater buoyancy gliders (hereafter UW gliders, Figure 6) are slow moving (~0.5 knots),
typically small (<2 m), low power platforms that house sensors capable of making multidisciplinary
oceanographic observations over months and hundreds to thousands of kilometres (Davis et al., 2002).
Underwaterbuoyancyglidersmovethroughthewatercolumnbychangingtheirvolumeandbuoyancytoprofile
verticallyintheocean.Variationsinvolumeareachievedthroughinflatingordeflatingtheiroilorwaterfilled
bladder.Wingsareusedtoconvertverticalvelocityintoforwardmotion,sothattheglidertypicallyfollowssaw-
toothprofiles.Thevehicleissteeredeitherbychangesinthecentreofgravityrelativetothecentreofbuoyancy
or by using a rudder.When at the surface, satellite navigation and communication enable the glider to be
directedandcontrolledremotelyandperformadownloadofdata.
Therearecurrentlythreeclassesofunderwaterbuoyancygliders(WoodandMierzwa,2013):1)thosethatuse
mechanicalorelectricalmeansofchangingtheirbuoyancy(i.e.dropweightsorelectricalpowerfrombatteries),
2)thosethatusethethermalgradienttoharnesstheenergytochangethevehiclesbuoyancy,and3)hybrid
vehiclesthatcombinestandardpropulsionsystemsandbuoyancyglidersystems.
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Figure6.UnderwaterbuoyancyglidersSeaglider(left)andSlocum(right)(images©DamienGuihen).
7.2.6.1 (Mechanical)Electricbuoyancygliders
Electric buoyancy gliders change their buoyancy by converting battery energy into a volume changewith a
hydraulic pump. Energy efficient movement is enabled through using only slight changes in buoyancy to
minimizeexpendedenergyandmovingslowlytoreducedrag.Fairing(theexternalsurface)shapeisdesigned
todecreasevehicledrag.
7.2.6.2 Thermalbuoyancygliders
Inanelectricbuoyancyglider,approximatelyeightypercentofvehiclepowermaybeusedforballastpumping
toalterdensity(Graver,2005).Thermalbuoyancyglidersusethedepth-relatedvariationinoceanictemperature
toeffectchangesinplatformdensity.Volumechangeinaphase-changematerialsuchaswaxisusedtoalter
buoyancyasitvariesbetweenaliquidandasoliddependingontemperature.Extrapolationofenergyuseduring
ashortdeploymentofaSlocumthermalinMarch2010indicatedapotentialenduranceof3.4years(Joneset
al.,2011a),greaterthanthreetimesthedurationoftheelectricbuoyancyglider.Initiallyjustusedforpowering
thebuoyancychange,researchanddevelopmentisnowexaminingthepotentialforharvestingthermalenergy
topowerthesensors(Jonesetal.,2011a;Buckleetal.,2013).
7.2.6.3 Hybridbuoyancygliders
Thehybridbuoyancyglidercombinesthebuoyancypropulsionmethodwithjetorpropellerpropulsion,where
internalenergy isconvertedtopropelleror jetthrust.Buoyancyglidersareconstrainedbyarequirementto
undertakeverticalsawtoothprofileswhenmovingforward.Jet/propellerpropulsionenablesgliderstomake
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constant altitude transits and can improve their ability tomake progress against strong ocean currents, or
navigateareaswithrestrictedaccesstosurfacewaters(Claus,2009).
7.2.7 Poweredsurfacecrafts(ASV)
Unmannedpoweredsurfacecraftoffergreatversatilityandanumberarenowavailable.Thereisadiversityof
approachesanddesignsandtheytakemanyforms.Mostoftenthesevehiclesareoperatedremotelyfromshore
orasupportvessel.Suchcraftmayalsobeknownas“autonomous”.Asadefiningterm,“unmanned”ismore
technicallycorrectasnoneofthesevehiclesareautonomous inthesenseofbeingwholly independent–all
require some mission planning and/or remote operation by human hand. The following examples of
autonomous powered surface craft are categorised into three groups based solely on hull shape: boats,
catamaransandsemi-submersibles.
7.2.7.1 Boat
ManysuchASVbearastrongresemblancetoanotherwisefamiliarcraft,suchasarigid-hulledinflatableboat
(RHIB)withaninboardoroutboardmotor.Essentially,anysmallcraftcanbepotentiallyconvertedintoanASV
bytheadditionofcontrols,navigationandtelemetry.Forlegalreasons,duetothepotentialfornavigationalrisk
toothercraft,boatscannotbefullyautomated,andhavetoincorporatethepossibilityofmannedoperation.
Oneofthemostpopularwaystocreateautomatedordual-mode(manned/unmanned)vesselsistointegrate
manual-to-automatedcontrolconversionkits intocommercialvessels(Caccia,2006;Cacciaetal.,2009).The
advantageofthisapproachisthatremotecontrolbringsarangeofpossibilitiestofamiliarvesselsofgreatersize
large and complexity. The disadvantage is that the increased complexity may bring issues on mechanical
reliability,whichcannotberesolvedremotely.
Figure7.AnAUVboattypeViknesdesignedanddevelopedbytheNorwegianUniversityofScienceandTechnologyinconjunctionwithMarineRobotics(image©NorwegianUniversityofScienceandTechnology).
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7.2.7.2 Catamarans
Forautonomousoperations,acatamaranbasedhullgivestheabilitytocarrysignificantpayloadonalargerarea
of deck space. This enables ready access to the payload and a flexible modular build approach as well as
supportinglargetop-heavyunits,suchasatelemetrymast.Acatamarangivesaverystableat-seaplatformbut
thismaybeat theexpenseofmanoeuvrability– themaindisadvantagecitedof thishull-type.Acatamaran
shapeisreadilyscalableinsizehoweverandthisallowsforscopeinambitionandcost.Smallermodels,suchas
theSpringer(Figure8left),areidealforresearchbuthavealsobeenutilisedforcivilapplicationsandeducation
purposes.ThelargerASVC-Enduro(Figure8right)utilisesitsshapeanddeckspacetocarrysolarpanelsand
supportathree-bladewindturbine.Missionrangeistherebyincreasedandspacestillremainsfortheaddition
ofsensorpackages.Thishas includedSeichePAMwhichunderwenttrials in theGulfofMexico in2015and
successfullydetectedanimalsremotelyandinreal-time.
Figure8.Catamarans:Leftpicture:Springer,acatamaranbasedASVdevelopedbyPlymouthUniversity(image©PlymouthUniversity),rightpicture:ASVC-Enduro,alargescalecatamaranbasedASV(image©ASV).
7.2.7.3 Semi-submersible
Themainbodyofthesemi-submersiblevehicle(Figure9)remainsunderwater.Onlyasmallsurfaceexpression
isgivenwiththecommunicationsmastandairintakeprotrudingabovethewaterline(Manley,2008).Thecraft
isnecessarilybulkyandtheeffectistoreducetheeffectsofwavesandmakeitverytolerantinroughseas.The
vehicleoffersaparticularlyhighlevelofsea-goingcapabilitygivenitsrelativesmallsizeandcost.Italsoenables
excellent seakeepingcapabilities,beingable to remainat itsdesignwaterline inalloperational speedsand
conditions,withoutrelianceonhydrofoilcontrol.Thesemi-submersible’sbulkalsolendsithighsurvivability.On
thedownside,physicallymanagingthevehicleduringlaunchandretrievalremainspotentiallychallenging.The
design is primarily intended for application in naval use but has potential for conducting bathymetric and
hydrographicsurveysincivilandcommercialsectors(Wolking,2011).
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Figure9.Thesemi-submersibleASV-6300designedbyC&CTechnologiesandASV(imagefromWolking,2011).
7.2.8 Self-poweredsurfacevehicles(ASV)
Self-poweredsurfacevehiclessuchaswaveandwindglidersuserenewableenergyfromwave,windorsolar
energy for generating forward motion or station keeping. The Liquid Robotics Waveglider (Figure 10), for
example,isnowawell-establishedautonomoussurfacevehicle.Itssurfacefloatcontainssolarpanelstopower
thenavigationsystemsandsciencepayloads,whileforwardpropulsionisprovidedbyarackoffinssuspended
7metres below the floatwhich directly convertwavemotion into forward power. The latestmodel of the
Waveglider,theSV3,alsohasasmallelectricallydrivenpropeller.Thecombinationofwaveandsolarpower
meansthatthesevehiclescanstayatseaindefinitelyanddeploymentsofmanymonthshavebeenreported.
Otherwaveandwindpoweredvehiclesarealsobecomingcommerciallyavailablesuchasthewindpowered
SailBuoy1andthewavepoweredAutonaut
2and is likelythatothervehicleswillappearofthemarket inthe
comingyears.
1 http://www.sailbuoy.no/ 2 http://www.autonautusv.com/
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Figure10.UnderwaterphotographofaLiquidRoboticsWaveglidershowingthesubunit,whosemoveablefinsconvertverticalmotionfromwavesintoforwardpowerandthesurfacefloat(photo,LiquidRobotics).
7.2.9 Drifter(ASVandAUV)
Driftingsensorpackageshavelongbeenusedbyoceanographers,oftenbecausetheirlowcostmakesitpossible
todeploymanysensorsatonce,therebyincreasingsamplesize.Manydrifterssimplystayatthesurfaceand
relay theirpositionsviasatellite link inorder tostudyoceancurrents.Moresophisticateddevicesalsocarry
sensorpackages,andinthecaseoftheARGOfloats3,moveupanddownthroughthewatercolumn,sampling
atvaryingdepths,beforetransmittingtheirdatatoshore.
TheclassicexampleofadriftingPAMsystemisthesonobuoy.Thesehavebeenusedbythemilitarysincethe
1940s,primarilyforsubmarinedetection.Modernsonobuoysgenerallyconsistofacylindricalpackagethrown
fromanaircraft.Whentheyhitthewater,ahydrophonedeploystoasetdepthandaVHFtransmitterisused
toradiosoundsbacktothecirclingaircraftwhichmustremainwithinlineofsightofthebuoy.Constellationsof
buoyscanbeusedtolocalizesoundsources,thoughlifetimesaretypicallyonlyafewhours.
Mostautonomousdriftersystemsarenon-recoverable,withallimportantdatabeingtelemeteredtoshoreor
satellitelink,whichpresentsachallengeforacousticmonitoringdataduetothehighvolumesofdatainvolved.
Griffiths (2015) describes a low-cost drifting sensor package for passive acoustic monitoring which can be
constructedforaround$5,000.Thissystemcontainsatwo-channelrecorderanddataarestoredonboardthe
device,onlyavailableonceithasbeenrecovered.
3 http://www.argo.ucsd.edu/
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7.3 Sensortypes
7.3.1 Electro-opticalimagingsensors
There are fourmain imaging technologies that should be evaluatedwhen considering detection ofmarine
animalsfromaircraft;RedGreenBlue(RGB),thermalInfra-Red(IR),non-thermalIR,andvideocameras.These
foursensortypesdiffermainlyinwhatpartoftheelectromagneticspectrumtheycanregisterradiationfrom.
ThoughRGBcamerasareabletocapture infraredwavelengths, for thepurposeof thisevaluationweassign
infraredcamerastoitsspecificcategory,includingboththermalandnon-thermalinfrared,giventhatthereare
sensorsdevelopedforthesinglepurposeofcapturingIRwavelengths.Severalsensorsarecurrentlyavailable
forUAS.Dependingonthepayloadavailable,asingleUASmaycarryoneorseveralsensorstoincreaseaccuracy
ofdetections,whichcanprovide informationonobjectpresence/absenceandanimalbehaviour/movement.
The informationgatheredbythesesensorscanbetransmittedtothegroundinnearreal-timeorstoredon-
boardtheaircraftforpost-processing.Byutilisingthedatawithregardtopositionandorientationofthecamera,
foreachimageorframe,itispossibletogeoreferenceeachindividualpixelinanimageinrelationtoageographic
coordinatesystem.Thisissometimesalsoreferredtoasgeocoding.Fordetectinganimalsfromanunmanned
aircraftitisimportanttomaximizethefieldofviewwhilemaintainingsufficientsurfaceresolutionandcontrast.
Todetectsmallmarinemammalssuchasporpoiseswhilesurfacingasurfaceresolutionofapproximately20
cm/pixelisrequired.
7.3.1.1 RGBcameras
AnRGBcameraconsistsofmillionsofsmallelectro-optical(EO)sensorelements,referredtoaspixels,which
register incoming radiation focused througha lens, and convert it to anelectric charge. This charge is then
digitisedandstoredonthecameraortransmittedtoacomputer.Camerascandetectradiationindifferentparts
ofthespectrumbutmostcommercialcamerasonlydetectlightinthevisiblerange(VIS)oftheelectromagnetic
spectrumbetween400to700nm.Thetwomostcommoncamerasensorstodayarethecharge-coupleddevice
(CCD)andthecomplementarymetal-oxidesemiconductor(CMOS).Tobeabletoseparatecoloursinanimage
usingasinglecamerasensor,theindividualsensorelementsarefittedwithared,greenorbluecolourfilter
array.Cameraswithnofilterarereferredtoasmonochromecameras.Theycannotseparatecoloursbutbenefit
from a much higher sensitivity than a colour camera. Commercial cameras come in a variety of different
qualities,withregardtoresolution,dynamicrangeandsensitivity.Camerascanrecorddatawithahighframe
rate,which,combinedwithahighresolution,generateslargeamountsofdata.
7.3.1.2 ThermalIRcamera
Thermal camerasor sensors register thermal radiationemitted fromanobject. Thermal imagers aremainly
dividedintwocategoriesbasedonthewavelengthregiontheyoperatein(1)mid-wavelengthinfrared(MWIR)
imagersoperatingintherangeof3to5µmand(2)long-wavelengthinfrared(LWIR)imagersoperatinginthe
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rangeof8to15µm.LWIRimagersaremostcommoninsmallandmediumsizeUASduetotheirsmallsizeand
relativelowcost.However,theresolutionislimitedandatypicalhighendLWIRimagerhasaresolutionof640
x512pixels(Tau640,FLIRSystems).Theenergyemittedfromanobjectwithregardtofrequencyorwavelength
canbeestimatedusingthePlanck’slawforblackbodyradiation(Planck,1914),wheretheenergyisshiftedto
longerwavelengthforlowertemperatures.Hence,forobjectssuchasmarineanimals,LWIRimagerswouldbe
theappropriatesolution.
Water isnon-transparent to thermal radiation, anda thermal IR systemcannot seeanythingbelow the sea
surface (evena fewmicrometres).Hence, theuseof IR systems formarineanimalobservationandstudy is
limited to surfacing animals only, resulting in the exclusion of fish and turtles as study objects (except for
leatherbackturtles,whichseemtobeanexceptionastheyregulatetheirbodytemperaturesimilartomammals
(Bostrometal.,2010)).Athinlayerofwateroftencoverssurfacinganimalsandexperimentshaveshownthat
when a surfacingminkewhale (Balaenoptera acutorostrata) is covered by a thin film ofwater, its thermal
radiationisalmostcompletelymasked;thetemperaturedifferencebetweentheanimalandthesurrounding
waterisusuallylessthan0.1°C(Baldaccietal.,2005).However,theblowandblowholeareeasiertodetectand
temperaturedifferencesofover4°Cbetweenblowholeandsurroundingwaterhavebeenreported(Baldacciet
al., 2005). Several publications have investigated the use of IR cameras for detecting blows using thermal
imagers;blowsfromlargebaleenwhalesweresuccessfullydetectedupto7kmaway(SanthaseelanandAsari,
2015; Weissenberger et al., 2011; Zitterbart et al., 2013). These studies all applied a higher quality (third
generationorlater)thermalimagerplacedonalargeshiporland-basedinstallation,allowingaviewoftheblow
againstthewaterbackground.Additionally,studiesfrommannedaerialsurveysprovideevidencethatitisalso
possible to detect thermal "footprints" of animals surfacing, using the same typeof equipment (Churnside,
Ostrovsky,andVeenstra,2009).
UsingthermalIRfromUASfordetectionofmarineanimalsseemsplausibletodetectmarinemammalsboth,
duetothelargethermaldifferencebetweenthecetaceanblowholeandthesurroundingsea(seealsoVerfuss
etal.,2016),anddifferencesinbodytemperatureofsmalleranimals.Inpracticehowever,wehavenotfound
any publication documenting the use of thermal imagers onUAS formarinemammal detection. Awhale’s
blowholeiscomparativelysmallandwouldrequirearesolutionofacoupleofcm.Forachievingaresolutionof
2cm/pixel,theresultingwidthofthescenecoveredbyathermalimagerwith640pixelswouldonlybe12.8m.
Toachieveanappropriateresolution,severalcamerascouldbecombined,whichwouldresultinabulkysolution
notwellsuitedforsmallormediumsizeUAS.ThermalIRisapromisingtechnology,thoughthecombinationof
payloadrequirementsofUASandcameraresolutionforaerialdetectionthatiswithinreasonablepricerange
forunmannedsurveys,ispossiblythemainreasonforwhythishasyettobedevelopedfurther.
7.3.1.3 Non-thermalIRcamera
Non-thermalIRcamerasoperateinthelowendoftheIRspectrumandareoftendividedintotwogroups:near-
infrared(NIR)intherangeof0.74to1.0µmandshort-wavelengthinfrared(SWIR)intherangeof1to3µm
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(Maldague,2007). Inthisregionofthespectrumverylittleenergyisemittedfromobjectswithnormalbody
temperatureandthesecamerasworksimilarlytoRGBcameras,whicharebasedon lightreflectedfromthe
object.SWIRcamerasexcelinlowlightconditions,partlybecausethesesensorsarehighlysensitive,butalso
duetoaphenomenonreferredtoasnightskyradiance.Thenightskyemitsfivetoseventimesmoreillumination
thanstarlight,nearlyallofitintheSWIRregion.Hence,withaSWIRcameraonecan“see”objectswithgreat
clarity onmoonlessnights. SWIR cameras are available in small sizes, such as the TAUSWIR (FLIR Systems)
weighting only 130 grams.However, SWIR cameras share similar limitations as thermal LWIR cameraswith
regardstorelativelowresolution(nomorethan640pixels).WehavenotfoundanyworkwhereSWIRcameras
havebeentestedforthedetectionofmarinemammals.
7.3.1.4 Video
TheuseofaHDvideowitharesolutionof1,920x1,080pixelsatanoperationalaltitudeof122m,andaground
resolutionof20cm/pixelcrossresultsinacross-trackdistanceof384mtrack(stripwidth),basedonoperations
within the Visual Line of Sight (VLOS). Flights at higher altitudes will cover larger areas, though national
authoritiesrequirespecialpermitstooperateatlargerrangesandaltitudes.Oneofthemostimportantfeatures
forthedetectionofmarineanimalsfromUASusing livevideo isthatthecamera isstabilisedusingagimbal
(Koskietal.,2009a).Thiswillreduceimagevibrationandimprovetheefficiencyoftheoperatormanuallytrying
toidentifyanimalsinthevideo.Reducingimagevibrationwillalsoreducethevideobitrateduetoimproved
encodingefficiency.Thoughitisalsopossibletostorethevideocollectedforfutureanalysis,thetransmission
ofreal-timedatatothegroundstationcanprovidevaluableinformationthatcanbeusedforbothscienceand
management(e.g.Hodgsonetal.,2013;Koskietal.,2013;Koskietal.,2009a).
7.3.1.5 Otherelectro-opticalimagingsensors
SensorscollectingLIDAR(Light-DetectionandRanging)remotesensingdatausepulsedlaserlighttomeasure
rangestotheEarthbyusingultraviolet,visibleornearinfraredlighttoimageobjects(NOAA,2015).Thistypeof
sensorhasprovenitsvalueinsurveysofmannedaircraftanditisgaininginterestintheUASmarket.LIDARhas
shownitsrelevanceinfisheriesandcandetectfishatdepthsupto16meters(Butler,1988)thoughthedetection
of marine organisms using such technology remains untested for UAS surveys, possibly due to the same
limitationsasforinfraredequipment.Despitetheinterestintechnologiesusinggreenlightthatcanpenetrate
thewater surface, thisequipmentuse currently appears tomainly focuson topography (including sea floor
elevations)andhydrodynamicmodelling.Thus,theuseofthistypeofequipmentisnotfurtherdealtwithinthis
report.
7.3.2 PAMsystems
Passiveacousticmonitoringreliesondetectingthesoundsproducedbyanimals.Threegroupsofmarineanimals
producesound:crustaceans(predominantlyshrimp),fishandmarinemammals(Horne,2000).Snappingshrimp
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aredominantsoundproducersinshallowwatersatlatitudesoflessthan40°(Horne,2000),asinglesnapcan
reachpeak-to-peaksourcelevelsof185dB(re1µPa)withabroadfrequencyspectrumupto200kHz(Auand
Banks,1998).Soniferous(soundproducing)fisharecommon,andcanbeusedtoidentifyspecificspecies.Over
800speciesoffishfrom109familiesworldwidearecurrentlyknowntobesoniferous(Kaatz,2002).Fishproduce
soundstocommunicatewitheachotherwhilefeeding,matingoraggressivedisplaysaswellasmakingincidental
noisesassociatedwithfeeding,swimmingandotherbehaviours(Rountreeetal.,2006).Thesoundsoffishesare
rarelyhigherthan1kHz,atleastinthezonesofmaximumamplitude,andincludethoseproducedbyfriction
between bones, contractions of sonic muscles, or hydrodynamic noises from swimming. A comprehensive
reviewofsoundandsoundproductioninfishescanbefoundinKasumyan(2008).Itisgenerallyconsideredthat
PAMcouldbeusedtomonitorthedistribution,abundanceandbehaviouroffish(Rountreeetal.,2006).The
useofPAMfor thedetectionof fish isstillverymuchadeveloping fieldofactivity,andwhile theremaybe
potential for monitoring some species and spawning events, active acoustic survey and tagging fish with
ultrasonicsoundemittingtagsremainthemorecommonmethodsusedtostudyfishspecies.
Thespeciesgroupforwhichpassiveacousticmonitoringismostwelldevelopedismarinemammals,particularly
cetaceans (whales anddolphins).All cetacean species are known tomake soundsof some typeor another,
althoughforseveralspecies,soundproductionislimitedtocertainindividualsatcertaintimesoftheyear(for
instance,malehumpbackwhalesingingduringthebreedingseason).Forsomespecies,particularlydeepdiving
odontocetes,individualsspendsignificantlymoretimevocallyactiveandavailableforacousticdetectionthan
theyspendatthesurfacewheretheywouldbeavailableforvisualdetection.Ontheotherhand,manyspecies,
orimportantcomponentsofpopulations,areknowntoremainsilentforextendedperiods,thusmakingthem
unavailableforacousticdetection.
The frequency rangeofmarinemammals vocalisations span14octaves from the infrasonic soundsof large
baleenwhales,witheachsoundlastingformanyseconds,toshort(100µs)ultrasonicecholocationsignalsfrom
smallodontocetesatfrequenciesrangingupto150kHz.Forexample,individualvocalisationsofabluewhale
maylast150,000timeslongerthanthoseofaharbourporpoise,andthefrequencyofaharbourporpoiseclick
maybe15,000 times thatof abluewhale call. This incrediblywide rangeof sound types createsparticular
problemsforsystemsattemptingtodetectandlocalisemultiplespeciestypes.
VocalisinganimalscanbedetectedsolongasthePAMsystemhassufficientbandwidthtocapturethesounds
of interest. Sampling theory dictates that the sample ratemust be at least twice the highest frequency of
interest,soforsmallodontocetes,suchastheharbourporpoise,sampleratesintheseveralhundredsofkHz
arerequired,witha500kHzsampleratebeingtypicalformanysystems.
Detectionrangeisgovernedbyhowloudtheinitialsoundis,howmuchnoisethereisaroundthereceiverinthe
frequencybandofinterestandtransmissioneffectssuchasdownwardrefractionandhigherabsorptionofhigh
frequencysounds.For largebaleenwhales,detectionranges inthetensorevenhundredsofkilometresare
possibleandspermwhalesareoftentrackedatrangesofseveralkilometres.Smallerspeciesthatproducehigher
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frequencysignalstendtohavemuchshorterdetectionranges,ofteninthelowhundredsofmetreseveninlow
noise conditions. Detection range can be expected to vary dramatically depending on local industrial noise
sources,suchasairgunsandvesselpropulsionsystems.Certainlyabigpotentialadvantageofsmallautonomous
vehicles for PAM is that the local noise field can be expected to be both low and consistent between
deployments,meaning thatdetectionperformancemaybebothbetterandbetterunderstood than it is for
manyvesselbaseddeployments.
Acousticsourcescanbelocalisedusinganumberoftechniques.Bearingstosoundscanbeobtainedwithsmall
clustersofhydrophonesand forsomespecies,multiplebearings to individualanimals, taken fromamoving
platform,canbeusedtoestimaterangeasisdemonstratedforspermwhalesinLewisetal.(2007).Asageneral
rule,thelowerthefrequencyofthesoundofinterest,thewidertheseparationbetweenhydrophonesneeded.
Separationsoftensofcentimetresareadequatetoobtainbearingstobroadbandorhighfrequencyodontocete
clicks,butseparationsofmanymetreswouldbeneededtoestimatebearingstolowfrequencybaleenwhale
calls.Inordertomeasurerangedirectlytothesourceofasinglesound,widelyspacedhydrophonesarerequired
andlocalisationwillonlybeaccuratewithinapproximately3timesthearraydimension(i.e.ifhydrophoneswere
spreadover100m,localisationwouldbeaccuratetoabout300m).Separatinghydrophonesbysuchdistances
canpresenttechnicalchallengesonboardavesselandwouldbeparticularlyproblematiconsmalllowpowered
autonomousvehiclesystems.
MostPAMsystemsincludeahumanoperatorinanydecision-makingprocess,whetherthatbereal-timedecision
makingorpostanalysisofdata.Bytheirverynature,autonomousplatformsdonotaccommodateahuman
operator,sodatamustbestoredortransmittedinaformwherebyitremainspossibleforanoperatortoview
sufficientdatafordetectionverificationpurposes.
PAMsystemsforautonomousdatacollectioncanbroadlybedividedintotwocategories:
1. Rawdatasystems
2. Systemswithonboarddataprocessing.
Thelargesizeofmodernharddrives,andmorerecentlyofflashmemorycards,hasledtothedevelopmentof
awidevarietyofsystemswhichsimplystreamrawdatatostoragemediaforlateranalysis.Fixedautonomous
recordersforPAMwerethoroughlyreviewedinanindustry-fundedprogramin2013(Sousa-Limaetal.,2013)
anditisnotourintentiontorepeatthisreviewprocesshere.Asisclearfromthiswork,thereisageneraltrade-
off between size and capability in these systems.Many of these systems are nowwell established and are
commerciallyavailableforhireorpurchase.Whileintendedforfixeduse,manycouldpotentiallybeadapted
(e.g.throughtheadditionofanexternaltowedhydrophone)foruseonmovingautonomousplatforms.
Packageswithonboardprocessingare lesscommon,althoughtherecentavailabilityofpowerful lowpower
usageprocessors,oftendevelopedforthesmartphonemarket,meansthatthisisarapidlydevelopingfield.
Whenagliderisatthesurface,summaryinformationofcandidatedetectionsissenttoshoreviasatellitelink
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whereitcanbeverifiedbyahumanoperatorfornearreal-timedetection.ThesystemdevelopedbyKlinkto
detect beaked whales also sent back some summary information about detections during each dive, but
insufficientdataweresenttoshorefordatavalidation.Datavalidationwasthereforeonlyconductedoncethe
vehicle was recovered and full recordings analysed. Similarly, Gillespie deployed a Decimus system on a
Wavegliderforthedetectionofharbourporpoisesandalthoughsummarycountsofdetectionsweresentto
shoreviasatellite,detaileddataneededtoconfirmthosedetectionswereonlyavailableoncethevehiclehad
beenrecovered.
As devices increase in acoustic bandwidth, so the pressures on data storage, data processing and power
requirementsbecomemoreacute.Thiscanlimitthepracticalitiesofusingthesesystemsforlongmissionson
small,lowpowervehicles.
7.3.3 AAMsensors
Anactiveacousticsensoractsbybroadcastingasoundwaveinthewaterandmeasuringthereturnedsignal
fromencounteredtargets.Theperformanceofthesonarsystemdependsonthedegreetowhichthebeamof
soundisfocussedontothetargetandhencethedirectivityofthearraygeneratingthesoundbeam.Itisalso
dependentonthelevelofbackgroundnoise.Activeacousticmonitoringequipmenttypicallyincludesfisheries
sonarandechosoundersthatoperatefrom~18kHzthroughto~500kHz.TargetsthatcanbedetectedwithAAM
thatareofinterestforthisreviewaremarinemammals,turtlesorfish.Theanimalisdetectedthroughtarget
reflectionratherthanvocalisation.Therefore,activeacousticmethodsarenotlimitedbypoorlight,visibilityor
mostweatherconditionsandanimaldetection isnotdependentonvocalisationorsurfacingbehaviour.The
rangeanddetectionabilitiesofactivesonarisfrequencydependent;attenuationofsoundamplitudeincreases
withincreasingfrequencyleadingtoshorterdetectionranges.Ontheotherhand,theresolutionincreasesas
frequencyincreases,thereforehigherfrequenciescandetectsmallertargetscomparedtolowerfrequencies.
InordertouseAAMtodetectmarinemammals,turtlesorfishtwofactorsarerequired:amethodofidentifying
theorganismintheacousticdataandanestimateofthesensitivityoftheinstrumentandeffectsofthephysical
environmentonsoundpropagation(seeVerfussetal.,2016forfurtherdetails).Identifyingtheorganisminthe
acoustic data can be done through either target strength (e.g. Bernasconi et al., 2013), target aggregation
features(Fernandes,2009),targetmovement(stationaryfishschoolsversusmovingwhales;(Selivanovskyand
Ezersky,1996;Bernasconietal.,2013)ormulti-frequencyresponses(KorneliussenandOna,2003).
Therearefourtypesofechosounders/sonarsusedinactiveacousticresearch:single-frequencyechosounders
(allowing for multiple transducers e.g. multi-frequency), multibeam echosounders, wideband/broadband
echosounders and sonars. A generic feature of all these echosounders/sonars is that the amount of echo
returnedfromatargetisafunctionofthefrequencyusedandthesize,shape,compositionandbehaviourof
thetarget(Horne,2000).
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7.3.3.1 Single-frequency/multifrequencyechosounder
Thesearethestandardfisheriesechosounders,typicallyavailablefrom18kHzto>500kHz,andnormallyhave
afocussednarrowbeamwidth.Thesizeofthetransducerisrelatedtothebeamwidth,withasmallerbeamwidth
resultingfromalargertransducerfaceatanyparticularfrequency(MacLennanandSimmonds,1992).Thefirst
echosoundersonlytransmittedasinglebeamofsound,wherethepositionofthetargetwithinthebeamwas
unknown.Modernechosoundersaresplit-beam,wherethetransducerhasfourquadrants,allowingthelocation
oftargetsinthreedimensions.Theuseofsingle-frequencyacousticsislimited,asitcannotdistinguishbetween
sizeandtargettypeandthereforehasreducedcapabilityforspeciesidentification.Multifrequencyacousticsare
required for species identification (KorneliussenandOna,2003), exceptwhere targets are simpleorexhibit
particularrepetitiveandrecognisablepatterns.
7.3.3.2 Multibeamechosounder
Amultibeamechosounderemitssoundwavesinafanshape.Thisgreatlyenhancestheareaandvolumeofthe
acousticwindow,typicallyprovidingahorizontalswathofinformationwithangularcoverageto150°,formed
fromhundredsofbeams.Theyimageasynopticsliceofthewatercolumnandcanensonifywholeaggregations
offish,aswellasdiscriminatingandtrackingthepositionofindividualtargetswithintheswath(Mayer,2006;
Colboetal.,2014).Mostrecently,dedicatedbiologicalsystems,whichcollectcalibratedacousticbackscatter
datathroughoutawholefanof500beamshavebeendeveloped(e.g.theMS70,Korneliussenetal.,2009).For
the detection ofmarinemammals and sharks,multibeam echosounders (vertically downward looking) and
multibeamimagingsonars(horizontallylooking)havealmostexclusivelybeenused.
7.3.3.3 Wideband/broadbandechosounder
Widebandechosoundersmakeuseofachirppulsetocoverawidefrequencybandinasingleping,thereby
providing echo strength measurements over a wide frequency range. Wideband equipment4 currently
represents the forefront of technical developments in fisheries acoustics. It has four advantages over
narrowbandsystems:(1)spectralinformationforspeciesidentification,(2)improvedtargetdetection,(3)more
stableestimateofsignaland(4)improvedtargetresolution.
7.3.3.4 Omnidirectionalsonar
Anomnidirectionalsonaremitssoundwavesinalldirectionsinahorizontalplane.Thisgreatlyenhancesthe
areaoftheacousticwindow,andallowsdetectionofmarinemammalsandfishfromdirectionsnotimmediately
undertheship.
4 www.simrad.com/ek80
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7.3.4 Animal-bornetags
Identityandmovementsof individualorganisms(marinemammals, turtlesandfish)canbemonitoredusing
transpondingacoustictags(e.g.,Voegelietal.,2001).Aminiatureelectronicpingercanbeplacedonananimal
andfollowedusinghydrophones(e.g.Wroblewskietal.,1994;Wroblewskietal.,2000).Tagstransmitatspecific
frequencies to provide a unique marker for each individual. Detections are generally made with bespoke
receivingequipmentfromthetagmanufacturer.Tagdetectionrangeandtransmissiondurationareproportional
totagsize.Obviously,akeyrequirementisthattheanimalistaggedinthefirstplace.
8 Evaluation of autonomous systems 8.1 Criteriaandmetrics
Thereareseveralexamplesinpublishedliteratureofcompleteautonomoussystems(seesection9.2),which
combineaplatform(orvehicle)withasensoranddatarelaysysteminordertoachieveaspecifictask.Forthe
evaluationwehaveseparatedplatforms,sensorsanddatarelaysystemstoenableacomparativeanalysisofthe
variousparts.However,themainemphasisoftheanalysisisassessingthesuitabilityoftheplatforms,sensors
and data relay systems (and any of their combinations) for mitigation and/or population monitoring or
monitoringofindividualanimals.
Manyoftheplatformsreviewedatleastclaimtobehighlyflexibleinhowtheyarefittedoutwithsensorsand
data relay systems, and data relay systems in particular must often be chosen for particular applications.
Therefore,aswellasdefiningwhatisrequiredintermsofperformance,wehavegatheredpracticalinformation
to inform which platform-sensor-transmission system combinations may be feasible in the future. General
informationontheplatformsandsensorswascollectedaswellasdetailssuchastechnicaldataandoperating
figures,expenses,mainpropertiesdeterminingmitigationmonitoringandmarineanimalsurveycapabilities,
operational information and data sources. To collect this information in a comparable manner, evaluation
criteriawerechosen.Thischapterprovidesexplanationsonhowthesecriteriaweredefinedandwhytheywere
chosen.Togenerate thecomparisonmatrices in section11.3, thesecriteriaweregrouped into the following
categories:general information,technicaldetails,costs,surveycapabilities,operationalaspectsandmanning
requirementsforboththeplatformsandsensors.Thesegeneralcategoriesfacilitatedtheorganisationofthe
hugeamountof informationgathered foreachsystem.Section11.1givesabulletpoint listoverviewof the
criteriadefinedinthischapteraswellasalinktothecorrespondingcomparisonmatrixtable.
Howaplatformandsensorareconfiguredanduseddependsverymuchonthepurposeofthecollecteddata.
Platform,sensoranddatarelayrequirementsformitigationmonitoringaswellasoperationalaspectsarequite
differenttothoseforpopulationmonitoringandthesedifferencesarehighlightedinthesectionsbelow.When
mitigating,thepriorityisgenerallyonthedeliveryofnearreal-timedataandhighdetectionefficiency,whereas
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forlongtermmonitoringreal-timedataareoftennotrequiredanddetectionefficiencyneednotbehigh,so
longasitisreasonablyquantified.
8.1.1 Collectionofsurveydata
Toachievethesurveydesign,datacollectionandanalysisrequirementsoutlinedinsection11.6,thefollowing
evaluationcriteriawereidentified,whicharediscussedbelow.
Themajorityoftheplatformcriteriaarelinkedtosurveydesign.Aswithmitigationmonitoring,platformmission
durationandanyfactorslimitingit,andspeedarekeycriteriathatmustbeassessedtoensurethataplanned
surveycanbecompletedinthedesiredtimeframe.Theminimumandmaximumverticalrangeaplatformcan
cover(heightabovesealevelforaerialvehiclesanddepthforunderwatervehicles)arealsoimportant.Foreach
consideredplatform,itisimportanttoknowwhetherasystemhastracksettingoptionstofollowapre-designed
track,andwhetherthetrack is implementedusingpre-programmedcoordinates, throughmanualpiloting,a
combinationofbothorsomeothermethod.Environmentallimitationsthatwouldpotentiallyaffectasystem
keeping to its track are also investigated. Some systems have the ability to self-correct if deviation from a
plannedtrackoccurs.Itisimportanttoconsiderwhetherasystemcanself-correctfortworeasons.Firstly,self-
correctionwillimprovetheabilityofasystemtoadheretoaplannedsurveydesignbut,secondly,self-correction
may increase systemnoise,whichmay negatively impact data collection. The ability of a system to remain
stationary(oratleastperformturnstostayinthesamelocation)isalsoconsidered.Station-keeping(asthis
behaviourisknown)isusefulfor(1)makingvehiclesactasfixedplatformsor(2)havingvehiclesinteractwith
other fixed platforms over a small spatial scale or (3) potentially collecting fine-scale animal behavioural
information.Thecapabilityofagivensystemtomakeautonomousdecisionsand,therefore,potentiallyhave
multiplesurveymodesisalsoinvestigated.Theabilitytoswitchbetweensurveymodeswouldallowasurveyto
be adapted either through interactions with other vehicles or due to some detections/measurements
encounteredduringthesurvey.Finally,whetherornotclocksynchronisationwithothersystemsispossibleis
also a useful consideration. Clock synchronisation may be required if the same detections needed to be
identifiedacrossmultipleplatforms(thisisalsorelevanttospecificsensors).
Unlikemitigationmonitoring,itisnotnecessarythattheprobabilityofdetectionofagivenanimalorgroupof
animalsishighduringsurveys.Instead,criteriarelatingtosurveyingcapabilitiesareprimarilyconcernedwith
thepotentialtoestimatetheprobabilityofdetection,usinganyofthemethodsdiscussedinsection11.6.2.The
firstcriterionforthesensors,however,providesageneralassessmentofwhatenvironmentalfactorswould
preventoptimalsensorperformance.Thetypeofdatacollectedandstoredbythesensorisalsoascertained.It
isimportanttoknowwhetherthesensorstoresrawdataorwhetherthereissomeon-boardprocessing.Ifonly
processeddataarestored,thenitismoredifficulttoassess,forexample,theproportionoffalsedetections.It
isalsocriticaltoknowwhethertheresolutionofthestoreddataissufficienttoidentifytheobservedspecies.In
order to assess the potential of a given sensor to store data that could be used to estimate detection
probabilities,itisnecessarytoascertainwhetherthedatacouldbeusedtoestimate(1)bearingstoobservations
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or(2)directrangeswhereabsolutelocationoftheanimalmaynotbeknownor(3)horizontalrangestoanimals
(wheresomebearingambiguitymayexist).InordertoestimatehorizontalrangeusingAUVsystems,itwould
benecessarytoknowanimaldepth.ForestimatinghorizontalrangefromUASsystems,itwouldbenecessary
tobeabletoestimatethehorizontaldistancefromthevehicle’stracklinetotheanimal.Alsolinkedtodetection
probabilityestimation,butonlyrelevantforPAMdata,arethefollowingcriteria:can(1)receivedlevelsofthe
detectedanimalsounds,and(2)ambientnoiselevelsbeestimatedfromthedata.Intheabsenceofanyother
information about acoustic detections (i.e. no range or bearing estimates), such information becomes
increasinglyuseful.Receivedlevelsandambientnoiselevels(inthefrequencybandofinterest)canbeusedto
calculatesignaltonoiseratios.Inaddition,CTDdatawillallowaccuratesoundspeedprofilestobeestimated,
whichcanbeusedinsoundpropagationmodellingtoestimatetransmissionloss.Transmissionlossisanother
acoustic measure that can be used in non-standard detection probability estimation methods. Clock
synchronisation isalsoarelevantcriteriontoassess;clocksynchronisationwillbekey if thesamedetection
neededtobeidentifiedacrossmultiplesensors.Theaccuracyofthesynchronisationwillalsodeterminewhether
multiplesystemsorsensorscanbeusedtocreateinstrumentarraysthatcanpotentiallyestimatebearingsto,
orlocalise,animals.
8.1.2 Mitigationmonitoring
To identify which system is suitable for which kind ofmitigationmonitoring requirements, the operational
aspects of a platform need to be evaluated in combination with sensor criteria. Some of the points to be
consideredarealsoimportantforpopulationsurveysbut,ashasbeendiscussedinsection7.1,therearealso
someverydifferentrequirementsbetweenpopulationsurveysandmitigationmonitoring,soseparatesetsof
evaluationcriteriawereconstructed.
Themostbasicrequirementsformitigationmonitoringarethatdataretrievedbythesensormustbeavailable
innear real-timeand thatdetectionefficiencyover themitigation zone shouldbe sufficientlyhigh that the
chanceofmissingananimalislow.Eachdetectionmethodologyhasitsadvantagesanddisadvantages,e.g.PAM
canonlydetectvocalisinganimalswhileanyopticalmethodreliesonanimalsnearorbreakingthroughthesea
surface.Therefore,thesystemclassofthesensors(e.g.AAMorPAM)isthemostimportantcriteriawhenit
comestowhichmarineanimalspeciesactuallycanbedetectedandinwhichsituations.Thishasbeenintensively
discussedinVerfussetal.(2016).Forevaluatingthedetectionefficiency,thecollecteddatatypeandrelated
dataprocessingisimportanttoknow.Thesystemclassaswellascertaintechnicaldetailsofthesensorwillalso
determinewhichspeciesorspeciesgroupsareabletobeclassified.Todeterminewhetherananimalisabout
toenterorispresentintheexclusionzone,localisationoftheanimalinrelationtothesoundsourceneedsto
bepossible,whichcanbedonebyevaluatinginformationonbearinganddirectorhorizontalrangeofanimal
totheplatform(withpossibleambiguityregardingbearinganduncertaintyontheanimal’sswimmingdepth
whereonlydirect range isestimable)orat least thedeterminationof therangebetweenanimalandsound
source.Furthermore,itislikelythattherewillbeassociateduncertaintywiththelocalisationorrangeestimate.
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Waysinwhichtominimisesuchmeasurementerrorneedtobeconsideredatthesurveyplanningstagetomake
surethatthemeasurementsaresuitablyreliableforuseinmitigationdecisions
Unless highly reliable automatic detectors are running on the autonomousplatform, itwill be necessary to
transferlargequantitiesofunprocesseddatainnearrealtime.Ifautomaticdetectorsareusedthenitwillstill
benecessary to transfer sufficientdata forhumanverification inorder toavoid falsealarms. Itmayalsobe
necessarytodemonstratethaton-boarddetectorsarerunningwithsufficientefficiencysuchthatnoanimals
aremissed.
Themaximummissiondurationaswellasthefactorslimitingitsdurationisonelimitingfactorthatdetermines
which monitoring requirements a system can fulfil. Also, theminimum, maximum and typical speed of a
platform is important forunderstanding thepotential tocoveramonitoringareaanddesign,aswellas the
environmentalfactorslimitingtheplatformperformance.
Thepayloadcapacity(seesection8.1.3)aswellasthetypeofinterfacedetermineswhatkindofsensorfitsonto
theplatform.Thatdeterminesmanyotherfactorsimportantformitigationmonitoring:Highnoiselevelscreated
bytheplatformorsensormayinterferewiththedetectionprobabilityorhaveaneffectonanimalbehaviour.
Thedatarelaysystemmayinfluenceifreal-timedetectionsareatallfeasiblebut,togetherwithitstransmission
range,alsodeterminesthemaximumrangeasystemcanoperateat.This,inturn,determinesthesizeofthe
monitoringareathatcanbecovered.Theverticalrangesaplatformcancoveralsodeterminesthedimensions
ofmonitoringareacoverage,whichforsomesystemshavetobecombinedwithcertainsensorpropertiesfor
meeting the requirementsneeded formitigationmonitoring.Forexample,ahigh flyingUASwillneed tobe
combinedwithahigh-resolutioncameratoretrievequalitativesufficientdataforanimaldetection,whilealow
flyingUAScanbecoupledwithasensoroflowerresolution.
8.1.3 Operationalaspects
Selectingthemostappropriateplatformandsensorsolelybasedonthecriteriaoutlinedinsections8.1.1and
8.1.2forconductingacertainkindofmarineanimalmonitoringisnotenoughtoproduceasufficientlyrunning
system.Therearefurtheroperationalcriteriathatarenecessarytoconsider.Forinstance,mostplatformswill
havesomemaximumpayloadspaceandpoweravailability,whichinformsastowhichsensorsmayfitintothose
payloadspacesandhowlongtheymightoperatefor.Therefore,definingthepayloadpower,capacityweight
andspaceforboth,platformandsensor,allowsanassessmentofwhichsensorsystemsaplatformcanfeasibly
carry.Bypayloadpowerwemeanthepowersupplynecessaryforagivensensorpackagetooperate–though
thisbalanceislikelytobeconfigurabletospecificmissionrequirements.Capacityweightandspaceidentifies
thenecessaryphysicaldimensionsandlimitationsofasensor/platformcombination.Thismayhavetwoaspects:
thespacewiththeplatformandthatpositionedoutside.Forsensors,thepayloadcapacitywasdividedintotwo
furtheraspects:theinternalpayloadcapacity,whichisthespacethatwouldberequirediftheelectronicsis
removed from the standard deployment package and incorporated into the vehicle payload bay, and the
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externalpayloadcapacity,whichisthespacerequiredforthestandardmanufacturespackage.Operationallyit
isnecessarytounderstandthedeploymentandrecoveryproceduresfortheplatformandsensor,aswellasthe
manningrequirementsfordeploymentandrecoveryandtheleveloftrainingneededinordertobeableto
deploy,recoverandinterpretdatafromthesensors.Withdeploymentandrecoveryproceduresitisimportant
tonoteany requirements in termsofnumberof crewmen formanualhandlingand theneed forany lifting
equipmentatsea.Specialisttrainingmayberequiredfordeployment/retrievaland,morepertinently,operation
ofthecraft–potentiallyremotelyfromanonshorebaseaswellaspersonnelrequirementsforinterpretationof
data. It is also important tounderstand the failsafemodes theplatform incorporatesand the transponder,
detect andavoid capacities theplatformhas.Wedefine the failsafemodeas thedefault functionalityof a
system that allows it to operate shouldmajor problems emerge. This is of particular importance regarding
navigational and positioning ability within a field of industry operations. Failsafe mode options therefore
encompass:Bearing,todefinetheheadingofthecraft.Location,todefineitsgeographicposition.Endpoint,
notingitsfinalwaypointforretrieval.Keeptrack,describingthecoursetobeadheredtoorstop.Worstcase
scenarioswillhave tobeconsideredandtheabilityofaplatformto“limphome”andavoidbeingaserious
hazard requiresclose scrutiny.Considerationof transponder,detectandavoidcapacities takes intoaccount
abilitiesofsensor/platformtosafelynavigateandrespondtoitssurroundings.Furthercriteriarequestedwere
fuel type for the platforms and their level of autonomy. Fuel type varies greatly between platforms, and
individualcraftmayhaveanumberofoptions.Thisishighlyrelevantwhenconsideringmissionduration,safety
andmeansofre-fuelling/recharging.Understandingthelevelofautonomyiskeytothesuccessofanygiven
ASV/AUV/UASifitistoproveworthoverandaboveexisting“manned”methods,particularlyintermsofability
tomeetwaypointsandremainundercontinuouscontrol.
8.1.4 Datarelay
Datatransferrequirespowerandoftenalsoinfrastructuresuchasmobilephonemastsorsatellites.Offshore,
wheremobilephoneinfrastructureisgenerallyunavailable,theprinciplemethodsofdatarelayarecoveredby
thefollowingdatarelaysystemclasses:
• Wirelessmodems.Thesevaryfromsomethingsimilartohomeorofficewifi(highdatarate,butshort
range) to systems which have ranges in the tens of km, but lower bandwidth. They are generally
operatedaspeertopeernetworksandincurnodatatransmissioncosts.
• Satellitesystems.Thesecomeinmanyvarietiesfromsmall,lowpower,butalsolowbandwidthdevices
up tophysically large, highpower andhighbandwidth systems. These relay systemsare relianton
satelliteinfrastructureandthereforeincursignificantusagecosts.
• Analogsystems.Thesearerelativesimpleandasomewhatdatedtechnology,butcanbeusedtorelay
audioandvideodataovershortranges.
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Thedatabandwidth isoneof themost fundamentalparametersgoverningthechoiceofdatarelaysystem.
Generally,bandwidthisquotedinbitspersecond,orbps.Mbpsmeansamillionbitspersecondandkbps1,000
bitspersecond.Notethatmostofustendtothinkaboutdataintermsof“bytes”,e.g.“Mymemorystickhas
an8GBytecapacity”.Thereare8bitstoabyteandthisneedstobetakenintoconsiderationwhenevaluating
thesedata.
For instance, a vehicle collecting long term monitoring data far from shore will have little choice but to
communicatethroughsatellitelinkanditmaybeacceptabletoreceiveeithernoreal-timedata,oronlyvery
basic summary informationon the systemstatus.Thesamevehicleandsensorcombination,whenused for
mitigation,mightoptforamuchhigherbandwidthradiocommunicationsystemtonearbyships,whichwould
allowmuchgreaterreal-timethroughputatnegligiblecost.
Theotherfundamentalparameterisrange.Transmittingdataovergreaterrangesrequiresmorepower.Allof
usareusedtoofficeandhomeWiFisystemswhichhaveexcellentdatathroughput,ofteninthehundredsof
Mbps.Goingintothenextroomcausedataratestodropanddisconnectionwilloccuratrangesinthelowtens
ofmetres.Datadelaywillgiveanunderstandingonhowlongoneneedstowaitfromsendingdatatoreceiving
it.Anothercriterionispowerconsumption:themoredatayouwanttosendandthegreaterthedistance,the
more power you’re going to need. For small, low power autonomous systems, power consumption can be
critical.
Clearly,thetransmitterunitneedstophysicallyfitwithintheautonomousplatform.Thisiswhytheoperational
sizeisimportant.Also,weightisanissuethatishoweverlessimportantforsurfaceandunderwatervehicles,
butessentialinformationifsystemsaretobeincorporatedintoautonomousaircraft.Platformrequirements
thatmightbenecessarytoinstalladatarelaysystemsalsoneedtobeconsideredaswellastheknowledgeon
anytrainingneedsnecessarytorunthesystems.
Fortheeconomicside,itisimportanttoinvestigatethepurchasecostoftransmittersandreceivers,aswellas
theiroperationalcost.Forsomesystemsusingfreespectrum,whereyoubuyyourownreceiver,operational
costsarezero(apartfrompower).Forsatellitebasedsystems,datacostscanbeconsiderable.Packagedsize
andweightofthesystemmayalsobeimportantforoverviewingupcomingcostsfortransportationifneeded.
Potential usage restrictions are also important to investigate as some radio based technologies may be
restrictedincertainareas/countries.
Environmental limitations to optimal system performance such as current or weather (wind, rain) were
requestedtounderstandinwhichsituationsperformancemaydrop.
8.1.5 Furtherinformationgathered
Generalinformationrequestedforthecomparisonmatrixincludethetypeofplatform,platformnameandthe
manufacturer and/or the designers. Next to the evaluation criteria as outlined above, further criteriawere
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specifiedthatareimportanttonoteforanymonitoringandprojectplans, influencingthetimeplanning,the
budgetorallowingforadditionaldatatoberetrievedwhileperforminganimalmonitoring.
The technology readiness level as defined in Table 10 is important to assesswhether or not the system is
available for commercial use or its current stage of development. It was also prudent to determinewhich
platformsthesensorshavealreadybeenintegratedinto,orwherethereareplanstointegratethem,asthiswill
impactontheleveloftestinganddevelopmentrequiredforuseinautonomousmonitoringabilitiesinthenear
future.
Operationalandpackagesizeandweightoftheplatformsareimportanttoknowintermsofspaceandcostof
shippingaswellasspaceneededonasurveyvessel.Thisinformationisalsorequestedforthesensorsanddata
relaysystemstounderstandrelatedshippingcostsandimplementationpotentialintoaplatform(asmentioned
above).ThehazardclassofplatformandsensorasdefinedinTable11areespeciallyimportantforshippingand
transport issues. Here the hazard class levels related to battery or fuel type of an evaluated system was
evaluated.Onehastobearinmindthatoilsorothermaterialsusedinthemanufacturingofsystemsmayalso
havehazardlevelsassociatedwiththem,butthesecouldnotbeconsideredinthisreview.
Thepresenceorabsenceofadditionalsensorsmayhelptogatheradditionaldatabutmay,ontheotherhand,
alsobeafactorlimitingtheperformanceoftheplatformorsensorusedforanimalmonitoring.Oneotherfactor
interestingtoknowisthedeploymenttypeoftheCTDsensorifpresent.Thissensorcanbemountedeitheras
afixedsensororprofilingsensoronanAUV/ASV.AfixedsensorcanonlymeasureCTDdataatthelocationof
thevehicle.AprofilingCTDsensorcanmeasureCTDprofileofthewatercolumn.Thisisofparticularlyimportant
forASV,whereCTDprofilesofthewatercolumncanonlyberetrievedwithaprofilingCTD.
Forprojectplanningpurposes,itisofcoursealsoimportanttoinvestigatethecostsrelatedtothepurchaseof
theequipment,oralternativelytherentalprice.Operationalcostsandmaintenancecostsarealsoconsidered
forplatformandsensor.
8.2 Evaluationresults
8.2.1 UASplatforms
There are a number of different types of UAS,whichwe divided into powered aircrafts,motorized gliders,
lighter-than-air aircrafts and kites. These aredescribed in detail in the introductorypart of this review (see
section7.2).Forthisreview,weincludedpoweredaircraftsandmotorizedgliderswithfixedwingandrotary
wing-typesystemswhichenableoperationsfromlandandvessels,andallowforenoughpayloadtobecarried
for conducting animal surveys offshore for long term monitoring and real-time detection. Lighter-than-air
aircraftconsideredhereincludetetheredandremotelyoperatedballoonsandblimps,respectively.Tethered
balloonsarealsooccasionallycalledblimps,but,inthecontextofthisreview,thesewillbekeptseparate.Only
kitesthatcanberemotelyoperatedwereincludedinthisreview.Forthesensors,thosenotexceeding2kgand
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able to provide an image resolution that allows for the detectionof animalswith at least 1m lengthwere
considered.
Basedontherecommendationsof(Koskietal.,2009a),wedefinedasetofcharacteristicsrelevanttowildlife
monitoringoffshoretounderstandwhichUAStoincludeintothisreview.Flightdurationandcommunication
rangewiththegroundstationshouldbesufficientlylongtoallowfordeploymentsfromlandandfromvessels.
Wedefinedthatdetectionofobjectsassmallasonemeterinlengthshouldbepossible,whichincludessharks,
turtles, fish shoals, and smaller marine mammals. This will allow UAS to be versatile in any environment,
regardless of the species present in any region. Additionally, to include both timely decision making and
populationsurveys,weconsideredaminimumhorizontaloperationalrangeof500mfromvesselsand200km
from land, considering the JNCC definition of high risk zone in marine mammal monitoring for industrial
operationsoffshore.ThelandrangeforUASoperationsdefinedhereallowsforfuturecomparisonstudiesto
determinewhetherthesesystemscanperformsimilarlytomannedaerialsurveys.Thisalsorequiresthatthe
flighttimeshouldbeatleast30minutesiftheUASisoperatedfromavessel,and4hoursifoperatedfromland.
Duetothe largeamountofUAS,wefocusedthecomparisonofUASsystemstothosethatcanbedeployed
rapidlyandwiththeneedofnomorethan3to4personstooperate.Theseareallintheclassofsmalltomedium
sizeUASandwithamaximumtake-offweight(MTOW)oflessthan80kg.Weconsidercharacteristicsthatare
relevantforanimaldetectionanddeploymentswithbothspaceandtimeconstraints.
The selection of platforms included different range, payload and deployment capabilities,whichmay serve
differentpurposesdependingonthestudiesofinterest.Weincludedsystemsthatcanbeappliedforpopulation
andmitigationmonitoringaswellasmonitoringofindividualanimalsfortheapplicationintheO&Gindustry.
8.2.1.1 Poweredaircrafts
PoweredplatformshavebeenmostwidelytestedintheUASfield.Thecapabilitiesofthesesystemsallowthem
tobeconsideredasalternativemethodsforworkdevelopedbothatseaandonland.Dependingonthestudy
design, there is currently a wide variety of platforms available at the moment that can overcome many
limitations that human observers and other aerial platforms (e.g. digital surveys using manned aircraft)
experience. The versatility of these systemshighlights thepotential applicationsof this equipmentboth for
researchandmanagement.
Inmostcommerciallyavailablesystems,themanufacturerisabletoprovideafullyintegratedpackagethatcan
satisfytheneedsofoperatorsandclients.However,listedsensorsystemsinthecomparisonmatrix(Appendix
11.3.8)havesimilarcharacteristics,especiallyforthermalIRandRGB(RedGreenBlue)cameras,whichmaybe
applicabletoplatformsthathavegimbalcapacity.
NeitherofthelargestUASmanufacturerslistedinthisreport,suchasInsituandArcturusUAV,providedaprice
ontheirsystemuponrequest.Specificrequestsforpriceestimatesorpricerangeswereunsuccessful.Inother
occasions,quotesweregivenonaspecificsystemwithdetailsregardingthequote,suchasprice,beingundera
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strictNon-DisclosureAgreement. Themilitarymarket clearly is thebiggestmarket for these systems today,
thereforekeepingthepriceofsuchsystemsasecretmaybebeneficialwithregardtopricedifferentiationamong
differentcustomers.However,accordingto(Thammetal.,2013),thesesystemstendtovaryinpricebetween
US$27,360toUS$76,577.Systemsbasedonmilitaryapplications,suchastheScanEagleforinstance,mayreach
highercostsduetothedemandformorecomplexlaunchingandlandingequipment,andcontractswithcertified
operators.
UASplatforms are generally very versatile.Dependingon the studydesign andmain goals of a survey, it is
possible touseawidearrayof systemsthatcanprovidedifferentdata types.Twoof themainaspects that
shouldbeconsideredareflightdurationandrangefromthegroundstationandthemainpilot.Thispointalone
willhelpdefine theUAS that suitsa study in thebestwaypossibleand fromthere, restrict theselectionof
sensorsthatareapplicable.UsingIridiumsatellite,mostsystemsconsideredcanbepilotedglobally.However,
forlivedatastreaming,thepayloadcanbelimited(unlesstheoperatorhasaccesstoasatellitedatalink,orcell
phonecoverage).Thechoiceofdatalink,andhencerangevariesdependingonlocation,asregulationsregarding
frequencybandsandtransmitterpowervariesfromcountrytocountry.
MostUASareabletoconducttransect-basedsurveys.Bothfixed-wingandVTOLplatformsareabletoconduct
waypointflights(transects)andfocalfollows.However,fixed-wingplatformsmayhavedifficultiesinhovering
andrelyongimbalsensorstolockthecameraonthepointorobjectofinterestwhileloiteringaroundthetarget.
The choicebetweendata in the formofphotoor videowill alsobedependenton the study’sgoal and the
resolutionthatisrequired.However,aswithanyothervisualmethodofobservation,UASareonlycapableof
detectinganimalsthatarevisibleatthesurfaceorinthetoplayersofwater,whichmayprovetobeefficientfor
studiesconcerninganimalsthatspendlongperiodsoftimeatthesurface.Foranimalswithlongdiveduration
(e.g.beakedwhales),theflighttimerequiredismuchgreaterandstudiesforthosespecieswouldprobablyrely
onacombinationofsurveymethodstoaiddetection.AsUASevolve,sodotheircarryingcapacity(payload),
whichcould include thedeploymentofacoustic technologyduring flightwhileat thesame timeconducting
visualsurveys.Thecombinationandcomparisonofdifferentsystemsisatopicwhichhasbeenunderdiscussion,
asallcurrentmethodshaveadvantagesandlimitations,andmayprovidemoreaccurateestimatesofanimal
positioning,size,andmoredetaileddescriptionsofanimalbehaviourinanimalstudiesatsea.
TheonlytwofullelectricpoweredaircraftproposedinthisreviewaretheTrimbleUX5andtheBramorC4EYE.
These systemsbenefit fromthe lightweight,easeofuseand the lowoperational cost, requiringonly1 to2
personsforoperating.Theybothcanlandinconfinedareasandarewellsuitedforshortmissionsclosetothe
shoreline.TheBramorC4EYEhasthelongestflightduration(uptothreehours)andhasalsothecapacityfor
streaminglivedatabacktotheoperator,whilethedatahastobedownloadedfromtheTrimbleUX5afterthe
missionhasended.Fourfuel-poweredaircraftsareproposed,wheretheArcturusJump-20istheonlyaircraft
withVTOLcapabilities.Thisisaclearbenefitwhenoperatingfromshipsorinareaswithoutasuitablelanding
strip,andrequiresnoextrainfrastructureotherthanaflatsurface,suchasahelipad.TheInsituScaneagleand
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ThalesFulmarbothhavecapabilitiesforwireornetlandingonshiporland.ThePenguinBistheonlyaircraft
thatrequireanairfieldtoland.Withdurationof24hoursithasalongrange,andshouldbeconsideredincases
whereonecanoperatefromanairfield.TheThalesFulmaristheonlyproposedaircraftwhichcanlandonsea
andiswellsuitedfortheharshenvironmentofmarineoperations.
Withmore than800,000proven flighthours5 the Insitu ScanEagle is in a classof its ownwhen it comes to
mediumsizedUAS. Ithas thecapabilities tooperate fromshipsandruggedterrainandhaspreviouslybeen
evaluatedforsurveyingofmarinemammalsandmarinefauna.Withamissiondurationof24+hoursandits
capacityforstreaminglivedatabacktothegroundstationitcanbeusedforcontinuouslymonitoringoflarge
areas.However,apossiblelimitationforallcurrentlyavailablemediumsizeUASisthelackofanintegratedde-
icingsystem.
Inpoweredaircraft,thedatacanbestoredintheformofSDcardsandthendownloadedtoalocalcomputer
foranalysis.Imagesandvideomaybeeditedaposterioriandprocessedusinganalysissoftware(Irelandetal.,
2015) and human visual inspection. Live streamingmay also be possible,with storage and further analyses
occurringatthereceivingend.TheonlyproposedsystemwithoutthiscapabilityistheTrimbleUX5.
8.2.1.2 Gliders
Gliderplatformstendtobe lessversatile thanpoweredaircraft,giventhattheyaredesignedtoharvest the
energypresentinrisingair.Hence,theydonotprioritizemaintainingaltitudeorflyinginstraighttrajectories.
Further,thecapabilitiesarethesameasfixedwingpoweredaircraft.Thesesystemswerenotfoundsuitablefor
marineanimalstudiesduetothesinkzoneabovetheocean.Hence,noglidersystemswereconsideredinthis
evaluation.
8.2.1.3 Kites
Kitesaregenerallylimitedtowindspeedsunder6ms-1,whichmaylimitthelocationsinwhichtheycanoperate
(Thamm et al., 2013). Adding to the dependency in environmental conditions, this equipment is not often
stabilized, resulting indifficulties inhoveringoveranobjector locationof interest.Thenumberofavailable
platformsislimited.Often,parafoilkitesmaybeusedandthepartscollectedtocreateasystemusedforsurveys.
Thereareonlineplatformsavailablewheretheconstructionof thesesystems isadvisedandguided,making
theseplatformshighlypersonalised.Thesesystemsarenotwellsuitedformarineanimalstudiesduetotheir
wind limitations.Thereare fewcommerciallyavailable systemsor systems thatarebecomingavailableasa
completepackage,andwehavethereforeselectedthesinglekiteplatformthatisabletoconductsurveysof
marineanimalsinflexibleweatherconditions.
5 www.insitu.com
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BasedontheestimatesbyThammetal., (2013), it isestimatedthatthepricerangesforkitesystemsrange
between~US$20,000and~US$44,000.
Kitesaresimilartofixedwingpoweredaircraft,exceptthatthewingsarenotrigid.Theadvantagesare
• Easiertocarryinabackpackthancomparedtoafixedwingwithsamepayloadcapacity.
• Neednorunwayfortakeoffsandlandingsandcanbeoperatedfromaship.
Thedisadvantageistheextremely lowweathermaximaandlimitedcontrolduringflight,whichmakesthem
unusableforoffshoreapplicationssuchasmonitoringofmarinemammals.
Kitesaremoreweather-dependentthantheothersystemsabove.Theyareparticularlysensitivetowindand
rain,asthesecanaffectthestabilityoftheequipmentleadtodifficultiesinmaintainingaspecificsurveytrack.
These UAS may conduct transect-based surveys for animal population studies, but struggle in maintaining
hoveringpositiontoperformmorefocalassessments.Windspeedmayhaveastrongeffectonkitestabilityand
alsoontheamountofoverlapthatmayresultfromasurvey,andshouldthenbetakenintoconsiderationduring
flightpreparations.AswithothertypesofUAS,itispossibletochoosebetweendataintheformofphotoor
video, and as any other visual detection methods kite systems maintain the same limitations concerning
detectabilityofanimals.Thepayloadcapacityofthesesystemswilldependonthesizeofthesystem,thoughit
hasyettobetestedatsea.
Kitesystemshaveaflighttimethatcangouptoafewhours,thoughitseemstorequireslightlylesstraining
timeforpilotsandoperators(Thammetal.,2013).Similartopoweredaircraft,thesizeofthekitewillprovide
an indicationofthepayloadthat itcancarry,thoughsmallerkitesmayprovetobemoreunstable instrong
wingsthanlargerones.Theseplatformsrequirerunwaysfortake-offandlanding,thusmakingthemdifficultto
operatefromvesselsinareasdistantfromthecoast.Seesection7.2.3fordetails.
Forthesesystems,thedatacanbestoredinthesameformatasforthepreviousaircraft.Imagesandvideomay
bealsoeditedaposterioriandprocessedusingbothanalysissoftware(Irelandetal.,2015)andhumanvisual
inspection.
8.2.1.4 Lighter-than-airaircraftsystems
Similartokites,thesesystemsmaybedependentonweatherconditionstoprovideadequateimagesofthearea
surveyed.However,newsystemsarebeingdevelopedthatmayovercomethislimitation.Wethereforeinclude
theseplatformsinthelistofmethodsformarineanimalmonitoring.
TheOceanEye is suppliedwith a triple sensor unit (EO/IR/AIS) and is capable of real-time, night video, and
imagery.TheelevatedAISreceiverallowsforincreasedshipdetectionrange,whichisofvaluewhenconducting
studiesofanimalswithastrongperceptionofvesselpresence.Thecompactcameraisattachedtotheballoon,
improvingthestabilityofthesensorunitandqualityofthefootage.TheHelikiteplatformisabletocarryGyro-
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stabilisedandstandardPTZ (pan-tilt-zoom)andmodularvideocameras,whichcanberemotelyoperated to
provideawiderview,orstoreinformationon-board.Bothcanbeattachedinanypositionofthetetheringline,
allowingforanexchangeofbatterieswithoutlandingtheballoon.
NopriceinformationwasrequestedfromthemanufacturerfortheOceanEyesystem.Basedonthepriceranges
oftheHelikiteweestimatethatthepricerangesforballoonplatformsrangebetweenUS$147andUS$7,362.
TheseUASmaybelessweather-dependentthankites,astheyaregenerallytetheredtoavessel.Theyarealso
sensitivetowindandrain,butareabletomaintainacertaindistancefromthesurveytrackorthestudyanimals
duetothepresenceofacablethatstabilizes(uptoalimit)winddisturbance.TheseUASmayconducttransect-
basedsurveysforanimalpopulationstudies,andcanbecontrolledtomaintainahoveringpositionandperform
morefocalassessments.However,thisequipmentisdependentonthepresenceofthesupportvessel,which
mayaffectanimalpresenceandthuscreatebiasedstudies.
Tetheredballoonscan,atstandardtemperatureandpressure,carryaboutafewkilogramsofpayloaddepending
mainlyontheirhelium(orhydrogen)capacity.However,thoughnotgenerallyacknowledged,theweightofthe
required length of linemust be taken into account tomake accurate assumptions about the actual sensor
capacityoftheballoonorblimp.Additionally,weatherconditions,particularlyheatandhumiditymayaffectthe
lift of the balloon,which in conditions of rain, snow, andmist can get extraweight from the accumulated
precipitationon topof theplatform.This typeofplatform isgenerally sensitive towindconditions.Helikite
managedtoovercomethislimitationbyincorporatingatetheredheliumballoonwithakitewing.Furthermore,
giventhattheyaretethered,thelengthofthelinecanbeadjustedtocoverdifferentheightsanddistancesfrom
theattachedvessel.
Inlighter-than-airaircraft,thedatacanbestoredon-boardtheaircraftanddownloadedtoalocalcomputerfor
analysisafterthesurveyhasbeencompleted,ordirectlytransmittedtoagroundstationpresenton-boardthe
supportingvessel.Throughreal-timetransmissionofvideoimages,itisalsopossibletotriggeracameratoselect
photosofthetargetobjects,ratherthanstoringthewholevideo(Hodgson,2007).
8.2.2 UAS-sensors
Fromthediscussionabove,welimitthesensorsurveytostabilizedcamerasystems(i.e.gimbalsystems)capable
ofacquiringHDvideo(orbetter).Mostgimbalsystemsonthemarketcomepre-assembledwithsensors,and
themanufacturers gives the customer a choice froma selection of different sensors to suit the customer’s
application and needs. As the systems listed below are expensive, high performance gimbals, most
manufacturersdeliverthesamecamerasensors,orcameraswithsimilarcharacteristics.Hencethechoiceof
gimbalsystemoftencomesdowntowhattheUASsystemprovidercandeliverasstandardoptions.
Thebiggestgimbals(likeCloudcapTASE400)areonlyneededforexotic(combinationsof)cameras,suchasthe
VIS+LWIR+SWIR,orforheavyopticalzoom(whichweprobablydonotneedformarineanimals,aswemost
oftenwishtoflybelow120metersduetoaviationrequirementsforVLOSoperations).
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• CloudcapTechnology:Tase400&Tase310
• UAVVision:CM202&CM100.TheCM100Gimbalisthesmallestandlightestinthestudy,henceitis
possibletofitmostUASsystems
• DST:OTUSU135HIGHDEF&OTUS-L205HIGHDEF.
Inaddition,therearespecializedgimbalsfromInsituandC-ASTRALwhicharecompatiblewiththeScanEagle
andBramorsystems,respectively.
Mostplatformsconsideredinthisevaluationareavailableasaset.Itispossibletoexchangethesensorsfora
singleplatformbutitwilldependontheavailabilityofcompatiblesensorsdevelopedbythesamemanufacturer.
ItmayalsobepossibletodeploysensorssuchasDSLRcamerasasgiveninthestudyexamplesofTable7,butit
hastobetakenintoconsiderationthepayloadcapacityoftheplatformanddatatransmissioncompatibility.It
isthereforerecommendedtofollowmanufacturers’adviceonthepossibilityofusingalternativesensors.
Giventhatforaturn-key(fullyintegrated/assembledandready-to-go)UASsystemthesensorsandstabilizing
gimbalarepurchasedasapackage,andthe informationpublicallyavailable is limited, itwasnotpossibleto
obtainpricerangesforsinglesensors.
Imaging sensors suchashigh resolution still or video camera, operating in the visible spectrumor thenon-
thermalregionoftheinfraredspectrumarethemostrelevantsensorsformarineanimalstudies.Forvideo,itis
importantthatthecameraisgyrostabilised.Thiswillimprovetheefficiencyofoperatoranalysingthedataand
willalsoreducetherequiredbitratewhendoingvideoencoding/compression.Itisalsoimportantthattheimage
framesaregeocoded,sothatthelocationandsizeofaspottedanimalcaneasilybedeterminedfromtheimage.
Afuturesystemcombininggyrostabilisedgeocodedvideowithcorrespondinghighresolution,geocodedand
overlappingstillimagesseemstobeaneffectivesystemfornear-real-timedetectionandidentificationofmarine
animalsfromUASdata.Insuchasystem,onecouldimaginetheoperatoridentifyingpotentialsightingsinthe
video stream by point and click, and using the corresponding high resolution still image from this area for
verifyingandidentification.
UASsensorsarepositionedbelowthesurveyplatformeitherprovidingastraightorangledview.Thechoiceof
video(real-timeoron-boardstored)orstillimage,andgroundresolutionwillrelyontheobjectivesofthestudy,
butthechoiceofthetypeofsensorhastobetakenwithconsiderationforthepayloadcapacityoftheUASto
beused.SomemanufacturerssuchasARCTURUSUAVandTRIMBLEprovideonlycompletepackages,inwhich
case one can only operate using the sensors provided in the package or produced specifically by that
manufacturer.
AnintegratedsystemforanalysingthedatafromtheUASisrequired.Somedataprocessingcapabilities,such
asobjecttrackingormotiondetection,canbeincludedinthesensororgimbal.Thishasmostlybeenusedin
systemsdesignedforsurveillancepurposes,andmaybeusefulformarineanimalmonitoring,especiallyforfocal
followsandmitigationpurposes.Thecommunicationsystem,usedfortransferringdatafromthesensorandto
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thegroundisoftenanintegratedpartoftheUASandalsousedforthemainorsecondarytelemetrylinkfor
monitoring and control of the aircraft. Depending on the aim of the study, it is possible to createmosaics
(overlapping the photos and creating a chart), analyse each photo or video individually, or for real-time
detection,usingasystemsuchastheonedevelopedbyIrelandetal.,(2015).Giventhatthedatamaybestored
atalltimes,therewillbenodatalossesunlessthesensorisnotproperlyconnectedtotheaircraftorthereis
equipmentfailure.Analysingeachphotoorvideoindividuallycanbequitetimeconsuming,andwetherefore
urgethedevelopmentandpublicavailabilityofdetectionalgorithmsthatcanbeusedtotriagethephotos.
8.2.3 AUV/ASVplatforms
8.2.3.1 Propellerdrivenunderwatercraftandpoweredsurfacecraft
As ismade apparent in thematrix given in section 11.4, the range of potential combinations is extensive.
Dominantfactorsinshapingoptionsarethepayloadsizeandpowerbudget.Missiondurationandwhetherthe
data istransferredorrecordedareearlyquestions indefiningtheexactspecificationsforanygivenmission.
Whilsta“lowcost,off theshelf”approach is slowlyemerging (more rapidly inAUVthanASV), inalmostall
instancesspecificrequirementscanbetailoredtoaparticularsurvey.Onthepracticalside,self-noiseisakey
issue to be addresses by all crafts if applied to PAM or AAM. The craft itself may be very quiet but the
deployment/attachmentmethodofacousticsensorisallimportanthere.Self-propelledAUVhaveaparticular
challengeinthis.Alsotonote,thatcameras,bothHDandIRcanbeattachedtoASVwhich,thoughitoffersa
very low observation platform, may have an application in the detection of surface animals, e.g. marine
mammals,turtles,seabirds.
The purchase price range of the above AUV and ASV for existing models with integrated sensor packages
includedspansfromtensofthousandsofpoundsforsmallerAUVtoseveralhundredthousands,evenmillions,
ofpoundsforhigherspecificationinstruments.Anothermodelisrental.Thisisusuallybythedayandcostshere
areestimatedtobelowhundredsofpoundsperdayforlowerspecificationAUVuptoseveralthousandperday
for more. Pricing, especially for rental, may be subject to deployment length and exact requirements.
Manufacturers typically have been open to integrating new sensors and tailoring specifications for a given
project,butthisincursresearchanddevelopment(R&D)costs–whichcanbesignificant.Itisworthnotingthat
asthistechnologyemergesoutofR&Dlaboratoriestoseekanentranceintothecommercialmarket,anexact
“pricepoint”oftheproductisoftenstilltoform.Additionally,associatedcostssuchastransportationandservice
arrangementsshouldbekeptinmind.
AllaboveAUV/ASVaredesignedtofollowatracklinebywaypointsetting.Variationemergeswithoperator
capabilitytoaltercourse.AllASVclaimthisability,butquestionsariseonresponsiveness.Iridium,freewave,wifi
andacousticmodemcommandandcontrolenablebuoyancygliderstobedirectedinreal-time.Asaresultthey
are targeted platforms, and can be used to investigate ambiguous observations, or to change survey areas
rapidlyifrequired.Station-keepingabilitiesvarywidely.ItisclaimedbysomeAUVbutthereisuncertaintyhere
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as to exact capabilities. Powered ASV have the potential to stay on point more dynamically, but this has
implications on power/fuel. Themethod of staying in position by close circling hasmore flexibility but, by
definition,provideslesspositioningprecision.MoreclearlyknownonmobilityisthatallAUV/ASVmovevery
slowly.Except,thatis,forthepoweredboat-styleRIBs,someofwhicharecapableofveryhighspeedsindeed.
AllAUV/ASVaretheoreticallycapableofconductingPAMandAAMsurveys,butprojectspecificsmustbeborne
inmind.Datatransfercapabilitieswilldeterminewhetherthisisforpurposesofloggingdataonly,near-real-
timeorgenuinereal-time(i.e.formitigationmonitoring).Additionally,self-noisemaybeanissueinsome(such
aspropellerdrivenAUV).
WhenconsideringoperationalpracticalitiesofASV/AUV,aguidingthemeissafety.Anyriskof,forinstance,AUV
/ASVbreakdownaheadofaseismicvesselandassociatedequipmentwouldhavemassiveimplicationsforthe
wholeoperation.Beforeusageinindustrialfieldofoperations,extensivetrialsandproventrackrecordwillbe
requiredforallcraftstoprovideassuranceontechnicalreliability.Highconfidenceinabilitytocompletemission
durationandgenuineindependencearealsohighpriorityattributes.In-fieldsupport,suchasbyachasevessel,
may be available but not guaranteed and exact requirements will vary by application. Any ASV/AUVmust
demonstrate full capability in these areas. Logistical factors must be taken into account, such as ease of
deployment/retrieval.Safetyconcernsareparamount,butalsoimplicationsoncost(suchasfreighttooverseas
locations).
Regardingremoteoperation,technicalandsafetyassuranceswillberequiredonthedependabilityofthelink
andresponsiveabilityofremoteoperation.Thecontrol,handlingandmaintenanceofASVandAUVmayrequire
specialistknowledge,providedeitherbyadditionalAUV/ASVexpertsheadingoffshoreorbytrainingofexisting
crew.Aclearcodeofpracticeislikelytoberequiredanddisseminatedinadvancetopotentialmarine/airspace
users,includingthirdparties,inthearea.
8.2.3.2 Autonomousunderwaterbuoyancygliders
Thereare,toourknowledge,currentlyfourcommerciallyavailableelectricbuoyancygliders:theSlocumelectric
(Webb et al., 2001)manufactured by TeledyneWebb, the Seaglider (Eriksen et al., 2001)manufactured by
Kongsberg,theCoastalglider(ImlachandMahr,2012)manufacturedbyExocetus,andtheSeaExplorer(ACSA,
2013)manufacturedbyACSA.Inaddition,thereareseveralunderwaterglidersthatareinuseordevelopment,
includingSpray(Shermanetal.,2001),Deepglider(OsseandEriksen,2007),Tsukuyomi(Asakawaetal.,2011)
andtheLiberdadeXray/Zray(D'Spainetal.,2011;D'Spain,2009).TheCoastalgliderisdesignedforuseinthe
littoral zone (it is self-ballasting fromessentially fresh to fulloceanwater),witha fastermaximumspeed (2
knots)(ImlachandMahr,2012).TheDeepgliderisdesignedtoglidetodepthsof6,000m(OsseandEriksen,
2007),theSeaExplorerisdesignedtobefaster(1knot)andpoweredbyrechargeablebatteries,theTsukuyomi
isbeingdesignedforlongdurationasavirtualmooring(Asakawaetal.,2011)andtheLiberdadeZray/Xrayis
designedforlongdistance,longdurationcarryinglargeandhigh-data-ratepayloads(D'Spain,2009).Currently
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thereisonemanufacturerofathermalglider(TeledyneWebb),andfourhybridvehiclescurrentlyadvertisedor
in-use,theSlocumhybrid(Jonesetal.,2011a)manufacturedbyTeledyneWebb(note,allnewSlocumG2gliders
have the hybrid capability), the eFòlaga (Alvarez et al., 2009)manufactured by Graal-tech, the SeaExplorer
(Rochet,2015)manufacturedbyACSA,andtheexperimentalSeaBird(ArakiandIshii,2007).Wehavelimited
thedescriptionsbelowtothoseglidersthatarecurrently(orhavebeen)commerciallyavailable.
Thefirstocean-provenelectricalbuoyancygliderswereSlocum,SeagliderandSpraywithsimilarfeatures(Table
1).Themorerecentdesignstypicallyfocusonparticularimprovements/applications.
Table1.Commondesignfeaturesofelectricbuoyancygliders(fromDavisetal.,2002andWoodandMierzwa,2013).
Feature Design
Endurance
Endurance ofmonths to year, at slow horizontal speed (~0.3m/s) to
minimisedragandverticalvelocitiesof~0.1m/s
Ballast system that uses a hydraulic pump to move oil between an
externalbladderandaninternalreservoir.
PortabilityandversatilityA similar size, shapeandweight (~2m length,~60kg inweight), that
permitdeploymentandrecoveryby1to3peopleandasmallrib
EconomyinuseConstruction costs in theorderof $50,000 to$100,000 and refuelling
costs$3,000to$15,000perdeployment.
Real-timecontrolanddatarelayGPSnavigation,on-boardPC-levelinternaldataprocessing,andanability
toreceivecommandsandtransmitdatatimely.
Inmost commerciallyavailable systems, themanufacturer is able toprovidea fully integratedpackage that
integratesapassiveoractiveacousticsystemintotheunderwaterglider.Selectionofaspecificsensormaybe
limitedbythevehiclepayloadsize (which issmallbutcomparablebetweenthegliders), thecommunication
methodemployedbytheglider,andsuitabilitytoaplatformundertakingasaw-toothprofile.
Thepricerangeforallunderwaterbuoyancyglidersspans~$50,000to$200,000forexistingglidersandpre-
integratedsensorpackages.Themanufacturershavetypicallybeenopentointegratingnewsensors,although
thesehaveincurredresearchanddevelopmentcosts.Newlithiumbatteries,sensorcalibrationandre-ballasting
for amission are in the order of £5,000 to £15,000. Piloting telemetry costs are ~£2 / day, although data
transmissioncostscanbesignificantontopofthis.
Buoyancy gliders typically undulate in the upper 1,000m of thewater column, therebymaking subsurface
measurements.Traditionalvisualsurveymethodsinvolvingaircraftorshipsareexpensiveandcanbeineffective
atdetectingsmallaggregationsofanimalsthatspendsignificantperiodsoftimesubmergedandoutofviewof
theseasurface.Visualmethodsarealsonaturallylimitedbyfactorsthataffectvisibility,suchasroughseas,fog,
rain,snow,anddarkness(Baumgartneretal.,2013).Thebuoyancyglidercanthereforeundertakemarineanimal
surveysinconditionsnotsuitableforvisualmethods.
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Differentmakesofbuoyancyglidershavedifferentmissiondurationsbut typically, oncedeployed, theyare
operational 24 hours a day. Assuming themethod of detection does not require light, thenmarine animal
observationsareundertakencontinuallythroughoutthemissionduration(6hoursto>1year).
Iridium,freewave,wifiandacousticmodemcommandandcontrolenablebuoyancygliderstobedirectedin
real-time.Asaresult,theyaretargetedplatforms,andcanbeusedtoinvestigateambiguousobservations,or
tochangesurveyareasrapidlyifrequired.
Ships and aircraft are operationally expensive to run.With a purchase cost of ~£100,000 per platform and
running costs of ~£10,000 to £20,000 per mission (battery, piloting communication and CTD calibration),
buoyancyglidersareascalableeconomyforasurvey.Newmethodsforcontrollingmultiplevehiclesenablea
multi-vehicleapproachtosurveysthatmaycompensatefortheslow-movingnatureoftheplatform(Greene,
2014).
Buoyancyglidersareacousticallyquietplatforms,sincetheydonothavethrustersanduseinternalactuators,
makingthemidealplatformsforacousticmonitoring(bothAAMandPAM).However,acousticplatformnoise
(self-noise)generationdoesoccurs(predictably)whenthebuoyancychangeandtrimadjustmentsmechanisms
areactivated,causingpotentialperiodsofmaskingduringPAM(Ferguson,2010).
Buoyancyglidersaredesignedforslowspeedstoreducedragandextendendurance.Typicalspeedsare~0.3
m/s,with the addition of thrusters this can increase speeds to ~1m/s. The ability of a glider to perform a
transect,ortopassthroughawaypointisdependentontheoceancurrentsandtheirvariabilitywithinthesurvey
region.Theoperatorcanspecifyhowclosetoawaypointtheplatformshouldachievebeforeitisacceptedto
havereacheditsgoal,howevertheroutetothatwaypointcanbeconvolutedandstraight-linetransectsmay
thereforenotbeachievableindynamicoceanenvironments.
There is limited information on the reliability of gliders, due to their relative newness. Brito et al., (2014)
investigated the reliability of gliders using 205missionsmade by 56 gliders undertaken by the EUGROOM
(GlidersforResearch,OceanObservationandManagement6)programme.Theprobabilityofadeep(1,000m)
underwaterglider,independentofmanufacturer,survivinga90-daymissionwithoutaprematuremissionend
is approximately 0.5. The probability of a shallow underwater glider surviving a 30-day mission without a
prematuremissionendis0.59.
8.2.3.3 Self-poweredsurfacevehiclesanddriftingsensorpackages
In recent years, several self-powered surface vehicles havebecomeavailable. These vehiclesmostly extract
energyfromwavemotionandconvertitintoforwardmotion.Somevehiclessupplementthiswithsolarorwind
powered electricity generation which is then used to drive a propeller. By extracting energy from their
6 http://www.groom-fp7.eu/doku.php
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environment,thevehiclescanoftenstayatseaalmostindefinitely.Electricalpowerforpayloadsissuppliedby
batterieschargedbysolarpanels.Control issimilar forallvehicles.GPSandon-boardsoftwarenavigatethe
vehicle between waypoints. Vehicle position and be monitored via satellite and new instructions can be
uploadedatanytime.
Self-poweredanddriftingsurfacevehiclesarerelativelyquiet,havingnopropellernoise.Thismakesthemhighly
suitable forPAMaswell asAAM.Someresearchershavealsoattachedcameras to thesevehiclesandhave
detected birds andmarinemammals. Purchase prices are in the $100,000’s for theWavegliders while the
smallestoftheMOSTAutonautvehiclescostslessthan$100,000.
Alloftheself-poweredsurfacevehiclescanfollowatrackline.However,incalmconditions,whenwave-power
isunavailable,theywillnotbeabletodosoaccuratelyandinflatcalmconditions,willdriftwiththecurrent.
TheexceptionistheSV3,whichhasapropellerthatcanbeswitchedonatthesetimes,solongassufficientsolar
powerisavailable.Duetotheirsub-seastructurewhichdescends7mbelowthesurface,wavegliderscanonly
beoperated inat least12–15mwaterdepth (shallower-water versions canbeavailableon request). This
generallyrequiresdeploymentfromavesselequippedwithasmallcranetolowerthefloatandsub-unitover
theside.Wavegliderscanbedeployedfromaslipifthesub-unitistieduptothefloatandthevehicleistowed
outtodeepwaterpriortothesubunitbeingreleased.
TheAutonautvehicleshaveasmalldraftandcanthereforealsobeeasilydeployedfromaslipwayorbeachas
wellasfromasmallvessel.Oncedeployed,thevehiclescanstayatseaformanymonthsandbothWavegliders
andAutonautsareknowntohavesurvivedseverestorms.Communicationwithshoreviasatelliteisprovided
on all Waveglider and Autonaut vehicles except for the smaller Autonaut 2 which only has UHF andWifi
communication. Where satellite links provide insufficient bandwidth for data transfer, it is possible to fit
additionalradiolinkstothesevehiclesforhighvolume/shortrangecommunication.
8.2.3.4 DataprocessingandtransferinAUVandASV
Data processing and transfer issues are generic to AUV and ASV platforms. Basic communication (piloting
telemetry)withvehiclesisundertakenwitheasethroughthecurrentsatelliteorRFmodemoptionssincedata
volumesarerelativelysmall.However,datarelaygenerallyrequiresconsiderablymorebandwidthandmaybe
impossibletoachieveunlessahighlevelofautomatedprocessingcanbeimplementedinordertoreducedata
volume.Challengesoccurdependingon the sensors fitted to thebuoyancygliders, andwhat information is
requiredtobetransmitted.Therefore,theseissuesarerelatedtothesensorfittedtotheplatform
8.2.4 AUV/ASV-sensors
Thereviewofsensorshasbeenlimitedtothosewhichhavebeendeployedonautonomousplatformsorwhich
performahighlevelofprocessing,makingthemsuitableforreal-timedetection.Thisreviewthereforeexcludes
mostofthePAMsystemsdesignedforfixedautonomousmonitoringreviewedinSousa-Limaetal.,(2013).
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8.2.4.1 PAM
Todate,mostinterestfromresearchershasbeeninthelowercostsubmarineglidersandself-poweredsurface
vehiclesinthehopethattheywillprovidequiet,persistentplatforms,capableofcollectingPAMdataonlarge
temporal and spatial scales. The only PAM systems which are suitable for use on small lower powered
autonomousvehiclessuchasunderwaterglidersaretheSoundTrap,theDMONandtheWISPRboard.Indeed,
theWISPRboardisavailableasastandardpackagewiththeKongsbergseaglider.However,itsrelativelyhigh-
powerconsumptionsignificantlyreducesthelifetimeofgliderdeploymentsfrommonthstoweeks.
Thesurfacevehiclesthathavebeenreviewedcouldpotentiallyworkwithanyofthesensorslistedsincethey
havefewerspacerestrictionsandgenerallyhavemorepoweravailable.However,theremaystillbetrade-offs
betweenpowerconsumption,poweravailabilityandmissiondurationformanypossiblecombinations.
Thelargerpoweredvehiclescouldalsoaccommodatewidebandsatellitecommunicationssystemsandcouldin
principle,providemorereal-timemonitoringwhenoperatingremotely.
WeareunawareofanyPAMsystemsbeingusedontherangeofpoweredunderwatervehicles.Thereisnothing
fundamental preventing this from happening beyond the obvious possibility of noise interference from
propulsionsystems.Mostlikelyresearchershaveshownlittleinterestinthisduetotheshortmissionduration
ofthesevehicleswhichwouldmakethemimpracticalforthecollectionofmarineanimalsurveydata(which
generallyrequiresdatacollectionoverlongtimeperiodsinordertoobtainsufficientsamplesize).
Wheregiven,pricerangesforPAMsystemswereallinthe$2,000to$10,000pricerange.However,itshouldbe
notedthattheremaybeengineeringcostsassociatedwithmountinganyofthesystemsintospecificvehicles.
Theseadditionalcostsmaybeneededtocoverinstallationofthemonitoringdeviceintoasecuredryspace,
mountingofhydrophones,supplyofpowerandinterfacingofanycommunicationssystems.Thesecostsare,
however,likelytoremainlowcomparedtothepurchaseandoperationalcostsofmostvehicles.
SeveralofthePAMsystemsarerecordingonly,whereasotherspurporttoofferreal-timedetection.Perhaps
themostsuccinctlyusefulcommentcameinareplyfromCornellUniversityregardingtheautomaticdetection
capabilitiesoftheirAutoDetectionbuoys:“…anyspeciesforwhichatrustedalgorithmexists”.Asdiscussedin
section 9.5.2, an increasingly wide array of automatic detection algorithms is appearing in the literature,
however,mostofthesearetunedforveryspecificanalysisofhistoricaldataandareunlikelytoperformwellin
many situations. PAM systems generally require a human operator to verify detection data and without a
considerable quantity of data available for human verification the chances of false alarms from automatic
detectorscanbehigh.
Forlongtermmonitoring,themostpracticalsolutionsareprobablythelowestpowersystemavailablewhich
canrecordasmuchdataaspossibleandperhapsperformaminimumofdatareduction.Keytoanymission
planningisdefiningtherequiredupperfrequencylimitofthesystemsincethiswillcontrolthemissionduration
availablewithagivendatastoragecapacity.Eitherallrawdatacanbestored,orsimplealgorithms(employed
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torunatbothhighefficiencyandhighfalsealarmrate)canbeusedtoreducethequantityofstoreddata,which
isparticularlyusefulwhenworkingwithhighfrequencyspecies.
Formitigationmonitoring,thenmissiondurationmaybeoflessimportanceandprioritymaybegiventodata
relay,on-boardprocessingandeaseofconnectivity,whichisprovidedbythehigherpowersystems.Underwater
vehiclesystemsclearlyhaveadisadvantageformitigationmonitoringgiventhetimedelaysincurredwhilethey
returntothesurfacetotransmitdata.However, forsomemitigationscenarios,wherespeciesareknownto
moveslowly,theremaystillbeauseforthesevehiclesinmappingoutareasunlikelytocontainmarinemammals
so thatoperations canminimise the likelihoodof incurringa shut-downwhilst real-timemonitoringaround
thoseoperations.
None of the systems reviewed had any significant operational requirements. However, for real-time
interpretationof incomingdata,trainingandexpertisewouldberequiredofasimilar levelas isrequiredfor
PAMoperatorsonseismicvessels.
Physicaldimensionsofthesystemsvariedfrom9.5x4x2.5cmforthesmallestsystemto56x13x11cmfor
the largest.Weights ranged from120g to1.5 kg.Power requirementwasalsohighly variable,with several
systemsusing lessthan100mW,the lowestbeing35mWforthesinglechannelSoundTrapHF.Thehighest
powersystemsusedbetween2and4Wofelectricalpower.
Mostsystemshavetheabilitytostorerecordeddata,mostlyusinglosslesscompressionalgorithmssuchasFLAC.
However,evenwithterabytesofinternalstorage,thisdoesnotovercomethefundamentallimitationsofstoring
dataacquiredathighsamplerateoutlinedinsection9.5.However,asstoragecapacitycontinuestoincreaseit
islikelythatfullrecordingsofevenhighfrequencysoundwillbecomeincreasinglyaccessibleinthecomingyears.
RawPAMdatacomesinatarateofamegabytepersecondforhighfrequencyspecies,100kilobytespersecond
formid-frequencysoundssuchasspermwhaleclicksanddolphinwhistlesandafewkilobytespersecondfor
low frequency sounds, such as baleen whale calls. Some of the data relay systems using either analogue
transmission or Wi-Fi based digital technology are capable of transmitting that data volume over several
kilometresusingfreespectrum(i.e.nodatarelaycostsbeyondequipmentpurchase).Powerconsumptionfor
thesedatarelaysystemssbetween10and30W,whichismanytimeshigherthanthepowerconsumptionof
themonitoringsystemsthemselvesandwouldimpactonthelifetimeofmostvehicledeployments.However,
formitigationmonitoring,wheredeploymentsdurationsofhourstodaysmaybeacceptable,thismaynotbea
problem.
Thehighestpowersatellitebasedsystemscanrelaydataat128kbpsor16kBytespersecond.Thiswouldbe
insufficient fortransmissionofanythingbut lowfrequencyrawdata.Thesesystemsarealsophysically large
(tensofcentimetresineachdimension),highpower(100–150W)andcostthousandsofdollarspermonthto
operate.Smaller,lowpowersatellitesystems,suchastheIridiumL-Band,areavailablewhicharemoresuitable
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formountingonautonomousplatformsandonlyconsume2.5Wpower.However,thelowpowersystemshave
anevenmorerestrictedbandwidthof2,400bps(300bytespersecond,orarawacousticbandwidthof150Hz).
Theonlysystemsweareawareofwhichhaveachievedsufficientlyreliablealgorithmssuchthatadequatedata
canbesenttoshorevialowbandwidthsatellitelinksaretheunderwatergliderbasedDMON(Baumgartneret
al.,2013)andtheCornellAutoDetectionbuoys.However,bothofthesesystemsonlyachievethatlevelofdata
reduction for certain species of baleen whale. None of the systems reviewed have to date sent adequate
informationviasatellite linksforhighfrequencyodontocetesoundsforreliablereal-timedetectionwith low
falsealarmrates.SeveralofthesystemsofferserialandEthernetbasedconnectivityandtheSAInstrumentation
LtdDecimusandtheSeichesystemcanbothbeusedwithwirelessmodemsystemstosendenoughdatainreal-
timeformitigationmonitoringofawiderangeofspecies.
To summarise: for real-time mitigation monitoring, high frequency real-time data can be sent over short
distances (up to a fewkilometres)using free spectrum radio links.Where local radio links arenotpossible,
accurate identification of marine mammals is currently only practical for some baleen whale species. For
populationmonitoring,whereit isunlikelythatradiolinkscanbeused,rawdata,orpartiallyprocesseddata
shouldbestoredforanalysispostrecovery,withonlysummaryandstatusdatabeingsentthroughsatellitelinks.
8.2.4.2 AAM
CurrentlythereareselectedAAMsensorsandAUVgliderplatformsthathavealreadybeenintegrated.These
include:theImagenexES853andNortekADCPsensorintoboththeSlocumG2gliderandtheSeaglider(ogive),
aswellastheImagenexES853sensorintotheSlocumG2hybridandtheSontekADPsensorintotheSCRIPPS
Spray.ThecomparisonmatricesshowedthattheImagenexES853,Kongsberg/SimradWBTmini,SontekADP,
NortekADCPandtheVemcomodularVR2Ccurrentlyhavethecapacitytobeintegratedintoanyofthelisted
AUVglidersystems.TheBiosonicsDT-X-Sub,miniWBATandtheASLAZFPcouldpotentiallybeintegratedinto
anyofthelistedAUVglidersystemswhilstthesesystemsandtheSimradWBATcouldbeintegratedintolarger
AUV.ABiosonicsmultifrequencyinstrumenthasbeenintegratedintoaWaveglider(Greene,2014).
Wheregiven,pricerangesforAAMsystemswereallinthe$2,000to$100,000pricerange.However,thisisthe
purchase price for the sensor and therewill likely be additional costs associatedwithmounting any of the
systemsintospecificvehicles.Ofthesensorsthatareavailabletorent,thepriceforathree-monthrentalranged
between$5,000and$20,000.
ThedatacollectedbytheAAMsystemisraw,unprocesseddata,withtheexceptionoftheBiosonicsDT-X-Sub
whichhason-boardprocessorsthatproducesdatasummarieswhichcanbeviewedassimplifiedechogramsand
isabletoprovidealertstriggeredbyacousticevents.MostoftheAAMsystemsareableto identify fishand
zooplanktonfromtherawdatacollected,aswellasprovideabearinganddirectrangeestimatetotheanimal
fromasingledevice.Conversely,theVemcomodularVR2CandVMTcanonlybeusedtodetectspecieswhich
arelargeenoughtobetaggedwithanacoustictagandcannotprovideeitherabearinganddirectrangeestimate
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totheanimalfromasingledevice.Real-timedatatransmissionispartlypossibleforallAAMdevices(apartfrom
theVemcoVMT)whereapingcanbetransmittedinrealtime.Howeversinglepingshavelimitedvalueexcept
toindicatetheinstrumentisworking,analysesaretypicallyundertakenonmultiplepings.
Whereprovided,thelevelofautomationfortheAAMsensorswascategorisedasself-loggingwithafixedping
rateorprogrammedintervals.
TherawdatavolumevariesgreatlybetweenAAMsensors,dependingontheircomplexity.Forexample,theraw
datavolumefortheImagenexES853is256bytesperping,comparedtothe32bytesperpingfortheSontek
ADPand5,6124to1,159bytesperpingfortheASLAZFP(dependingonthenumberoffrequencies,binsizeand
range).MoreinformationonthedataprocessingandtransferinformationwasprovidedfortheBiosonicsDT-X-
sub,whichhasarawdatavolumeof30MB/sec,theabilitytoprovidelowbandwidthsummaryreportingasa
methodofreal-timedatareduction,apowerusageof30wattsandarequiredexternalpowerfeedof12to24
voltsDC.
8.2.4.3 Animal-bornetags
ItispossibletointegratetagreceiversintoanAUVorASVtoprovidereal-time/near-real-time(dependingon
glidercommunicationstrategy)information(e.g.Waveglider,SlocumorREMUS).Vemcotaglocatorshavebeen
integratedintoaSlocumglider(Haulseeetal.,2015),REMUSAUV(Eileretal.,2013)andaWaveglider7.PAM
systemsasevaluatedinsection8.2.4.1canpotentiallybeusedtodetectthesignatureofacoustictags(Sparling
etal.,2016).Wedidnotconsiderthesesystemsfurtherasithasaminorroleinmonitoringwithregardstonoise
impact.
8.3 Discussion
8.3.1 How to use the evaluation results to find the appropriate autonomous platform / sensor
combination
Thisreviewpresentsa largevarietyofautonomousvehiclesandsensors thataresuitable formarineanimal
monitoringduringE&Pactivities.Therequirementsformarineanimalmonitoringwerethedriversbehindthe
informationthatwasgatheredforeachofthesesystems.Thecompiledcomparisonandevaluationmatrices
(seesections11.3and11.4aswellasTable2,Table3andTable4)helptoselecttheappropriateplatform/sensor
combination, and the followingdiscussionpointsoutwhat capabilities are required from theplatformsand
sensorsinvariousmonitoringsituations.Anyonewishingtousetheinformationprovidedinthisreviewshould
definetheobjectivesandrequirementsofthemonitoringfirst.Forexample(thisisnotacomprehensivelistof
questions):
7 http://oceantrackingnetwork.org/otn-tests-new-wave-glider-technology/#prettyPhoto
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• Whichanimalspeciesorspeciesgroupshallbemonitored?
• Isitmitigationmonitoring/populationmonitoring/fine-scalebehaviouralmonitoring?
• IfduringE&Pactivity,whatkindofactivity?
• Isthemonitoringareanearshoreoroffshore?
• Whatisthesizeofthemonitoringarea?
Speciesorspeciesgroupwilldeterminethekindofsensorthatcanbeusedformonitoring.Anextensivereview
on which sensor is best suitable for which type of animal in which environmental conditions (including
geographicallocation)wasrecentlyconductedforIOGPandsummarisedinVerfussetal.,(2016)andistherefore
kepttoaminimuminthecurrentreview.
AAMcandetect anythingwith a largeenough target strength tobe capturedby its receivingelements, i.e.
enoughenergyfromtheemittedsonarpulsesneedstobereflectedbytheanimals’bodytobeimagedonthe
sonar screen.This iswhyany large “body”, fromzooplanktonpatchesand fishesup to largewhales canbe
detected(Table2).Classificationoftheanimalspeciesorspeciesgroup is,however,complexwithAAMand
reliesonknowledgeoftheanimal’sspecifictargetstrengthatdifferentfrequenciesandswimmingbehaviour.
Specificalgorithmsfordetectingandclassifyingmarineanimalssuchasseals,porpoise,dolphinsandsharkswith
theTritechSeaTecsystemhavebeendevelopedorarestillindevelopment(asdiscussedinsection7.3.3).These
mayhowever,onlyworkinthecontextforwhichtheyweredevelopedandtherefore,mayneedtobetested
forothersituationsorenvironments.
PAMonlydetectsvocalisinganimalsandismainlydevelopedforwhaleanddolphindetection.Whichcetacean
speciescanbemonitoreddependsmainlyonthefrequencyrangeofthesystem,thekindofdetectorusedand
whethertonalcallsorecholocationclickscanbecaptured.Speciesidentificationisrestrictedtoanimalswith
verydistinctivecallsorclicksthatcanbedistinguishedfromotherspeciesanddependsonthesoftwareused
with the systemand the entailed classification algorithms.Detection algorithms canoftenbeused for data
reduction,filteringthemostlikelydetectionsoutofthehugeamountofdataretrieved,butgenerallyrequire
visualconfirmation,especiallyformitigationmonitoring,wherefalsealarmsmaycausedelaysinoperations.
WhilePAMandAAMaresensorsthatareusedwithAUV/ASVandarethereforespecialisedtounderwaterlife,
video sensors are coupledwithUAS andworkwell for animals surfacing frequently. Thismeans that video
sensorsmaybeinefficientfordeepandlongdivingspecies,atleastformitigationpurposeswherethedetection
probability would need to be high (see Verfuss et al., 2016). With appropriately trained human observers
monitoringthevideostream,classificationofanimalswiththiskindofsensorispossible.Inordertoreducethe
hugeamountofdata collectedbyvideo sensors, algorithmscanbeused topresent likelydetections to the
observer,thusdecreasingthepotentialworkloadandincreasingobserverefficiency.
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Thekindofmonitoringdetermines theplatform requirements. Thesearedetailed in section9.1.Mitigation
monitoring, for example, relies on continuous real-time detection capabilities in order to enable a timely
decisiononwhethermitigationmeasuresare tobe takenornot (section9.1.1). Forpopulationmonitoring,
sendingdatainreal-timeisnotrequiredbutmaybeadvantageous(section9.1.2).Focal-followsmayormaynot
requirereal-timeoption,dependingonwhetherthevehiclesarebeingpilotedmanuallyorusingdetect-and-
trackcapabilities(section9.1.3).
Table2givesanevaluationofthosesensorsincludedinthisreviewwithreal-timedetectioncapabilities,and
whichspeciesgrouptheyareabletodetectorevenclassify.
Formitigationmonitoring,wherethecontinuousreal-timemonitoringisrequired,thesensoraloneisnotthe
determiningfactor.Theplatformneedstobecontinuouslyincontactwiththedatarelaysystem.Anysensor
mountedonanunderwaterautonomousvehiclecanonlysenddatabackwhenattheseasurfaceandincontact
withtherelaysystem.Therefore,onlysurfaceoraerialsystemswouldbefeasibleinmonitoringandmitigation
instanceswhencontinuousreal-timedetectionisrequired.
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Table2.Evaluationmatrixofdetection/classificationcapabilitiesofsensors(includedinthisreview)capableofreal-timedetection.Withthehelpofthesesensors,oneiscapableofdetecting(Detect)ornotcapableofdetecting(No)marineanimals.Someofthesensorsdeliverdatawithwhichmarineanimalscanbeclassified(tosomeextent)tospeciesorspeciesgroups(Classify).
Systemclass Systemname Realtime
Zooplankton
/Fish
Whales
Dolphins/
Porpoises
Seals
Turtles
Largefish
AAM
Aquadopp Partly
Detect
ADP Partly
AZFP Partly
DT-XSUB Partly
ES853 Partly
Gemini720i YesDetect Classify Detect Classify
Gemini720is Yes
ModularVR2C+tag Yes Classify
WBAT PartlyClassify Detect
WBTmini Partly
PAM
C-POD-F Yes
No
NoClassify
No
Cornell/AutoBuoys Yes
Classify
Decimus Yes
DMONYes
(Whalesonly)Detect
SDA14 Yes
Classify
SDA416 Yes
Seichereal-time
transmissionsystemYes
WISPR Partly
VIDEO
CM100 Yes
Detect Classify
CM202 Yes
DualImager Yes
EO900 Yes
OTUSU135HIGHDEF Yes
OTUS-L205HIGHDEF Yes
TASE310 Yes
TASE400HD Yes
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ThekindofE&Pactivitywouldfurthermoredeterminethechoiceofplatform,e.g.anyAUV/ASVaroundseismic
surveyvesselswouldcreateasignificantsafetyrisk(seesection9.3.5)andwouldthereforenotbesuitablefor
mitigationmonitoringintheseareas(Table4).Ontheotherhand,deploymentofanAUV/ASVinthevicinityof
an explosive removal operation for a period of monitoring prior to detonation might be relatively
straightforward.Thelocationofthemonitoringareawilldeterminewhichdatarelaysystemtobeusedaswill
themonitoringkind.Mitigationmonitoringoftenrequiresthetransmissionofalargeamountofdatatoahuman
observerforthepurposeofverifyingdetections.Therefore,ahighbandwidthisrequired,suchasisprovidedby
awireless system. For locations furtheroffshorehowever,wireless systemsareoftenunavailable (Table3).
Satellite systems, by contrast, have an unlimited communication range thatwould assist the data transfer,
however,thedisadvantageisthelowbandwidththatsatellitesystemsemployingeneral.Theinstallationofa
wirelesssystemonavesselaccompanyingtheautonomousvehiclewouldbeanothersolutionforshortrange,
highbandwidthcommunicationoffshore(formoreinformation,seesection9.5).
Table3.Propertiesgenerallyapplyingforautonomousvehiclesforthedatarelaysystemssatellitelinksandwirelesssystems
Property Satellitelinks Wirelesssystem
Bandwidth Low High
Communication
rangeUnlimited Short
Thesizeoftheareamonitoredwilldeterminethetechnicalrequirementsofaplatformclass.Largeareaswill
demandlargerandmorerobustvehicleswhilesmallerandmoreagilesystemsmaybemoreappropriatefor
smallermonitoringareas.Forexample,inthecontextofoffshoreseismicsurveys:smalllow-rangeUASmaybe
usedtomonitormarinespecieswithintheexclusionorhigh-riskzone;whilelongrangeUASmaybeusedto
obtaininformationbeforeandafterthearrivalofseismicvessels,thusgatheringinformationonanimaldensity
anddistributioninthespecificregiontargetedforoperations(baselinemonitoring)(Wattsetal.,2010).
Theabovetextisanexampleforhowtoapproachthedatagatheredinthisreview.Thesubsequentsectionswill
discussandexplain furtherdetails tobe considered.Thisoverviewofautonomousvehicleswill support the
selection of the appropriate combination of systems for data acquisition and processing, considering the
differentqualitiesofplatformsandsensorsthatareapplicabletodifferenttypesofsurveys.
Ashighlightedabove,thediversityofapplicationsforautonomousvehiclesmeansthatspecificsurveyscenarios
arerequiredinordertoprovidespecificrecommendationsonwhichplatformandsensorcombinationmightbe
best to use in a specific scenario. Given the large number of target species, selection of the appropriate
sensor/platform type is possible rather than selecting specific individual systems. The same is true for the
differingmonitoringtypes,combinedwiththechoiceofdatarelaysystem.Thetechnicalandoperationaldetails
will need to be tailored to the specific needs of the E&P project, e.g. the area of interest, the platform an
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autonomousvehiclewillstartandfinishoperatingfrom,therequiredsurveylength,projectbudgetandmore.
Herewegiveanoverviewofwhichplatformtypesarebestforwhichmonitoringtypebycomparingeachclass
ofplatformsandhighlightingtheirparticularstrengthsrelatingtothespecificmonitoringtypes.Theresultsare
summarisedinTable4.ExamplesofspecificE&Pprojectoroperationsneedswillbegiventonarrowthechoice
ofplatformsbestsuitedtoaspecificapplication.Onecouldthenchoosetheappropriateplatformandsensor
fromtheshort listsgiveninsection11.2,basedonthetechnicaldetailsandoperationalaspectsgiveninthe
comparisonmatricesinsection11.1thatsuitthemonitoringapplicationconsidered.Theevaluationmatricesin
section11.4,showingwhichsensormayfitintowhichplatform,thenhelpstofindtheappropriatecombination
ofthosesystems.
Itis,however,importanttonotethattheuseofautonomousvehiclesforanimalsurveyingwillencountermany
of the same survey design, data collection and analysis issues currently experienced when using standard
mannedsurveyvehiclesorstaticPAM.Forexample,itshouldbeshownthatanyautomaticdetectorsusedare
abletoperformadequatelygiventhesurveyingconditionse.g.inpoorweatherconditions(forbothvisualand
acoustic data) or in the presence of local industrial or ambient noise sources (acoustic data). Furthermore,
suitablehardwareshouldbeselectedifestimationofdetectionprobabilities,orothermultipliers,isrequired.
Table4.Evaluationmatrixofmonitoringcapabilitiesofsensor/platformtypes.Giventheymeetthecriteriaasoutlinedinsection8,thespecificsensor/platformtypeslistedareingeneralwellsuited(�),suited(x)ornotapplicable(-)forthecertainmonitoringtypes(exceptionsmaybepossible).Themonitoringtypesarefurthermoredividedintomitigationmonitoringinareaseitherclearfromorbusywithotheroperationalgearortraffic,short-term(hours,days)orlong-term(weeks,months)populationmonitoringandfocal-followsconductedwithstaticormobilesystems.L-t-a=Lighter-than-airaircraft.
Monitoringtype
Mitigation Population Focal-follow
Sensor Vehicle Clear Busy Short-Term Long-Term Static Mobile
Electro-optical Poweredaircraft � � � - x �
Motorisedgliders - - - - - -
Kites � � x - x x
L-t-aaircraft � � x - x x
PAM
PoweredAUV � - � - � x/-
ASV � - � - � x/-
Self-poweredAUV � - � � � -
ASV � - � � � -
Drifter - - � � - -
AAM
PoweredAUV � - � � � �
ASV � - � � � �
Self-poweredAUV � - � � � �
ASV � - � � � �
Drifter - - � � - -
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8.3.2 Strengthandweaknessesofautonomousvehicletypes
8.3.2.1 UAS
UASgenerallyoperateatalowerspeedthanmannedaircraftandwithasmallerfieldofview.UASmay,however,
compensateforthisbysurveyingforlongerperiodsoftimeandreachingareasinaccessibletomannedaircraft.
Weatherconditions,suchasseastate,havebeenreportedtoaffectsightingrateofmedium-sizedmammals
lesswhenusingUASimagingcomparedtohumanobservers,whichisoneofthemainlimitingfactorsknownto
decreasesightingratesinmannedsurveys(Hodgson,2013).ThevariabilityinUASsizescurrentlyavailableisalso
agreatadvantageforoffshoremonitoring,sincesmalleraircraftallowformonitoringofsmallerareasatalow
cost (e.g. exclusion zones during seismic operations), and larger aircraft can cover larger areas and provide
valuable informationbothbefore,during, andafteroffshoreoperations. The longoperational rangeofUAS
mean that they are also quite suitable formitigationmonitoring of the area ahead of a seismic vessel for
detectingmarinemammalswithinanexclusionzonearoundtheanticipatedstartlocationofthesoundsource,
assuggestedbyVerfussetal.,(2016).
Inadditiontothepotentialuseformonitoringmarineorganismsusingvisualsystems,UASmayalsobeusedto
drop various types of autonomousmonitoring systems at pre-determined locations. In the case of marine
mammalmitigationinrelationtoseismicsurveys,itmaybepossibletodrophydrophoneswithabuilt-inradio
link(e.g.sonobuoys8)toacousticallymonitorareaspriortothearrivaloftheseismicexplorationvessel.This
would allow the operators to optimise operations planning given this “early warning” of potential marine
mammalpresence.
Hence, it ispossible to coverawide rangeof studiesusing the same technology.However, thereare some
shortcomingstotheuseofUAS,suchastherestrictedfieldofviewofanimageandtheimageresolutionrequired
tocompensateforthisathigheraltitudes,andthetimetoanalysethedataafterthesurvey.Forthislastfactor,
real-timedatatransmissionand/orautomateddetectionofanimalswouldhelptoreducepost-survey image
analysistime.
Theadvantagesofusingunmannedsystemsnotonlycoverhealthandsafetyissuesbutalsothequalityofthe
datathatcanbeacquired.Datafromdigitalsurveysovercometheissuesrelatedtoobserverbiasandaccuracy
oftheinformationconcerningflightoperationdata(altitude,speed,fieldofviewaccuracy).AcquiringtheGPS
locationofeveryimageprovidesgreateraccuracyinthelocationofadetectionthanformannedsurveyswhere
8 See http://www.uasvision.com/2015/07/22/ultra-electronics-studies-uav-drop-options-for-sonobuoys, https://www.google.com/patents/US3986159 (last accessed 03.02.2016)
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observersreportthesightings.UsingtheGPSlocationofanimage,itispossibletoobtainthecoordinatesfor
theimagecorners,andthusofeachanimalsightedwithinit(softwareVadar9).
ThesuccessrateintheuseofUASishighlydependentonthemainaimsforitsuse.Circumstancesthatdemand
long range operations with higher sensor quality will thus be pricier than in circumstances where focal
monitoringofsingleindividualsorgroupsofanimalsisneeded,whichmayprovidevaluableresultswithsmaller
andlesscostlyplatforms.Althoughthereareseveralhundredsofplatformsandsensorsonthemarket,andnew
systemscontinuouslybeingdeveloped,itisobviousthatmanysystemsproducegoodqualitydataregardlessof
theplatformused.Thecombinationbetweenplatformandsensorshouldbechosenbasedontheoperational
needsandthecapacityfortheplatformtocarryaspecificsensorfortheamountoftimeordistancerequired.
Dependingonthepayload,alow-costcamerawithhighorlowstabilityandqualitycanbemountedintothe
system,providingvaluableinformationaboutanimaldistribution,abundance,andbehaviour.Balloonsandkites
arecost-savingalternativesforsomefixedandrotarywingsystems(ThammandJudex,2006;Altanetal.,2004;
Fotinopoulos, 2004; Grenzdörffer et al., 2008; Scheritz et al., 2008), though they rely heavily on weather
conditionsandaredifficulttocontrolwhenattemptingtofollowapredefinedtrack.Intermsofdataanalysis,it
isoftensufficienttohaveaquickoverviewofimagesorvideosacquiredusinglow-costsensors,whereasfor
accuratemeasurementswithmoreprecisemethods(e.g.differentialGPS),datarequireamoredetailedreview
and evaluation. In recent investigations, the trend seems to be directed to fast processing such as online
triangulationanddirectgeo-referencing,whichmayfacilitateimageryanalysisandreducepersonnelcosts.In
addition,thesensorsandsystemsaregettingsmaller,atafractionoftheprice,andoperatingwithopensource
platformsthatarecontinuouslybeingdevelopedinphotogrammetry.Dataintegrationofvarioussensorsand
highprecisionandresolutionofspecificapplicationsandnearreal-timeapplicationshasalsobeenunderfocus
recently,withcontinuousprogress.
ThisstudymakesthefollowingshortlistofrequirementsforaUASmonitoringsystem:Stabilisedhigh-resolution
videofordetection,highresolutionimagesforidentification,real-timegeocodingofimagedataandacomputer
systemforefficientanalysisofrecordeddataandlocalisationofanimals.Ifthedataaretobetransmittedtothe
groundahighbandwidthdatalinkisalsorequired.
8.3.2.2 AUV/ASV
ThereisabroadrangeofAUVandASVmodelsandsensorsthatwillmeetmostneeds.Theplatformshouldbe
chosen based on the size, endurance, payload and depth capabilities required for each application. Larger
poweredAUVaregenerallymoresuitablefordeepwater,openseatasks,thansmallpoweredAUVwhichare
moresuitedtoshortermissions,wherethereducedcostisamustandthesmallerpayloadisnotalimitation.
9 www.cyclopstracker.com
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UnderwaterbuoyancyglidersoffersimilarcapabilitiesforsensorcombinationsaspoweredAUV.However,they
havereducedpayloadcapabilities,reducedmanoeuvrabilityinfastcurrentsandoperateatslowspeeds.Onthe
positiveside,theyhavelongmissiondurations,independencefromamothershipandtheabilitytoprofilethe
verticalrangeoftheocean.Theintegrationofbothactiveandpassivesensorsintounderwaterbuoyancygliders
isalsorelativelywelladvancedinthescientificcommunity.
Self-poweredcraftsuchastheWavegliderofferlargelysimilarcapabilitiesaspoweredASVbuttherearesome
maindifferences.Reducedpayloadcapabilities in self-poweredcraftmaybea limiting factorbut the longer
missiondurationandgreater independence(intermsofminimisedmechanicalmaintenance)ofsimplerself-
poweredASVhassignificantappeal(particularlyforlongertermenvironmentalmonitoring).
8.3.3 Platformtypesformitigationmonitoring
MitigationmonitoringgenerallytakesplaceinthepresenceofsomekindofE&Pactivitymeaningthatitishighly
likelythatanumberofvesselsareoperatinginthearea.Thesevesselsmayhavecompetenttechnicalpersonnel
on board with the ability to deploy, service and operate autonomous platforms. However, in many
circumstances,vesseloperationsarealreadyworkingclosetothelimitofwhatispracticallyfeasibleintermsof
equipmentdeployment,personnelarealreadyassignedtochallengingandall-consumingtasksandtheremay
notberoomonboardeitherforadditionalequipmentorforadditionalpersonneltooperateit.
Unmannedvehicleshavesofarnotbeenusedformitigationmonitoringbutbringpotentialintothisfield.Some
systemsmaywellbeplacedfortheuseofmitigationmonitoringastheyallowfor(near)real-timedetection,
whichisnecessaryfortimelydecisionmaking.Thesesystemswouldneedtooperateforlongenoughperiods
and cover wide enough ranges to meet the temporal and spatial requirements of the various guidelines.
Synchronisedoperationofasmallorlargerfleetofvehicleshasthepotentialtoobtainawiderfieldofviewto
cover larger areas than single vehicles. The concurrentuseofdifferentmitigationmonitoringmethods, e.g.
different autonomous sensor/platform types combined with monitoring methods installed on a manned
platform(e.g.MMO/PSO,thermal-IR)will increasetheprobabilityofanimaldetection,whichisanimportant
factorinmitigationmonitoring.
Mitigationmonitoringisusuallyconductedclosetothemitigationzoneasternoftheseismicvessel–anarea
wherenovessel,letaloneanautonomousone,cansafelyoperate.Othersupportvesselsdooperateaheadand
tothesidesofseismicvessels,but,asoutlinedinsection9.2.1,therewouldbesevereproblemsindeploying
additionalautonomousseabasedvehiclesintheseareas.ThisiswhyAUVandASVarenotconsideredsuitable
formitigationmonitoringinoperationallybusyareas(Table4).Aerialsystemshowever,maybesuitedifthey
meettherequirementsoutlinedinsection9.1.1.
Indynamicoperations(e.g.aseismicvessel)thereisgreatpotentialforautonomoussystem,especiallyUAS,to
scopeahead,tooperateasanalertsystemtomitigationmonitoringmethodsonthesourcevessel. Inmany
operations, the unmanned systemswould be at a stand-off distance from the source, whichmay (but not
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necessarily)limittheabilitytoestablishtheanimal’spositonrelativetothemonitoringzoneorsoundsource,
respectively.However,onebigadvantageofautonomoussystems is thattheygivetheopportunitytocover
great distances and large areas, especially when using multiple systems that coordinate their movements
throughaco-operativecommunicationsystem.However,asdiscussedabove,therearesignificantlogisticaland
safetyconstraintswhichwouldneedtobeovercomebeforeseabasedautonomousvehiclescouldbeoperated
inthevicinityofaseismicsurvey.
As discussed in section 9.1.1.1, the localisation of the target animal would be a great advantage. The
experimentalautonomousPAMsystemshavetendedtouseonlyoneorasmallnumberofhydrophonesclose
together. In most cases, these can only provide animal presence; some may be able to provide bearing
informationtotheanimal,butnonecouldprovideanimalrange.Theonlyexampleofanexceptiontothisisthe
simultaneousdeploymentofthreesubmarineglidersofftheUSEastCoast(Fuciletal.,2006),wheretimeof
arrivaldifferenceofthesignalatthethreevehicleswasusedtoestimateanimallocation.Whilethistechnique
couldbeimplementedusingmultiplesurfacevehicles,ifthedeploymentofonevehicleinthevicinityofaseismic
survey isproblematic,thendeployingthree(ormore)vehicleswouldclearlyrequirecarefulconsideration.A
vehiclecapableoftowingmorethanonearrayorequippedwithbeamformingcapabilityorvectorsensorscould
alsoresolveleft-rightambiguitiesandlocalizeinnearrealtime.
Ifitwerepossibletodeployvehiclesclosetoaseismicsurveyvessel,automaticdetectorscanbeusedwithsome
species, but false alarm rates are rarely low enough for action to be taken without some verification, so
significantamountsofdataneedtoberelayedbacktoanoperator.Therelayofsignificantquantitiesofdata
backtothevesselisaproblem,butnotaninsurmountableone,sinceanumberofwirelessradiosystemsexist
whichcansendwidebandwidthdataovershortdistances.
Inoperationssuchasunexplodedordnancedevice(UXO)removalorwell-headdecommissioning,wherethe
sourceisinafixedpositionbutanMMO/PSOordetectiontechnologycannotbeplacedcloseenoughtothe
source to provide adequatemonitoring, autonomous vehicles would provide a safe and effective solution,
particularly,astheydonotplacehumanswithinadangerousworkingarea.
TrialswithPAMonASVC-WorkerandC-Enduroshowedpromisingresultswithsuccessfulreal-timedetections.
AnappealingideaisfortheASVtoscopeaheadoftheseismicvesselformarinemammals.Thisoffersthebenefit
of operating in lower noise environment (away from the vessel), and would enhance ability to detect low
frequencybaleenwhalevocalisations,whicharehardtodetectwithPAMinstalledfromaseismicvessel(Verfuss
etal.,2016).However,thiswouldnotcoverthemonitoringzone(operatinginsteadasanearlywarningsystem).
So it would not replace existing methods, but would be complimentary, or could be used for assessing a
monitoringzonelocatedaroundtheanticipatedstartofsoundsourceoperation(e.g.duringseismicsurveys)as
suggestedbyVerfussetal.(2016).ThistaskcouldalsobeachievedusingUAS.
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8.3.4 Platformtypesforpopulationmonitoringandfocal-follows
8.3.4.1 UAS
Ofthethreeclassesofaerialplatformsconsideredinthisreview,poweredaircrafthavemanyofthecapabilities
requiredforaerialsurveysofmarineanimals.Kitessharemanyofthesameattributesaspoweredaircraftand
theyareeasiertotransportand,insomecases,deploythanfixedwingaircraft.However,theyarelessrobust
tobadweather,whichisamajordisadvantageforwildlifemonitoring,especiallyduringoffshoreoperations(see
Table4forthisandanyfurtherdiscussion).Sensordatafromtheseplatformscanbegeo-referencedusingdata
from a GPS and flight data recorder,which greatly improves the accuracy ofmeasurements related to the
individualanimal’spositionwhensighted.
Lighter-than-airplatformsalsohavecapabilitiestoperformmarineanimalsurveysbut,duetotheirrequirement
foratether,severalwouldeitherhavetobemooredtostaticbuoystocreateaseriesofmonitoringpoints,or
attachedtoamovingvessel (whichcould, intheory,alsobeautonomous)toconducta linetransectsurvey.
However,bothofthesescenarios(mooredandtethered)arepotentiallymorelogisticallycomplexthanusing
fixedwingUASand,therefore,poweredaircraftappeartobetheoptimalcandidateplatformforaerialtransect-
basedsurveysusingautonomousvehicles.
In terms of individual focal follows, however, lighter-than-air aircraft should be considered as a potential
candidateplatform,thoughtheirabilitytofollowanimalswillberestrictedtothemanoeuvrabilityofthesupport
vesseltowhichtheyaretethered.Poweredaircraftcanbepilotedtofollowanimals,rotarywingvehicleshave
performedwellinexistingstudies(Durbanetal.,2015),thoughfixed-wingaircraftmaystruggletohoverabove
stationary,orslowmoving,focalanimals.Therefore,poweredaircraftandlighter-than-airaircraftcouldbegood
candidateplatformsforindividual-basedmonitoring,dependingontheexactgoalsofthemonitoring.
Inconclusion,thestudiesmentionedpreviouslyandsystemsevaluatedforthisreviewhighlightthepotentialof
UASinmarinewildlifemonitoring.Koskietal.,(2009a)andHodgsonetal.,(2013)supporttheuseofunmanned
aircraftinmarinemonitoringanditspotentialtoreplacecurrentmethodsemployinghumanobserversposted
onthesourcevesselor inanaircraft.However,asmentioned inKoskietal., (2009a), thiscanonlyreallybe
achievedwhensurveyinglargeindividualsorlargegroupsofsmallanimalsifthesearchareaissmall.Forlarger
areasanddetectionofsmalleranimals,higherresolutionvideoisrequiredifoneistocomparebothmanned
andunmannedaerialdetection.Therefore,thenextstepsinestablishingtheefficacyofreplacingmannedaerial
surveyswithUASistoconductadirectcomparisonanddeterminewhethertheproportionofanimalsavailable
fordetectionfromtheairisequivalentforbothmethods.Thiswouldneedtobeconductedunderavarietyof
conditions,rangingfromArcticsummer/wintertotemperateandtropicalregions,whereboththedetectability
andenduranceofUASisexpectedtovary.Additionally,testingatdifferentlocationscouldalsoincludearange
ofdifferentspecies,whichwouldassistindeterminingthelimitationsofspeciesidentification,particularlyin
regionswithlong-divingandevasivespecies.
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8.3.4.2 AUV/ASV
ThefiveclassesofAUV/ASVreviewedinthisreportareallcapableofcompletingmarineanimalsurveysof
various kinds (see Table 4 for this and any further discussion). Some classes of platform share similar
characteristics, though there are also clear different strengths andweaknesses between classes relating to
marineanimalsurveying.
Thepotentialtoincreasespatialandtemporalsurveycoverageisamajorbenefitofusingautonomousvehicles.
Tothatend,thefactthatpoweredsurfacecraftandpropellerdrivenunderwatercrafthavesurveyduration
times on the order of hours, rather than the months that unpowered vehicles can be deployed for, is a
disadvantage for long-term wildlife surveying (though a notable exception was the ASV C-Enduro, with a
maximumsurveydurationof90days).However, theseplatformsmaybemore suitable for individual focal-
follows,especiallythosewithincreasedmanoeuvrability.
The autonomous underwater buoyancy gliders and the self-powered surface vehicles generally havemuch
longersurveydurations,whichmakethemveryattractiveforlong-termwildlifemonitoring.Furthermore,the
abilityofunderwatergliderstoverticallyprofilethewatercolumn(whichmayalsoaiddetectionofdifferent
marine animals that occur at varying depths) is a further advantage of these vehicles. Reduced payload
capabilitiesmaybea limiting factorbut the longermissiondurationandgreater independence (in termsof
minimisedmechanicalmaintenance)ofsimplerself-poweredASVhassignificantappeal,particularlyforlonger
termenvironmentalmonitoring.
Animportantadvantageofthesmaller, lowpoweredautonomousvehiclesistheirlownoisefloorwhichcan
provideatleastsomelevelofconfidencewhencomparingsurveyresultsfromdifferentregionssincea)masking
ofanimalsoundswillbebothlowandstableacrossplatforms,andb)animalreactiontothesmallerplatforms
islikelytobelowandconsistent.Thevaryingnoiseoutputfromvesselscanbothmasksoundsandcausechanges
inanimalbehaviourmakingitdifficulttocomparedatacollectedfromdifferentsurveyvessels.Theuseoflarge
vesselsformarinemammalsurveysalsoposestheriskofcollisionwiththeanimalsunderstudy.PoweredAUV
arelesslikelytoplayaroleinpopulationmonitoringduetotheirlimiteddeploymentlifetimesandlikelynoise
interference,particularly forPAMsensors.Althoughrelativelyquietcomparedto researchvessels,propeller
drivenAUVmaydisturbmarinefaunainsensitiveregions,especiallywhenrunninggeophysicalsensorswitha
high acoustic output (Wynn, 2013). The noise generated by the propeller in propeller-driven AUV has the
potentialtomasklowlevelsignalsinthesamefrequencyrange,therebylimitingtheuseofthistypeofAUVfor
PAMpurposes.
Another limitation of the AUV / ASV platforms is their sensitivity to environmental conditions, particularly
currents, and how this affects survey design. Though information was not available for all platforms, the
poweredinstrumentsarelikelytohavemostresistancetocurrents(forexample,theREMUSpropellerdriven
AUVcanholdstationinstrongcurrents).Clearlythedriftingbuoysarethemostaffectedbycurrents,though
theirmajor benefit is their low price compared to other platforms. As discussed in section 11.6.2, current
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researchisfocussingonhowmuchdeviationfromaplannedtrackisneededbeforethesurveydesigncannot
beusedtomakeinferencesaboutanimaldensity/abundanceinthewiderstudyarea.Inthecaseofself-powered
platforms, itmaybe that theextendedsurveyduration is a suitable trade-off for some track linedeviation,
particularlyifthealteredcoursechangesareminorcomparedtotheoverallsurveydesign.
FortheuseofPAMsensorsonAUV/ASV,hydrophonearrayswhichcanlocaliseanimalsmayberequiredfor
detectionprobability.Auxiliarybehaviouralstudiesmaybeneededtoestimatevocalproductionratesand/or
groupsizes.Forsomeregionsdataongroupsizeorvocalizationratesmaybeavailablefrompaststudies,or
might be extrapolated from similar species and regions in the absence of directly relevant data to supply
vocalizationrateandgroupsizevalues.RegardingrangeestimationusingPAM,ship-basedsurveysoftenuse
hydrophonearraysandtheappropriatesoftwaretoestimatebearingstoanimals.Astheshipmoves,multiple
bearingstothesameanimalorgroupofanimalswillcrossovertimetoprovideanestimateofrange(i.e.target
motion analysis). In the case of slowmoving autonomous vehicles, target motion analysis will not work if
animals’positionschangequicklyrelativetothevehicleasbearingstothesameanimal/groupareunlikelyto
cross.Therefore,morecomplexhydrophonearrayswillbeneededthatcanestimaterange,which,inturn,can
beusedtoestimatedetectionprobabilities.However,theversatilityofautonomousvehiclesisanadvantage
here; if amulti-element passive acoustic array (capable of estimating range to a calling animal) cannot be
deployedonasingleplatform,thenmultiplevehiclescouldbedeployedasanarray.Furthermore,amobilearray
couldberelocatedandalsoitsconfigurationcouldbeadjusteddependingonthetargetspecies.Theabilityof
anarraytobereconfiguredwouldbeanadvantageovercurrentfixedarrays,whichoftenhavetohavespacing
forrangeestimationthatisspecies(orgroupsofspecies)specificduetothewiderangeoffrequenciesatwhich
marineanimalsvocalise.
Although an advantage of self-powered platforms is that they can be deployed for long durations, the
consequenceisthattheyareslow-moving.Asdiscussed,thishassomeanalyticalimplications(i.e.theneedto
implementcue-countingorsnapshotmethodstomakesurethatanimalmovementdoesnotcreatebias).Italso
haslogisticalimplications–asmallerareaislikelytobecoveredbyanAUV/ASVthanbyashipinthesame
timeframe.Furthermore,anautonomousvehiclemayneedanadditionalsupportvessel,sothiscostneedsto
befactoredintothesurveybudget.However,despitetheseadditionalconsiderations,thefactthatautonomous
vehiclesbedeployedforextendedperiodsisamajoradvantageformarineanimalsurveys.Byhavingtheability
tothoroughlymonitoraspecificareathroughtime,butalsobeingabletoefficientlymovetootherpartsofthe
studyareamakethemapowerfulassettouse.
8.3.5 Technologiesthatmightbeusedinfuturefieldtrials
Thissectiondescribes(sectionsbelow)andsummarises(Table5)thetechnologiesthatmightbeusedinfuture
trials.
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8.3.5.1 UAS
Allsystemsreviewedinthisevaluationappeartohavethecapabilitiestomonitormarineanimalsinoffshore
regions.Futuretrialsshouldattempttofocusontheversatilityofeachsystem(platformandsensor)indetecting
speciesofvarioussizes,undervariableconditions.SinceUASmayhavedifferentqualitiesandapplications,itis
recommendedtoassesstheconditionstowhicheachsystemmaybemostsuited.Forinstance,forlong-range
experiments,itisnecessarytooperatevehicleswithhigherenduranceandrange,whichmaydemandspecial
flight permits and operating costs. Abundance and distribution studies focusing on the use ofUAS in areas
relevanttoOilandGasrelatedoperations,willprovidenotonlyinformationaboutanimaldistribution,butalso
seasonaldata that canbeused for theO&G industry toconductenvironmentally-consciousdecisions in the
operations conducted (e.g. restricting E&P activities temporally/spatially to avoid sensitive times/areas).
Additionally,studiesusingUASforbehaviouralexperiments,suchascontrolled-exposureexperimentsduring
seismicactivity,canresultinscientificallyvaluableknowledgethatcanalsobeusefulfortheindustry.
Side-by-side comparisons between different autonomous vehicles (AUV, ASV and UAS) can be valuable to
evaluatethedegreetowhichthedataacquiredbythesetechnologiescanbecomplementary.Simultaneous
deploymentsofvarioussystemsmayhelptoestimatedetectionprobabilitiesofagivensystembyusingthe
otherplatformstoconduct“trial”-baseddetectionprobabilityestimation.Thiswould,however,demandahigh
degreeofcontrolovertheconditionsinwhichtheequipmentisdeployed,butitwouldbeneverthelesshighly
relevantforagreaterunderstandingofthecapabilitiesofallthesesystems.
8.3.5.2 AUV/ASV
SeveralPAMandAAMsystemshavealreadybeencombinedwithseveraldifferentautonomousvehiclesand
shown themselves capableof collectingdata.Which technologiesmightbeused ina trialwouldverymuch
dependonthespecificaimsofthattrial.Unlesslogisticalandsafetyconstraintscanbeovercome,itseemshighly
unlikelythatAUVorASVwillbedeployedinthevicinityofaseismicsurveyvesselforreal-timemitigation.For
populationmonitoring, the choice of both vehicle and sensorwould be governedby the specific species of
interest, the geographic area, the water depth, local current strengths, shipping density, are the obvious
questionswhichwouldneedtobeansweredbeforeselectingaplatform,buttherearealsootherfactorssuch
as theexpectedanimalencounter rateand theprecision requiredof the studywhichmightaffectplatform
choice. Given PAM systems’ inability to distinguish some species groups (e.g. accurately separating dolphin
whistlestospecies),itwouldbenecessarytodeterminewhatlevelofspeciessubdivisionwouldberequired.
Answerstothesequestionswouldguidethetypeofsensorrequired,thedurationforwhichmonitoringmaybe
requiredandthenumberofvehicles(orlisteningstations)thatshouldbedeployed.
AUVandASVmayalsobesuitabletools forsoundmeasurement,particularly inremoteareaswheredata is
hardertoobtain.UnderwaterbuoyancyglidersandtheWaveglideraresuitableforthistaskifavesselcandeploy
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themnearby.Alternatively, theAutonaut inparticularwouldbewell suited to this taskdue to longmission
durationandindependence.
Table5.Recommendedtechnologytypesthatmightbeusedinfuturefieldtrialsforthedifferentmonitoringtypes.Themonitoringtypesarefurthermoredividedintomitigationmonitoringinareaseitherclearfromorbusywithotheroperationalgearortraffic,short-term(hours,days)orlong-term(weeks,months)monitoringandfocal-followsconductedwithstaticormobilesystems.L-t-a=Lighter-than-airaircraft.
Monitoringtype
Mitigation Population Focal-follow
Sensor Vehicle clear busy short-term long-term static mobile
Electro-optical Poweredaircraft ALL ALL
Bramorc4Eye,
BramorgEO,
BramorrTKFulmar,
Jump20,PenguinB
andScanEAgle
NONEALL–Dependenton
thesensortechnology
Motorisedgliders NONE NONE NONE NONE NONE NONE
Kites ALL NONE ALL NONE ALL ALL
L-t-aaircraft ALL NONE ALL ALL ALL ALL
PAM
Powered
AUV NONE NONE NONE NONE
Dependsonspeedof
animalsandvolume
forwhichtrackingis
required.
ASV
C-Worker,C-
Enduro.
Scoping
aheadof
source,
specificallyfor
LFdetections.
NONEC-Worker,C-
Enduro.NONE
Self-
powered
AUV NONE NONESlocumand
Seaglider
Slocumand
Seaglider
ASV NONE NONE Waveglider Waveglider
Drifter NONE NONE DASBR NONE
AAM
Powered
AUV NONE NONEALL–Dependentonthespecificspeciesofinterest,the
geographicarea,thewaterdepth,localcurrentstrengths,
shippingdensity,theexpectedanimalencounterrateand
theprecisionrequiredofthestudyandpossiblymore.
e.g.Bluefin9/9M,A9MandREMUS100forshallowand
mediumwatersandshortdurationsurveys;Bluefin12Dand
REMUS600fordeepwaterandlongdurationsurveys;
Bluefin21,A18DandREMUS6000forverydeepwatersand
longdurationsurveys.
ASV
ALL*
*see
population
monitoring
NONE
Self-
powered
AUV NONE NONE
ASV NONE NONE
Drifter NONE NONE All NONE
8.3.6 Datagapsandrecommendations
This section provides a description (sections below) and summary (Table 6) of the data gaps and
recommendationsidentifiedinthisreport.Oneoverarchingrecommendationdirectlyconnectedtothisreview
istheestablishmentofacomputerbasedexpertsystemthatusesthevastamountofinformationgatheredin
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this review and the preceding review on low visibilitymonitoring techniques as published in Verfuss et al.,
(2016).Thisexpertsystemshouldbebasedontheevaluationcriteriaestablishedinbothreviews,andshould
providealistofthemostappropriatesystemsandmethodsasanoutputbasedonthemonitoringrequirements
fedintotheexpertsystem.Acost/benefit-analysistoolcouldalsobeimplemented.Thissystemwouldneedto
bebasedonalivedatabasethatwillbeupdatedonaregularbasistokeeptheinformationfromthisrapidly
developingfielduptodate.
8.3.6.1 UAS
Asnewsystemsarise,additionaltestingisrequiredtofurthervalidatethequalityofthedataacquiredduring
unmannedaerialsurveys.Side-by-sidecomparisonswithcurrentmonitoringmethodsstillrepresentalargedata
gap, which are of high relevance particularly when considering the replacement of current methods.
Additionally,theamountofinformationprovidedbyUASproducersislimited,whichrestrictstheinformation
providedinthisreport.However,evenwithlimitedinformation,itisalreadypossibletoseethatthesystems
haveahighpotentialforapplicationtooffshoremonitoringofmarinemammals,turtles,andfish.
Thoughthesystemsshowgreatpromise,thedevelopmentofautomaticdetectionalgorithmsapplicabletothe
variety of data gathered using UAS, is one of the key areas that requires further development. Detection
algorithmsneedtobeadaptabletothevaryingenvironmentalconditionstowhichtheyareapplied,andthe
numberoffalsedetectionsneedstobewellquantified(andminimisedforreal-timemitigationpurposes).Real-
timedatatransfermayimprovedatafilteringandacquisition,butitremainsdependentonahumanobserver
toverifyananimaldetection,whichmaynotalwaysbeeasyiftheaircraftisflyingathighspeed.Finally,there
isatrade-offbetweenthedegreeoffalsepositivesandtheprobabilityofmissingadetection–amoresensitive
algorithmwillallowahigherdegreeoffalsepositivesbutwithalesssensitivealgorithmthereisahigherchance
ofmissingadetection.Foranyparticularapplication,thebalancebetweenthesemaydifferdependingonthe
consequenceofmisseddetections–forexampleinreal-timemitigationthereislikelytobemoreofaneedto
implementamoresensitivealgorithmthanforpopulationmonitoring.
Asplatformsandsensorsvaryintheircapabilities,itwouldbeworthassessingthepotentialuseofeachtypefor
specificapplications.Forinstance,testingthedifferenceindataqualitybetweenvideoandIR,orstill-photoand
IR,particularlywhenmonitoringlargeareasandpresenceofsmallspecies.Thedetectionofmarineturtlesand
largefishshouldalsobeunderfocus,sincemostmonitoringstudiesusingUASinvolveeitherterrestrialmapping
ormarinemammaldetections.
ThoughtherehasbeenanincreaseintheuseofUASinmarinemammalresearch,relativelyfewstudieshave
reported their effects onmarine animal behaviour (Smith, 2016). Altitude plays a key role in assessing the
potentialeffectsofUASonmarineanimals,butithasnotyetbeenestablishedwhetherthereisadistinction
betweendisturbancesfromnoiseversusvisualdisturbanceasafunctionofaltitude(Smith,2016).Asaltitude
decreases,itismostlikelythatthetriggersforbehaviouralresponseschangefromacoustictobothacousticand
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visual ones. For cetaceans in general, a behavioural disturbancemay be triggered during low altitude UAS
operations.Whencomparedtomannedaircraft, severalstudieshavedocumentedthatUAScauseasmaller
degreeofdisturbanceandavoidancebehaviour(Acevedo-Whitehouse,2010;Mulaca,2011;Moreland,2015).
ThisispossiblyasthenoiselevelsproducedbyUASarefarbelowthosefrommannedaircraft(Jones,2006;van
PolanenPetel,2006;NMFS,2008;Hodgson,2013;Goebel,2015;Pomeroy,2015).Overall,asUAStechnology
improvesweare likelytoexpectafurtherspike inUASoperationswithanapplication inanimalmonitoring,
particularly inmarineregions. It is therefore relevant tounderstandtheeffectsof this technology itselfand
comparedwithcurrentmonitoringmethodsforvariousspeciesandlocationsbeforeitisacceptedtoreplaceor
complementothermethodsusingaprecautionaryprinciple.
The evaluation presented here provides a glimpse at how UAS may efficiently collect valuable data and
transformoperationsfortheoilandgasindustry.ThevalueofUASforreducedriskandincreasedcostavoidance
isvisibleacrossthetestsconductedovertimeandtheprogressofthistechnology.LowerUASoperatingcosts,
for example, allow more frequent aerial inspections resulting in enhanced awareness and more proactive
maintenance.AsindustriesattempttofullyunderstandthebenefitsofemployingUAS,theyrequirethorough
planningtoutilisethebesttechnologytomeettheirneeds(i.e.takeinconsiderationregulatoryandtechnology
updatesthatmayaffectdataacquisitionandanalysis),andeffectivelyharnesstheincreasedvolumeofdatato
makeinformeddecisions(Gibbens,2014).
8.3.6.2 AUV/ASV
Thereareanumberofplatformsandsensorsatahighstateoftechnologicalreadinesswhichcanbeusedfor
thecollectionofPAMandAAMdatafromautonomousplatforms.Withalittleeffort,manyofthesesensors
couldbe combinedwithmanyof theplatforms and they couldbe sent out to collectmonitoringdata. The
problemsthatlimittheuseofthesesystemsalsooftenapplytosurveysconductedfromshipsinthatitisdata
interpretationratherthandatacollectionwhichisthefundamentallimitation.Theseproblemscanhoweverbe
moreacuteonsmallerlowerpoweredautonomousvehiclesthantheyareonshipsforanumberofreasons.
For PAM, the current trend is to deploymore sophisticatedhydrophone arrays that can at least determine
bearingstosoundsources.Fastmovingvesselscanalsousetargetmotionanalysistodeterminerangetosome
species, and the possibilities of combining visual and acoustic methods on the same platform can help to
understandoveralldetectionprobability.ThedevelopmentofPAMsystemsforautonomousplatformsthatcan
employmoresophisticatedarraysthereforerequiresconsideration.
AlthoughthedevelopmentofdetectionalgorithmsisfurtherdevelopedforPAMthanforvideoandAAM,in
mostsituations,thereliabilityofthesealgorithmsmeansthathumanobserverscontinuetoplayanimportant
roleindataanalysiswhetheritbeformitigationorpopulationmonitoring.Whiledetectionandclassification
algorithmswill always continue to improve, itmust be understood that theywill never be perfect and are
unlikelytoeverprovidesimplered/greenstop/gotrafficlightlikesystemsformitigationinmostsituations.This
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fundamentallimitationshouldbetakenintoaccountaswemoveforwardwithdevelopingthesesystemsand
whilewewouldnotsuggestthatfurtherresearchintoalgorithmsforautomaticdetectionisnotworthwhile,
research into both themagnitude and the effects ofmis-detection andmis-classification and investment in
systemswhichmakeitpossibleforhumanstocontinuetoplayaroleindecisionprocessesisprobablyofhigh
importance.
AAMfuture researchshould focusondevelopingdetectionalgorithmsanddatacompression techniques for
returningsummarisedand/orrawdataoverlowbandwidthcommunications.Simultaneousapplicationofthe
differentsensortypesmayhelpquantifyfalsedetectionratesandestimatedetectionprobabilitiesfordifferent
platform/sensorcombinationasdiscussedinsection8.3.5.2andasoutlinedinVerfussetal.(2016).Asforall
platforms,well-annotateddatasetsthatcanbeusedtotestdetectionandclassificationalgorithmsareessential.
TheperformanceofthedetectorsusedwithAUVandASVwillalsoneedtobetested/proveninthepresenceof
localindustrialorambientnoisesources.
TheuseofPAMsensorsformarineanimalmonitoringinrecentyearshashighlightedtheneedforcontinued
behaviouralresearchtobetterunderstandthecontextsinwhichanimalsproducesound(Marques,2013).This
iskeytoinferringanimaldensityfromacousticdata.Optimalsurveydesignforpassiveacousticsurveysisalso
anongoingareaofresearch(Marques,2013)but,asmentionedinSection8.3.5.2,theseinvestigationswillrely
onspecificsurveyscenariosinwhichtotestdifferentconfigurationsofplatformsandsensors.
Ingeneral,behaviouralinformationonmanyanimalspeciesformsahugedatagap.Therefore,auxiliarystudies
onanimalbehavioursuchasthevocalproductionrates,groupsizes,divingbehaviourorsurfacingrateswillhelp
toquantifydetectionprobabilitiesofthetargetspeciesandreducethesurroundinguncertainties.
8.3.6.3 Operationalaspects
Health and safety is the highest priority with regards to the operation of autonomous vehicles. Technical
reliabilityforthesesystemsisakeyissueforanycraftthatisconsideredformarineanimalmonitoring,andnot
allautonomousvehicleshaveaproventrackrecordoftheirtechnicalreliability.Anyconsideredcraftshould
thereforebetrialledextensivelytoensuretheirreliabilityduringoperationtoensuresufficientsafety.
FurtherpromotionoftheHSEprocedureswouldbethedevelopmentofanindustry-wide“codeofpractice”for
operation of autonomous vehicles in industrial operational fields to ensure safe interaction with other
marine/airspaceusers.Therearemanyissuesconcerningtheregulations,whichdifferamongcountriesandlack
internationalharmonisationonbothstandardsandregulations.Thiscancreatesomeresistancefromcurrent
airorwaterspaceusersandpublicapprehension.Thisshould,therefore,befurtherdevelopedinordertocreate
asmoothertransitionto,andacceptanceof,autonomousvehicleuse.Therefore,onerecommendation is to
constructaframeworkgoverningrulesandregulationsthatareflexibleandamendabletothespecificsofan
industrialprojectandtotherapiddevelopmentofautonomousvehicles.Thisframeworkshouldallowforthe
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rangeofapplicationabilitiesofautonomousvehicles.Afurtherstepwouldbetheinternationalharmonisation
onbothstandardsandregulations.
Further research into “detect-and-avoid” systems for autonomous vehicles is another step towards safe
operation of autonomous vehicles. There are, for example, safety and regulatory issues associated with
operatingaUASincivilairspace,whichneedtobeaddressedbytheaviationandairspaceauthorities inthe
countrieswhereunmannedsurveysaretobeconducted.Thelackofsystemswithsee-and-avoidtechnology
thatwouldpreventtheUASfromcollidingwithanaircraftlimitstheabilitytoconductstudiesinremoteareas
andisalsooneofthemainconcernsraisedbyaviationauthorities.Themandatoryinclusionofsee-and-avoid
technologyinUASisunderdiscussionatthemomenttohelpachievetechnologyacceptanceandsafety.
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Table6.Shortlistofrecommendationswithmedium(M)orhigh(H)priorityandwithmedium(M)orhigh(H)urgency,alongwiththerelatedtopicanddatagap.
Topic Recommendation Priority Urgency Datagap/issue
ALL
Studies comparing MMO/PSO, PAM, UAS, AUV, ASV and tagging to assess the
capabilitiesofeachsystems,determinetowhichextenttheymaycomplementeach
other,andprovideaccuracyinanimaldistributionandbehaviourstudies
H M
Need for assessment of technology
capabilities in relation to all monitoring
methods
AAMFocus on developing detection algorithms and data compression techniques for
returningsummarisedand/orrawdataoverlowbandwidthcommunicationsM M
Detection algorithms and data compression
techniquesnotwelldeveloped
PAMDevelopment of PAM system for employing sophisticated arrays for bearing and
distancemeasurementM M Limitationindatainterpretation
PAMTestingofperformanceofsystemsinthepresenceoflocalindustrial/ambientnoise
sourcesM M ApplicabilityofPAMinanoisyenvironment
Behavioural
data
Improve knowledge of target species behaviour through dedicated behavioural
studiesH H
Multiplierssuchasgroupsize/callproduction
rates are often important in population
monitoring and also relevant for mitigation
monitoring.
Operational
safetyExtensivetestingofsystemstoensurereliabilityduringoperation H H Reliabilityofsystemsoftennotknown
Operational
safety
Developmentofanindustry-wide"codeofpractice"toensuresafeinteractionwith
othermarine/airspaceusersM M
No"codeofpractice"existing for interaction
between autonomous vehicles and other
marine/airspaceusers
Operational
safety
Constructionofaframeworkgoverningrulesandregulationsthatareflexibleand
amendable to specifics of industrial project and development of autonomous
vehicles
M M
Regulations differ among countries, lack
international harmonization on both
standardsandregulations.
Operational
safety
Research into collision risk and integration of "detect-and-avoid" systems into
autonomousvehiclesforsafeoperationofautonomousvehiclesH H
Integration of "detect-and-avoid" systems
oftennotgiven.
Sensors Improvementofalgorithmsfor(real-time)detection,classification,localisation M MDetectionalgorithmformarineanimalspecies
notalwaysimplemented,lackofclassification
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Topic Recommendation Priority Urgency Datagap/issueof many species, localisation option may be
missing
Sensors
Identification of the magnitude and effects of miss-detections and miss-
classifications.
Simultaneousapplicationofdifferentsensortypestoquantifyfalsedetectionrates
andestimatedetectionprobabilitiesfordifferentplatform/sensorcombinations
H HFalsealarmratesandrateofmissesnotwell
known.
UAS
Furtherstudiesfortechnologytestinginoffshoreregionsbefore,duringandafter
seismicoperation.Comparisonwithcurrent(manned)methodsinassessinganimal
distributionanddensityinareasofinterestforthepetroleumindustry
H M
Technology capabilities in relation to current
aerial and ship-borne mitigation monitoring
methodsnotknown
UAS
UsecurrentavailableUASdatatodevelopversatiledetectionalgorithmsthatcanbe
used both during real-time transmission and for post-processing of the data
acquired.
H HAutomaticdetectionalgorithmsgenerallynon-
existingoratbasiclevel.
UASDevelopmentofreal-timedetectionsystemsandsupportforfurtherdevelopment
ofexistingreal-timedetectionsystems.H H
Hardly any real-time detection systems
existing.
UASInvestigatefurthercapabilitiesofUASthatcanbecombinedwithsurveillance,such
asdeploymentofotherequipmentatseaM M
Need for further investigation of the
capabilitiesofsensorandaircraftsystems
UAS
Compare different sensor systems for species such as fish and marine turtles,
tackling both the testing of sensor types and need for more knowledge about
sensitivespeciesinregionsofinterestforthepetroleumindustry.
M MNeed for further investigation of the
capabilitiesofsensorsystems
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9 Further information 9.1 Requirementsofautonomousvehiclesformarineanimalmonitoring
Theoceancanbeahostileenvironmentandtheuseofautonomoussystemswillnotonlydecreasetheriskto
surveypersonnel,butalsoenablesurveyingofhard-to-reachstudyareas,suchasareasaffectedbyicecover
(Dickeyetal.,2008).Theuseofautonomousvehiclesmaybemorecost-effectiveandabletogeneratemore
robustsurveydataandresultsthantraditionalshipandaerial-basedsurveys.Dependingonthesensorsused
withanautonomousplatform,round-the-clockmonitoringmaybepossible,incontrasttomannedsurveysthat
collectvisualsightings,whichcanonlybeconductedindaylighthours.Theuseofautonomousvehiclescreates
opportunitiestostudytheverticalvariabilityintheworld’soceans(usingdivingAUVsystems),whiletraditional
visual survey methods can be ineffective at detecting small aggregations of animals that spend significant
periodsoftimesubmergedandoutofviewattheseasurface(Baumgartneretal.,2013).Ingeneral,autonomous
systemshavegreatpotentialtoimprovethespatialandtemporalcoverageofmarineanimalsurveys(e.g.Van
Parijsetal.,2009).
Thereareseveralexistingexamplesoftheuseofautonomousvehiclesformarineanimalsurveys,whichare
summarisedinsection9.2aswellasTable7andTable8.However,itisimportanttoassessthesetechnologies
andconsiderwhethertheinstrumentsarecompatiblewithexistinganimalsurveymethodologies,orwhether
newsurveyingmethodsarerequired.
Thereisawiderangeofautonomousvehicles(outlinedinsections7.2),thus,theircapabilitiestocollectsuitable
data formarineanimalmonitoringwill vary. This sectionwill provideageneraldiscussionofmarineanimal
monitoring, identifying key features of survey design, data collection and data analysis which need to be
considered inrelationtousingautonomoussystemsasmonitoringplatforms.Thesefeatureswillbeusedto
assessthepotentialsuitabilityofthevariousautonomousvehiclesformonitoringmarineanimalsfortheO&G
industry.
Thekeyconsiderationswhenassessingwhetherautonomoustechnologiesaresuitableformitigationmonitoring
and surveying marine animals for both population-level and individual-level studies, can be split into the
followingtopics:
• Surveydesign
• Datacollection
• Dataanalysis.
In this section, each topic will be discussed in relation to autonomous technologies to understand which
capabilitiesanautonomoussystemneedstocovertoberegardedassuitableforacertainmonitoringtask(see
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alsosection8.3.1).Examplesonthecurrentuseofautonomoussystemsusedformarineanimalmonitoringcan
befoundinsection9.2.
9.1.1 Mitigationmonitoring
9.1.1.1 Surveydesign
Intermsofmitigationmonitoring(section7.1.1),thesurveyneedstobedesignedsothatthemonitoringensures
a timely detection of a target animal within the monitoring zone. Continuous real-time or near real-time
monitoring is required to achieve a timely detection so that the time between a target animal entering a
monitoring zone, its detection and the subsequent mitigation decision is sufficient to enable an effective
mitigationcascade(seeVerfussetal.,2016forfurtherdiscussion).
It is also important that the probability of detecting a target species is high. Key to maximising detection
probabilityresults inthechoiceoftheappropriatesensortechnology.Electro-opticalsensorscombinedwith
aerialvehiclescanonlydetectanimalspresentabove,at,orclosetotheseasurface(anexceptionisincaseof
thermal IR,which cannot detect sub-surface animals), PAM sensors combinedwithAUV / ASV require that
animalsvocaliseandthatthosevocalisationsaredistinguishablefrombackgroundnoiseinordertobeableto
detectthem,andAAMsensorsonlyworkunderwater.Anextensiveevaluationofwhichtargetspeciesisbest
detectedwithwhichsensortype,andthe influenceofenvironmental factorsonthedetectionprobability, is
giveninVerfussetal.,(2016)andwillthereforebeomittedinthisreview.
Asecondconsiderationistheresolutionofthespatialandtemporalsurveycoverageofthemonitoringzone.
Thecoverageneedstobesuchthatthechancesofdetectingananimalpresentwithinthemonitoringzoneare
maximised.
Theabilityofasystemtoclassifyand/orlocaliseananimalisalsouseful.Classificationand/orlocalisationisnot
necessarily essential for mitigation monitoring but will decrease the likelihood of mitigation processes
conductedbasedonfalsealarms.Theabilitytoclassifydetectionssothattargetspecies(orspeciesgroups)can
bediscriminatedfromnon-targetspecieswilldecreasetheamountoffalse-detections.Regardingthelevelof
classificationanddetailrequiredaboutagivendetection,inmanysituations,speciesidentificationiseithernot
requiredatall,or isonlyrequiredtothe levelofbroadspeciesgroups(e.g.adolphinora largewhale).The
sensortypesincludedinthisreviewhaveclassificationabilitiestosomedegree.Anextensiveevaluationofthis
matterisgiveninVerfussetal.,(2016),andwillbereconsideredinthediscussionofthisreview(section8.3.1).
Theabilitytolocaliseatargetanimalwillalsodecreasetheamountoffalse-detections,giventhatmitigation
measures will only be taken when a target animal is present in themonitoring zone. Animals outside the
monitoringzoneshouldeithernotbedetected,oritshouldbepossibletolocalisethemwithsufficientaccuracy
tobesurethattheyareoutsidethezone.Localisationdependsontheconfigurationofasensorsystem.Electro-
opticalsystemswillneedtohave(ataminimum)referencepointstoallowthemonitoringzonebordertobe
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identifiedontherecordedimages.Inmostcircumstances,singlehydrophonePAMsystemscannotdetermine
range.Smallarrays,asmightbedeployedfromasinglevehicle,candeterminebearingtosoundsourcesandfor
some species,multiplebearings fromdifferentpoints along the trackline canbeused to estimate range. In
certainwellunderstoodpropagationconditions,rangecanbedeterminedusingmultipatharrivalsofsignals(e.g.
surfaceandbottomechoes)ordispersivemodepropagationmodels.MostAAMsystemshaveexcellentrange
determination capabilities and high angular resolution. Further discussions on this matter can be found in
Verfussetal.,(2016).
9.1.1.2 Datacollection
Forrealtime,ornearreal-timeapplications,suchasmitigationmonitoring,considerableamountsofdataneed
tobetransmittedtoanoperatorifaccuratedecisionsaretobemade.Inalimitednumberofcases,thishas
been possible using low bandwidth satellite links where summary information or short sound clips from
automaticdetectorsaresenttoshoretobecheckedbyahumanoperator.Formostoperationalscenarios,the
quantities of data that need to be presented to an operator are currently too large to send through low
bandwidth.However,forshortrangecommunication,freespectrumwirelesssystemscanbeusedtosendhigh
bandwidthdataoverrangesofseveralkilometres.Acousticprocessing isanactiveareaof researchand it is
possiblethatforsomesituationstheamountofdatathatneedtobesentinorderthataneffectivemitigation
decisioncanbemadecouldbesignificantlyreduced.
Formitigationmonitoring,themostpracticaloptionisthereforetouseafreespectrumradiomodemlinkto
senddatatoanearbybasestation(whichmightbeononeoftheoperationalvessels)whereanoperatorcan
viewandverifydetectiondata.
9.1.1.3 Dataanalysis
Formitigationmonitoringitisadvisabletohaveatrainedhumanoperatortocheckdetectionsidentifiedbya
system’s detector before mitigation procedures are implemented. Detectors can be used to reduce data
volumesthatneedtobestoredortransmitted,butnonehaveyetbeensufficientlyvalidatedasbeingcapable
ofaccuratelymakingacompletedecisionconcerninganimalpresencewithahighdegreeofcertainlyandlow
falsealarmrate.Detectorperformancemayalsobecomepoorinthepresenceofnoise(industrial,biological,
weathergenerated,etc.).AutomaticdetectionandclassificationisgenerallymoreadvancedforPAMthanfor
AAMandopticalmethods.However,therearenosystemsofanytypewheredetection issoautomaticthat
automaticdetectorscanberelieduponontheirown.
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9.1.2 Animalpopulationmonitoring
9.1.2.1 Surveydesign
Asoutlinedinsections7.1and11.6,surveydesignisanimportantfeature,firstly,tobeabletoinferdensity
and/orabundanceinthestudyareaand,secondly,fordetectionprobabilityestimation.Therefore,autonomous
vehiclesthatcanadheretodesignedsurveytransectlines,orremainstationarytocreateamonitoringpoint,
wouldbeparticularly suited for acquiring surveydata. Technical termsused in this sectionareexplained in
section11.6.
Ifpilotstudyinformationisavailableontheexpectedencounterrateintheregion,theamountoflinelengthor
thenumberofpointsneededtoachieveanacceptablelevelofvarianceintheresultscanbeestimated(Buckland
etal.,2001).Formovingsystems,thepowerandspeedofthesystemwillneedtobeconsideredtoensurethat
it is logistically possible to complete the survey in the required timeframe. Deployment duration and
communicationrangewiththegroundstationshouldbeofsufficientlengthtoallowforsuccessfuldeployments
fromandreturnstolandorasurveyvessel(asnotedbyKoskietal.,2009ainareviewofUAS).
If systemswere tobeusedas stationarymonitoringpoints then, ideally,enoughsystemswouldneed tobe
available toachievesimultaneousmonitoringatallpoints. Itwouldbepossible to split thesurveyarea into
smallerblocks tosurveywith fewersystemsatanyone time,but thentheability to teaseapart spatialand
temporalfactorsthatmayaffectanimaldensity/abundancewouldbereducedorlost.
Duringthesurveydesignstage,everyeffortshouldbemadetokeepthenumberofmultiplyingparameters(or
“multipliers”-seesection11.6forexplanation)requiredtoestimateabsoluteanimaldensityorabundancetoa
minimum.Foreveryextraestimatedmultiplier,suchasdetectionprobability,additionaluncertaintyisaddedto
theanalysis, increasing theoverall varianceestimates. Furthermore, there is thepossibility thatamultiplier
estimate may be biased (e.g. an incorrectly applied call production rate), which may result in biased
density/abundanceestimates.Therefore,anidealdesignwouldbeonewhereallanimalsinthemonitoredarea
were certain to be detected and individual animals could be counted (so no group size or cue production
multiplierwouldberequired,forexample).Detectionprobabilityestimationmaynotalwaysberequired,ifit
canbeassumedthatallavailableanimalswithinasurveyedtransectorpointaredetected(asmaybethecase
inUASsurveyswhererecorded imagesarecollectedandanalysedpost-survey).However,mostpopulation–
level surveyswill require some formofmultiplier so substantial effort should bededicated to planning the
optimalsurveygiventhestudyarea,thestudyspeciesandlogisticalconstraints.Itisimportanttonotethatthe
surveydesignrequiredtoachievecertain,orashighaspossible,detectionprobabilitiesforreal-timedetection
withtheaimofmitigationmonitoringwillbedifferentfromthesurveydesignforpopulationsurveys(Verfuss,
etal.,2016).
Foreveryrequiredmultiplier,anappropriateestimationapproachshouldbeplannedatthesurveydesignstage,
andmayinvolvethecollectionofauxiliarydata.Probabilityofdetectionshouldbeestimatedusingoneofthe
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“standard”densityestimationmethods,suchasdistancesamplingorSECR,sothedesignofthesurveyshould
ensurethatappropriatedataaregeneratedfortheseanalyses(i.e.,estimatedhorizontalrangestoanimalsfor
lineorpointtransectsampling,orassociatingthesameacousticeventsacrosshydrophonesorre-identifying
individuals across surveying occasions for SECR). Particularly forUAS technology, implementingmethods to
accountformissinganimalsdirectlyonthetransectlineoratthepointduetonon-constantavailabilityshould
be considered (seeChapter11,Bucklandet al., 2015 fora recent reviewofmethods toaddress this issue).
AltitudeandspeedoftheUASwillbeofimportancewhendeterminingtheavailabilitybias;thetemporaland
spatialfieldofviewoftheUASmaybetoonarrowtoallowaseconddetectionofananimalwhenresurfacing,
soalternativeapproachesmayhavetobeconsideredfordeeperdivingspecies.InthecaseofAUVtechnologies
thatcollectpassiveacousticdata,itislikelythatcallproductionrate,orproportionoftimeanimalsarevocally
active,willberequiredasamultiplier.Theexactmetricwilldependontheexactformofthedensityestimator.
Therefore,anassessmentoftheavailabilityofsuitabledatafromtheexistingliteratureneedstobeconducted,
orassociatedbehaviouralstudieswillneedtobeplannedaswellas themainsurvey.Giventhatbehaviour-
linkedmultipliersarelikelytobecontext-specific,itisrecommendedthatmultipliersareestimatedatthesame
timeandplaceasthemainsurvey(Marquesetal.,2013a).
Furthermore, if it is suspected thatmodel-based inference (see Section11.6.2)will be required toestimate
abundanceinthewholestudyarea(e.g.ifthechosenvehiclemaydeviatefromitsplannedtrack),ordensity
surfacemodellingisrequired,thenappropriatecovariatedatasetswillhavetobeobtainedfromotherexisting
datasetsorcollectedfromthestudyarea.
GiventhenumberofavailableAUVandUASplatforms,itispossiblethattheremaybeseveraloptionsatthe
surveydesign stage.Different combinationsof systemand sensormaybe suitable for the samesurvey. For
example, smaller animalsor animalswith little surface contrast caneitherbedetectedwithhigh-resolution
camerasmountedonaUASoperatingatahigheraltitudeandspeed,orwithlowerresolutioncamerasoperated
bysmallerUASflyingataloweraltitudeandspeed.Thesizeoftarget,lightconditions,seastate,operational
altitude, speed and contrast between target and background are also important factors to consider when
determiningwhich imaging system is required for an operation. All survey design optionswill likely involve
differentprosandconsthatwillbesurvey-specific.
In order to assess animal population sizes, real-time detection is not required. However, there would be
advantagestosystemsthatcouldrelaydatabackinreal,oralmost-real,time.Forexample,surveydesignscould
beupdatedoradapted,dependingonwhatspeciesweredetected–thismaybeveryusefulifmultipleAUV/
ASVinstrumentsweredeployedtoformanarray,whichcouldhavealteredspacingdependingonthetypesof
speciesbeingacousticallyencountered.
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9.1.2.2 Surveydatacollection
Regardlessoftheparticularmethodologicalprotocolthatasurveymayfollow,therearebasicdatacollection
requirements foranysurvey.Firstly,a temporalandspatial recordof thesystem’ssurvey route isessential.
Secondly,systemsshouldcollecteithervisualoracousticdatathatcanbeusedtoidentifyspecies.Inthecase
ofUASsurveys, stabilizationof thecamera,especiallywith regard topitchand rollof theaircraft,hasbeen
identifiedasbeingimportant,particularlywhenusingvideoforthedetectionofmarinemammals(Koskietal.,
2009a).AUVandASVsystemsthatuseon-boardprocessingofacousticdataoughttokeepexamplesofrawdata
sothatfalsedetectionproportionscanbeassessed.
Clearly,whateversystemisused,inmostsituationsitisdesirabletostoreandpresenttoacompetentoperator
asmuch rawdataaspossible if accuratemanagementdecisionsare tobemade.For long termmonitoring,
considerableamountsofdatacanbestoredonhighcapacitymemorycardsorharddrivesand it isonlythe
lowest power and long duration vehicles (such as submarine gliders) that require significant real-time data
reductionforthisapplication.Ourrecommendationfor longtermmonitoringistostorealldata,orasmuch
dataasispracticallypossibleandtosendaminimalamountofdatatoshoreprimarilyasasystemstatusand
healthcheck.Thedetaileddataprocessingcanthenbeconductedoncethesystemhasbeenrecovered.
9.1.2.3 Surveydataanalysis
Thedevelopmentofalgorithmstodetectmarineanimalsautomaticallyfromaerialimagesisanareaofactive
research (Ireland et al., 2015). Image processing for fisheries stock assessments is also being investigated
(Mellody,2015).Therefore,atpresent,thevastmajorityofrecordedsightingsdatawillbemanuallyanalysed
byanoperator,eitherinnearreal-timeorafterthevehicleisrecovered.Inanyautonomousvehiclesurvey,the
particularabundanceestimationmethodologyusedwilldeterminewhatanalysesarerequired.Forexample,
estimationofhorizontalperpendiculardistanceforlineorpoint-transectsampling,individual(re)identification
forspatially-explicitcapture-recapture(SECR)andgroupsizeestimation(seesection11.6forfurtherdetails).
Where multiplier estimation analyses are required, such as group size estimation, collected data can be
subsampledifsamplesizesareprohibitivelylarge.However,asystematicrandomsubsample(i.e.,evenlyspaced
sampleswitharandomstartpoint)ofthedatamustbetakensothatthesubsampleisatruerepresentationof
thefulldataset.
Thedataanalysisconsiderationsoutlinedabovearenotsignificantlydifferentfromcurrentconsiderationsfor
collecting visual or acoustic data for wildlife surveying. However, the differences of UAS/AUV platforms to
currentlyusedplatforms(i.e.,ships,aircraft,ormooredacousticinstruments)meanthattherearesomedata
analysisconsiderationsrelatingtoautonomousvehiclesthatarecurrentareasofresearch.Thesearediscussed
here,thoughdefinitiveconclusionsand/orrecommendationsarenotyetavailable.
Thefirstissuerelatestosurveyeffortandanimalmovement.Typically,surveyeffortisdefinedaslinelength
travelled for line transect surveysand time spentmonitoring forpoint transect surveys.However, it canbe
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considered that slowmovingplatforms,although theymovealong transect lines, areessentially completing
point transect surveys,where thepointmoves slowly through space. The slowmovement of platformshas
implicationsfor(1)howsurveyeffortisdefinedinthedensityestimatorand(2)whetheranimalmovementmay
beproblematicornot(Glennie,2015). Ifaplatformmovesconsiderablyslowerthanthestudyanimals(or is
stationary),anybiascausedbyanimalmovementcanbeavoidedbyeithercountingcues(e.g.acousticcues,or
visualcuessuchaswhaleblows)ortreatingthesurveyasaseriesof“snapshot”momentsthroughtime.The
lattermethodworksbyestimatingdensityinshortperiodsoftime(i.e.,asnapshot)whereanimalmovementis
deemed tobeminimal (e.g.Ward,2012;Hildebrand,2015). Surveyeffort is thendefinedas thenumberof
snapshotsusedintheanalysis.Currentresearchisinvestigatingtheeffectofsnapshotwindowlengthondensity
estimation using data from slowmoving autonomous vehicle, specifically gliders and drifting platforms. In
addition,workiscurrentlyongoingtodeterminehowmuchdeviationfromaplannedsurveyisneededbefore
model-basedinferenceisrequiredtoestimateabundanceinthewholestudyarea.
9.1.3 Focalanimalstudies
Inthecaseoffocalanimalstudies,surveydesign,datacollectionandanalysisneedtobespecificallytailoredto
thestudyobjectives.Wethereforehighlightsomegeneralkeyconsiderationsinthissection.
Autonomousvehicleswillneedtobeeither(1)mobileinordertofollowanindividualofinterest(viamanualor
automaticpiloting)or(2)remainstaticwithafieldofviewsuchthatananimalcanbemonitoredforaperiodof
time.Whenusingamanuallypilotedsystem,real-timedetectionfeedbackisnecessaryforthepilottotrackthe
animal.Automaticpilotedsystemswouldneed tohaveadetect-and-track routine thatallows forautomatic
trackingofadetectedanimal.Mobilesystemswouldadditionallyneedtohaveageo-referencingsystemthat
allows thedeterminationof the target animal’s location.Anassociated time-depth recordermayalsobeof
interestforobtainingdive-profilesoftheanimal.
Duringthefocalfollow,detectionprobabilityshouldremaincertain,sothatacompleterecordoftheanimal’s
behaviourduringthefocalstudyisrecorded.This ismoredifficulttoachievewithPAMsystems,becausean
animalisonly‘observable’whenitisvocalising.Theabilitytoperformfocalfollowswillalsobelimitedbythe
communicationsystemandtheoperationalrangeofthesystem.
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9.2 Currentknowledgeonautonomousvehicles
9.2.1 Independentsystems
9.2.1.1 Unmannedaerialsystems
UAS have recently been introduced as alternative platforms to overcome the limitations of manned-aerial
surveys formarinestudies,with theultimatebenefitbeingtheirability toperform"dull,dirty,dangerous10"
tasksmoreefficientlyandatafractionofthecostofmanned-aerialsurveys(NeiningerandHacker,2011).These
systemshaveevolvedrapidlyoverthepastdecade,aprocessdrivenprimarilybytheirvarioususesinmilitary
operations. Only recently have they become available for civilian and scientific uses, for instance for earth
sensingreconnaissanceandscientificdatacollection(Wattsetal.,2010).Recentdevelopmentsthathaveturned
present-dayUAS into realisticalternatives tomanned systemsare longer flightdurations, improvedmission
safety,flightrepeatabilityduetoimprovedautopilotsystems,andreducedoperationalcostswhencompared
tomannedaircraft.Advances inquadcoptersystemshaveproventoreducetheneedforextensiveoperator
trainingandoperatingdifficultiescomparedwithfixed-wingaircraft,particularlyinflightswithshortrangesthat
mayrequirelowerspeedsandaltitudes.Theactualadvantagesofanunmannedplatform,however,dependon
manyfactors,suchasaircraftflightcapabilities,sensortypes,missionobjectives,andthecurrentUASregulatory
requirementsforoperationsoftheparticularplatform(outlinedinsection9.4.1)(Wattsetal.,2010).
UAShavebeenwidelyusedfordifferentpurposesbothforresearchandindustrysurveys.Someofthework
developedinresearchhasincludedstudiessuchasmeteorologicaldata(e.g.FunakiandHirasawa,2008),sea
icemonitoring(e.g.Inoueetal.,2008),andwildlifemonitoring(e.g.Hodgsonetal.,2010).Shellhasconducted
a few studies in offshore Arctic regions in the last decade, some showing the full potential of this type of
equipmentforindustrialoperations(Koskietal.,2009a;Lyonsetal.,2006).Additionally,differentstudieshave
beenconductedtotestindividualsystemsandsensorsinawaytobetterunderstandtheircapabilities(Table7).
Inoffshoreareas,therehasbeenanintensefocusonseaicemonitoring,pipelineinspectionandwildlifesurveys
withUAS,whichreducestherisktohumanpersonnelandarecompletedatafractionofthecostcomparedto
theuseofmannedaerialplatforms.TheavailabilityofcommercialandsmallUAShasalsopromptedtheuseof
thistypeofequipmentinresearch,withoutrequiringlargebudgetsorinstructedpersonnel.However,withthe
continuousdevelopmentofplatformsandsensorsthecapabilitiesofthesesystemstomonitoroffshoreregions
anddetectmarineorganismsevolve.
10Dull as marine mammal surveys can be considered dull – spending hours looking for an animal either from plane or ship, most of the time not seeing anything. Dirty as fieldwork can be dirty – sea sickness or plane sickness, few clothes, maybe no access to shower and clean toilet conditions, bad weather and sea salt can affect equipment and log sheets, etc. Dangerous because if there is bad weather at sea it can easily affect navigation and risk people's lives.
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Inrecentyears,UASoperationshavebeenintegratedintoavarietyoffieldstudiesinvolvingarangeofspecies
and applications of UAS, including the use of UAS to deter birds from feeding in commercially important
agricultural areas (Grimm et al., 2012),monitoring habitat and biodiversity loss (Koh andWich, 2012), and
monitoringillegalpoachingactivities(Olivares-Mendezetal.,2013;Mulero-Pázmányetal.,2014;Smithetal.,
2016).UndertheMarineMammalProtectionAct(MMPA),NOAA'sNationalMarineFisheriesService(NMFS)is
challengedwithdevelopingfurtherresearchandstockassessmentsforbothcetaceansandpinnipeds,andhas
beendevelopingaUASprogramto improvescientificapplications.Mostofthepublishedresearchprograms
andactivitiesareontheapplications,capabilities,andinventoryofUAStechnology.However,thereremainsa
missing component on the application of these systemswith amore industrial approach focusing on their
benefits inmanagement issues and their integration with other autonomous technologies (coordination of
unmannedvehiclesaerial,surface,andunderwater,asacommunicationnetwork).
ThereareavarietyofUASsystems,includingremotelycontrolledaircraft(e.g.operatedbyapilotataground
controlstation)andaircraftthatcanflyautonomouslybasedonpre-programmedflightplansormorecomplex
dynamicautomationsystems,suchasforsearchandrescuewheretheaircraftisprogrammedtofindanobject
oritscueinaspecificarea.Theycanalsoincludevarioustetheredsystemsthatareeitherstationaryorstationary
relative to amobile anchor point. The great potential of UAS systems liemainly in their comparative cost
effectivenesscomparedtomannedaircraft,butalsostemfromthefactthatthesesystemsaregenerallysafer
andoperationallysimplertouse.Theirusefulnessformarine-basedsurveyswilldependgreatlyontheirflight
capabilities(speed,range,altitude)andontheirpayloadcapacityinrelationtosuitablesensorpackages(e.g.
cameras, thermal sensors) (Campoyetal.,2009).Payload includeseverything that isnotpartof theaircraft
structureitself,rangingfromthefuelandbatterytypetothedifferentcamerasanaircraftcancarry.Thoughit
isoftennotspecified,itisgenerallyassumedthatthepayloadassociatedwithUASrepresentssolelytheweight
oftheimagingequipment.However,thisshouldbespecifiedbythemanufacturers,assomemaydefinetheir
payload as all the components that are external to the aircraft and others may only include the imaging
equipment.
Severalstudieshavebeenconductedtotestthecapabilitiesofdifferentaerialsystems,suchaspoweredaircrafts
(e.g.Hodgsonetal.,2013;Koskietal.,2009b),kites(Fraseretal.,1999;Vorontsova,2015)andlighter-than-air
aircrafts(e.g.Flammetal.,2000;Hodgson,2007).Eachsystemmayhaveadifferentapplicability,dependingon
theweatherconditionstowhichitisexposedandthepayloadsensorsthatitcancarry.Whiletheunderstanding
oftheadvantagesandlimitationsofpoweredsystemsformarinemonitoringhasincreasedinrecentyears,there
is alsoan interest inexploring theapplicabilityof kitesand lighter-than-air aircraft, since these systemsare
quieter and therefore potentially less disturbing to animals. Furthermore, they can be considered more
environmentally friendly than fuel powered systems. However, there is much less information about the
performanceofsuchsystemsinmarineapplications,andnocomprehensiveoverviewofthedifferentsystems
andtheirapplicationtomarineorganismmonitoringhasbeencarriedoutuptodate.Theextenttowhichthese
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systemscanprovideusefulscientificoroperationaldataiscurrentlyuncertain,andtheuseofUASformonitoring
andmitigationpurposesinrelationtomarineorganismsisthereforeverymuchatanearlyteststage.
ThereisawiderangeofdifferentUASwithsizesassmallas18grams(PD-100byProxdynamics)to14,000kg
(GlobalHawkbyNorthropGrumman).However,thecomplexityofthelargersystemsrequiresalotofpersonnel
to operate andmany of the systems are only available formilitary use. Larger platformsmay also require
runwayssimilartothoserequiredbymanned-airplanes,whichinturnlimitsthepossibilityfordeploymentsat
sea.Smallerplatforms,ontheotherhand,mayhaveastrongersensitivitytoenvironmentalconditions,andare
limitedinthepayloadweighttheycancarry.Tetheredsystemsmayovercomesomeoftheseproblems,butin
turnarelimitedintheiroperationalrange.
9.2.1.2 AutonomousUnderwaterandSurfaceVehicles
AUVandASVareunmanned,self-propelledvehiclesthatcanoperateindependentlyforperiodsofafewhours
toseveralmonthsorevenyears.AwidevarietyofAUVandASVarenowavailableforpurchaseandhire.These
range from relatively small vehicles,which can be lifted by one or twopersons and deployed froma small
inflatable,tolargediesel-poweredsurfacevesselswithbespokelaunchandrecoverysystems(Griffiths,2002).
Thesmallervesselsareoftenoperatedwithahighlevelofautonomyandcanstayatseaforseveralmonthsat
atime.Thelargersurfacevehiclestendtobemorecloselycontrolled,althoughthisrestrictionismostoftenfor
regulatoryandsafetyreasonsratherthanafundamentaloperationalconstraint.
Many AUV’s and ASV’s were initially developed for military applications (e.g. mine sweeping, battle space
preparation)andlongtermacademicsurveys.TheyhavealsobeenextensivelyusedinO&Goperationssuchas
forsubseaequipmentinspections,leakdetection,dynamicpositioning,etc.Itistimelytoconsidertheuseof
AUVandASV in theenvironmentalcommercialsector,specificallyoilandgasoperations.Key issuesarethe
vehicles’deploymentduration,healthandsafetyconcernsassociatedwiththehazardsofrefuellingatsea,and
deployment, recovery and control of the vehicles in adverse conditions. Autonomous vehicles may have
particularadvantagesinareaswheretheycanbeeasilydeployedandrecoveredfromland.However,forlong-
termdeploymentatseainvolvingmaintenancerefuelling,morefrequentdatacollectionandsupportstaffmay
berequired,introducingarangeofcomplexlogisticalproblems.Additionally,deploymentriskismoresignificant
inbusyareasofhighmilitary, shippingor fishingactivity,due toacoustic interference,collision riskandnet
entanglement (Wynnetal.,2013).However, thepotentialofAUVandASV is clearlygiven in theirability to
acquiredataininaccessiblepartsoftheoceanatshortnotice(giventhathiringvesselsorsurvey-planesmay
require long lead times) and provide improved temporal and spatial resolution of a broad range ofmarine
measurements.
Inthenavalsector,nicheapplicationshavebeenidentifiedtogivecleardevelopmentalguidanceoncraftand
sensortyperequirements.Intheresearchworld,applicationaimsremainmuchmoreopen.Scoresofplatforms
(particularly ASV) exist in the developmental stages and trialling tends to remain geared toward system
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improvementasageneralaiminitself.Withintheindustrysector,veryfewcraft(particularlyASV)appeartobe
genuinelyavailablewithcommerciallyviableapplicationsstillbeingestablished.Incomparison,AUVareslightly
moreadvancedcommercially.Thoughthereisclearpotential,thecombinationofcraftandsensor(s)thatmeets
anicheiscurrentlyinthepioneerstage.ThepresenceofASVinthemarketiscertainlygrowing,especiallyas
theyareincreasinglyrecognisedasavalidalternativetoAUVinmanyapplications.RelativetoAUVhowever,
regulatory uncertainty are a constraint to the wider use of ASV (see section 9.4.2 for further details). The
relationshipbetweenASVandAUVisalsocomplementaryhoweverandthereisgrowinginterestintheuseof
ASVtosupportAUVinnavigationandasadatarelay/transmissionstation.
Many surface and underwater autonomous vehicles are used on a regular basis for the collection of
oceanographic data by research institutions world-wide (e.g. Figure 11). Primarily, they are used for the
collectionofoceanographicdataand thedetectionofhigherorganisms, suchasmarinemammals and fish,
generallyremainsanicheareaofresearch.SeveralpublishedpapersdescribefieldtrialsofAUVandASVfor
environmentalandoceanographicapplications(summarisedinTable8),butmostofthepublishedworkfocuses
ontechnicalaspectsofautomatednavigationandcollaborativeperformanceofAUV.
Figure11.TheUKNationalOceanographyCentresAutonomousVehicleFleet11
AUVandASVarecapableofcarryingavarietyofsensors fore.g.seabedmappingandacoustic relateddata
collection,including,butnotlimitedto,Multi-BeamEchoSounders,Sub-BottomProfilers(SBP),side-scansonars
(SSS),magnetometers, geochemical instruments, imaging systems (HDcameras),oceanographic instruments
(Conductivity-Temperature-Depthunits(CTD),echosounders,AcousticDopplerCurrentProfilers(ADCP),water
11 http://www.seebyte.com/wp-content/uploads/2014/09/NOC_019.jpg
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samplers and passive acoustic data acquisition systems (PAM); Wynn et al., 2013). The sensors deployed
determinethealtitude(depth),speedandenduranceofthevehicle.Forexample,higherpowersensors(SSS
andSBP)reduceendurance,highresolutionseafloorimaging(HDcamera)requireslowaltitudes(Wynnetal.,
2013).Fernandesetal., (2003) investigatedtheapplicationofAUVforfisheriesacoustics.Formarineanimal
monitoring;passiveandactiveacousticsensors(PAMandAAM)havebeensuccessfullyintegratedintoAUVand
ASV,asdumb(recordingonly)andsmart(real-timeprocessing)sensors,andreportspublishedoftheirfindings
(Table8).However,withintheexistingliterature,noreviewhasbeenfoundthatevaluatesfieldtrialsofAAMor
PAMformarinemammalsasinstalledinautonomousvehicles.
PAM systems have successfully been deployed on buoyancy gliders for the detection of baleenwhale calls
(Baumgartner et al., 2008; Baumgartner et al., 2013; Baumgartner et al., 2014b; Baumgartner, 2014;
BaumgartnerandFratantoni,2008))andforbeakedwhales(Klincketal.,2009;Klincketal.,2012;Klincketal.,
2014).Baumgartneretal.,(2014a)usedaDMONdetectormountedonaSlocumElectricGlider,programmedto
detectthetonalvocalisationsofseveralbaleenwhalespecies.TrialswereconductedofaDecimusPAMsystem
onanSV2wavegliderinthespring2014offthecoastofScotland,successfullydetectingharbourporpoiseclicks,
dolphinwhistles,spermwhalesandseismicairgunnoise(D.Gillespie,pers.Comm.).Harbourporpoiseswere
alsodetectedusingamodifiedDMONsystemonasubmarinegliderofftheSWoftheUK(Subergetal.,2014).
The British based companyMOST trialled their own wave-powered vehicle (Autonaut) in 2014, which was
equippedwithashortstreamerarray,althoughunfortunatelythishadinsufficientbandwidthforthedetection
ofcetaceans(D.Gillespie,pers.Comm.).
Thelevelofreal-timereportingfromtheseinstallationsvariedconsiderably.Baumgartneretal.,(2013)were
abletosendsufficientdatatoshoretodetectthepresenceofbaleenwhales innearrealtime.Klincketal.,
(2012)sentsummarydataforeachdivetoshore,butahighfalsealarmratemeantthatitwouldhavebeen
difficulttousethesedataforreal-timedecisionmaking.ThesystemdescribedinSubergetal.,(2014)hadno
real-timereportingcapability. Inmostcases,onlyasinglehydrophonewasdeployedoneachdevice,so the
vehicleswereunabletoprovidelocalisationinformation.TheexceptiontothisisastudydescribedbyFucileet
al., (2006),whoreportonadeploymentwhere threeSlocumgliderswereequippedwith timesynchronised
hydrophones.Thegliderswereprogrammedtomaintain stationapproximately5 to7kmapartand timeof
arrivaldifferenceswereusedtolocaliseanimals.PAMsystemshavealsobeensuccessfullydeployedfromboth
profilingandsurfacefloats.Walletal.,(2012;2014)describestudiesinwhicharecordingdevicemountedona
SlocumgliderwasusedtodetectfishsoundsoffWest-centralFlorida.Severalspeciesofsoniferousfishwere
detectedaswellassomeunknownsoundsbelievedtobeofbiologicalorigin.Matsumotoetal.,(2013)describe
aPAMsystemdesignedtodetectbeakedwhaleswhichusesthesameacoustichardwareasdescribedbyKlinck
etal., (2012)attached toanAPEXprofiling float12. Lowcost surfacedriftersarealsounderdevelopmentby
12 http://www.webbresearch.com/apex.aspx
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researchersatNOAAfisheries(Griffiths,2015).Duetotheirrelativelowcost,profilingfloatsanddriftershave
thepotentialtobedeployedinlargenumbers,howevertotallackofnavigationalcontrolmaylimittheiruseto
regionswithspecificcurrentregimes.PAMsystemshavealsobeenincorporatedintoanumberofsmallsurface
vehiclesincludingtheLiquidRoboticsWaveglider,theASVC-EnduroandtheMOSTAutonaut,howevernopeer
reviewedliteratureyetexistsdescribingthesedeployments.
We are not aware of any studies which have collected PAM data from powered underwater vehicles. The
expense,shortmissiondurationandnoiseoutputfromthesevehicleswhichcanbothmaskPAMdetectionsand
affect the behaviour of marine organisms has led the research community to show little interest in these
platformsforPAM.
AcousticreceiversforthedetectionofacoustictagshavebeenintegratedintobothunderwaterglidersandAUV
(Eileretal.,2013;Haulseeetal.,2015).Therearealsoanumberofarticlesonlinethatdiscusstheintegrationof
acoustictagsfortrackingfishandothermarinelife131415.
AAM have already been integrated into underwater gliders and ASV (Table 8) and have focused on using
echosounderstodetectzooplanktonratherthanfishandotherlargerorganisms,althoughwehaveincluded
theminthisreviewasproofofconcept.AAMisusedfrequentlyinfisheriesresearchtoprovidefishbiomassand
distributionestimates (e.g. herring, bluewhiting,mackerel, sardines andanchovy;Doray, 2012;Huseet al.,
2015).For thepurposesof this review,wehave identifiedthe followingchallenges for their integration into
autonomousvehicles:
(1) Transducersizebalancedwithbeamwidthbalancedwithrequiredfrequency.Forexample,onlyhigh
frequency(120kHz)widebeamangle(10degrees)areusedonsmallautonomousplatformssuchas
ImagenexES853onSeaglider(Guihenetal.,2014),
(2) Powerconsumption(mosthullmountedtransducerstransmitat0.2to2kWatt),and
(3) Datatransmissionforreal-timedecisions(eventhesimplesingle-beamechosounderrawdataistoo
largetotransmitbackoverIridium).Currentlyacousticprocessingisnotundertakenonboard,butthis
couldbeanareaforfuturedevelopment.
AAMhavealsobeenusedtodetectseveraldifferentspeciesofmarinemammal,however,thesestudieshave
notbeenconductedwithAUVorASV,butratherwithAAMsensorsdeployedfromvessels,fixedstructuresor
intanks.WeincludeexamplesofthisworkheretodemonstratethepotentialoftheAAMsystemsformarine
mammal(andotherlargeanimal)detection:greywhales(LucifrediandStein,2007),finwhales(Nøttestadet
al.,2002),humpbackwhales(Love,1973),bowheadwhales(Pycetal.,2015),rightwhales(MillerandPotter,
13 http://www.futurity.org/glider-fleet-to-track-fish-in-real-time/ 14 http://news.uaf.edu/underwater-gliders-may-change-how-scientists-track-fish/ 15 http://www.gizmag.com/wave-glider-sharks-stanford/23758/
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2001),bottlenosedolphins(Au,1996),duskydolphins(Bernasconietal.,2011),spinnerdolphins(Benoit-Bird
and Au, 2003b; Benoit-Bird and Au, 2003a), killer whales (Knudsen et al., 2008), West Indian manatees
(Gonzalez-Socoloskeetal.,2009;Gonzalez-SocoloskeandOlivera-Gomez,2012),greyseals(Hastieetal.,2014),
harbour porpoise (Hastie, 2012). A wide range of different AAM sensors have been used in these studies
including:theSimradSA950,EK60andEK500,theHumminbird987cSI,theTournamentMasterFishfinderNCC
5300,theKongsbergSM2000,theCodaOctopusEchoscope2andtheTritechGemini.AAMhasalsobeenused
to detect shark species including bull sharks, greatwhite sharks and tiger sharks (Parsons et al., 2014) and
baskingsharks(Lieberetal.,2014)usingtheTritechGeminiandtheReson7128sensorsystems.Therearealso
afewexampleswhereAAMdeviceshavebeenusedtodetectandinvestigatethetargetstrengthofvarious
turtlespecies(Mahfurdzetal.,2015;Perez-Arjonaetal.,2013;MahfurdzandHamzah,2014;Mahfurdzetal.,
2013;DavyandFenton,2013).
9.2.1.2.1 Autonomousunderwatervehicles
AUVhavebeenprimarilyappliedinseabedimagingfortopographiccharacterisationandhabitatassessmentof
benthicspecies(e.g.Williamsetal.,2010),environmentalmonitoring,hydrographicsurveys,anddetectionand
density estimation of zooplankton (Brierley et al., 2002) and fish schools (Fernandes and Brierley, 1999).
Fernandes and Brierley have also carried out studies on the avoidance response of krill and fish to vessels
(Brierleyetal.,2003;Fernandesetal.,2000).
Inthecommercialsector,sitesurveyingandgeo-hazardassessmenthavebeentheprimaryuseofAUVs.Other
typical commercial applications of AUV are pipeline monitoring, oceanographic surveys and water quality
assessment, geophysical surveying and coastalmapping. Environmental and oceanographic applications are
beginningtocrossoverintothecommercialsector,inwhichASVhaveprimarilybeenutilisedinbathymetricand
environmentalsurveying(Cacciaetal.,2009).Semi-submersiblemodelshavebeenusedforbathymetricand
hydrographic surveys inmilitary, civil and commercial applications (Wolking, 2011). Self-powered crafthave
beenusedformeteorologicalandoceanographicdatacollectionaswellassomeenvironmentalmonitoringin
boththeresearchandcommercialsectors(Cacciaetal.,2009).
For naval purposes, AUV have been utilised formine sweeping, battle space preparation, harbour security,
submarine detection and ISR (Intelligence, Surveillance and Reconnaissance). They have been deployed for
harboursecurity,minesweepingandalsousedasordnancetargets.Theseapplicationshavefrequentlybeen
basedonremotelyoperatedsurfacecrafts(e.g.ASV’sC-WorkerandTargetfamilies).Militaryinterestforself-
poweredcraftisunknown,buttherewouldappeartobescopeforasurveillanceapplication
ForAUV,asGPSreceptionislostduetotheantennabeingsubmerged,alternativesolutionsfornavigationare
requiredforpositioningunderwater.AUVnavigateusing1)arraysofacousticbeacons(transponders)onthe
seafloor for Long Baseline (LBL) communication, or 2) a combination of Ultra Short Baseline (USBL)
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communications,GPSpositioningandinertialnavigation,applyingdeadreckoningwhenbelowthesurfaceusing
combinedinformationfromdepth,inertialandDopplervelocitysensors(Wynnetal.,2013).
Propelled AUV originate from the modern torpedo, a self-propelled projectile driven by compressed air,
inventedbyRobertWhiteheadin1866.Whitehead’storpedotravelledataspeedof3m/s,coveringdistances
ofupto700m.ThefirstAUVwastheSpecialPurposeUnderwaterResearchVehicleorSPURV,developedby
the Applied Physics Laboratory at the University of Washington in 1957, used to study diffusion, acoustic
transmissionandsubmarinewakes.Workdoneinthe1960scomprisedtheinitialinvestigationsonpossibleAUV
applications. In the 1970s there were important advances in the development of the technology, and the
experimentationwithprototypesnoticeablyincreasedoveracoupleofdecades.Inthe1990s,experimentation
movedintoafirstgenerationofAUVaimedtoperformdefinedtasks.TheproliferationofAUVmodelsledto
theexistenceofaround75AUVworldwidein2001(Fernandesetal.,2003),whenthefirsttrulycommercial
productsbecameavailable(Blidberg,2001).
Autonomousunderwaterbuoyancyglidersarerevolutionisingthewaythemarineenvironmentisinvestigated,
monitoredandassessed(Stommel,1989;Davisetal.,2002;Griffithsetal.,2007).Theyenableobservationsto
bemadeinregionspreviouslyinaccessibletovessel-basedinstrumentse.g.beneathpolaricesheets(Sagenet
al.,2008;Sagenetal.,2009;Jonesetal.,2011a),orinhurricanesandtropicalstorms(Milesetal.,2015),and
continue to operate in weather conditions (both visibility and sea state) that would limit vessel-based
observations (Milesetal.,2013).Theyprovideextremelyhighverticaland lateral resolutiondata thatmake
observations of oceanmechanisms at scales that are not resolved using conventional measurements from
vessels(Heywoodetal.,2014;Kaufmanetal.,2014).Theyarearelativelyinexpensivetechnology,withthecost
oftheplatformanditsdailyrunningtypicallyordersofmagnitudelessthanthatofvessels(Davisetal.,2002).
Frequentlythisenablesmultipleplatformstobedeployedsimultaneously,significantlywideningthespatialand
temporalresolutionofthesurvey(Rudnicketal.,2004).Comparedwithmanyothervesselstheyareacoustically
quiet(WoodandMierzwa,2013),makingthemanidealplatformtomeasurebackgroundnoise(Baumgartner
etal.,2014b).Finally,whilstfrequentlyusedforopenoceanresearch,theyaretypicallysmall,lightweight,and
deployablebyasmallrigidhulledinflatableboat(RHIBor“RIB”)withacrewofonlytwopeoplemakingthem
highly versatile (Davis et al., 2002). In the last ten years gliders have moved from being experimental,
developmentalplatforms,toformingkeycomponentsofroutinecoastalobservationalnetworks(Schofieldet
al.,2007;Schofieldetal.,2010).Theirapplicationindefence,industryandpolicysectorsisalsobeingexamined
(Wynnetal.,2013).
Theabilitytoreceiveinstructionstoadjustoperationalparametersandtransmitdatasetsquicklyisakeyfeature
ofunderwaterbuoyancygliders.Nearlyallglidersuselow-earthorbitsatellitecommunications(andinparticular
theIridiumsatellitephonesystemforbi-directionalworldwidecommunication),severalalsohavethecapability
touseradiofrequency(RF)LocalAreaNetwork(lineofsightRFmodem),CircuitSwitchedCellularandacoustic
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modem communication. ARGOS16 is commonly used as an emergency back-up locator. ARGOS transceiver
modulesaremountedonnear-polarlow-orbittingsatellites.Atthepoles,eachsatellitepassesapproximately
twice a day (equal to 28passes), at the equator there are 6 to 7 passes in total. Location estimates for an
instrumentrequireaminimumoftworeceivedmessagesduringasinglesatellitepass.Duetothelownumber
ofsatellitepassesatlowlatitudes,gapsofafewhourscanoccurwherepositioninformationisnotpossible.All
systems,exceptacousticmodems,aresubjecttolossofperformanceinhighsea-states(Davisetal.,2002).The
amountofdataandcommunicationundertaken isabalancebetweenthebandwidthof thecommunication
method,thecostofdatatransferand,withtheexceptionofacousticmodems,thetimetaken.Whenatthe
surfaceagliderisnotincontrolofitsnavigationandthereforebothsusceptibletooceancurrentsandcollisions
withothervessels.
Eitherwaypointsorheadingdirectionareusedtodirecttheunderwaterglidermissionandglidersaresenton
transects(e.g.Piterbargetal.,2014)ortoactasavirtualmooring,profilingupanddownatasinglelocation
(e.g.Smeedetal.,2013).Whencurrentspeedsarelessthanthegliderspeed,aglidercanperformrepeated
profileswhilemaintainingatanearlyconstanthorizontalposition(Rudnicketal.,2004).Theprimarynavigation
systemofunderwaterglidersusesanon-boardGPSreceivercoupledwithanaltitudesensor,adepthsensor
andanaltimetertoprovidedead-reckonednavigation.Asimplenavigationalgorithmisusedtocomputethe
headinganddiveanglerequiredtoreachadesiredlocationfromthecurrentposition.Themajorityofglidersdo
not compensate forwater currents; the Seaglider is unique in its ability to estimatewater currents using a
Kalmanfilter(Benderetal.,2008).Inregionswheresurfaceaccessischallenging(asaresultoficeorheavyship
traffic), alternative means of location are acoustic base-line or geophysical-aided navigation methods or a
surfacevesselshadowingtheAUV(ClausandBachmayer,2015).TheuseofRAFOS(RangingandFixingofSound),
typicallyanetworkofsea-floormountedbaselinetranspondersasreferencepointsfornavigation(anacoustic
base-line),hasbeensuccessfullyimplementedintogliders(Jonesetal.,2011b;DAMOCLES).Thegeophysical-
aidednavigationmethodsproposedbyClausandBachmayer,(2015)involveterrain-aidednavigationusingthe
glidersdead-reckoningnavigationsolution,theon-boardaltimeterandalocalDigitalElevationModel(DEM-
3Drepresentationofaterrainssurfacecreatedfrombathymetricdata).
Themajorityofbuoyancyglidersarecurrentlybatterypowered,andcarryanincreasingvarietyofsensorsof
interesttooceanographers(Rudnicketal.,2004).Theseincludephysicalvariables(e.g.pressure,temperature,
salinity,currents),noise(e.g.background,ambient,ships,marinemammalcalls,fish),chemicalvariables(e.g.
dissolvedoxygen,CDOM,nitrate,pH)andbiologicalrelevantvariablessuchasabundanceofphytoplanktonand
zooplankton(Rudnicketal.,2004;Davisetal.,2008;Guihenetal.,2014;Klincketal.,2012).Threetypesof
sensorscanbefittedtoglidersthathavethepotentialtomonitormarinemammals,turtlesandfish:passive
acousticmonitoring(PAM)sensors,activeacousticmonitoring(AAM)sensorsandanimaltags.
16 www.argos-system.org
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9.2.1.2.2 Autonomoussurfacevehicles
CivilianapplicationsofASVconsistbasicallyinbathymetricandenvironmentalsurveyandmonitoring(Caccia
etal.,2009).Militaryapplicationsincludeharboursecurity,coastalsurveillance,minesweepingandsubmarine
detection.ASVhaveundergonethemostappliedtriallinginthedefencesector.ThedevelopmentofASVwasin
apioneerstageby2006andtherearenowmanyoptionstochoosefrom(Caccia,2006;Cacciaetal.,2009).
PoweredASVresearchfocusesonmeteorologicalandoceanographicdatacollection,withbathymetryimaging
beingthemostcommonsurveyingactivity(e.g.Vanecketal.,1996).Otherimportantenvironmentallyoriented
studieshavebeenfocusedonautomatedfishtracking(Goudeyetal.,1998)andacquisitionofwatersamplesin
theseasurfacetopmicrolayerfortheanalysisofclimaticchanges(Cacciaetal.,2005).
Theautomatednavigation technologyused inmodern civilianASV,bothpoweredand self-powered,has its
origininthreeautonomousvesselscreatedbyMITduringthenineties(ARTEMIS,ACESandAutoCat)andthe
autonomous kayak SCOUT. These demonstrated the feasibility of automatic heading control, way-point
navigationbasedonDGPSandfullyautomatedcollectionofhydrographicdata(Cacciaetal.,2009).Powercan
bederivedfromavarietyofsources,includingdieselmotors,fuelcellsandbatterypacks.Electricalpowersupply
isgenerallypreferredinenvironmentalsamplingapplications,whichrequirenopollutionoftheoperatingarea;
diesel propulsion is preferred in long missions (e.g. coastal surveillance or Mine Counter Measure (MCM)
operations).Thesevehiclescantravelathighspeedsandhavethecapacitytocarry/towsignificantpayloads.
9.2.1.3 MissionPlanningandVehicleControl
Forsafetyandlegalreasons(seesection9.4)aircraftandlargesurfacevehiclesaregenerallynotoperatedout
oflineofsightofahuman“pilot”,althoughthereisnofundamentalreasonastowhytheycouldnotbegiven
greaterlevelsofautonomyiflegalandsafetyconstraintscanbeovercome.AdvancesinGPStechnology,and
nowAIS,havebeenkeytotherapiddevelopmentofpoweredaswellasself-poweredASVformanyresearch,
commercialandmilitaryapplications.ASVnavigationatthewatersurfaceallowsthemtorelayhighfrequency
transmissionsintheairandacoustictransmissionsinthewater(ASVcanbeusedtosupportAUVnavigation).
ASVareoutfittedwithGPSreceiversandattitudeandheadingreferencesystem(AHRS)sensors.AUV’sonthe
otherhand,canonlygatheraccurateGPSpositionswhentheycometothesurface.Otherancillarysensorsare
Dopplervelocitylogs(DVL),whichmeasurethefluidflow(currents)andallowforbettercontroloftrajectories.
Typically, in an autonomous vehicle, lower level actions (like rudder control) are automated and overall
behaviour(likewaypointselection)ismanagedbyanoperator.Fullautonomyisdesiredinresearchandcivil
applications;inmilitaryapplicationsremote-controlledvesselsarepreferred,andautomationisregardedasa
waytooptimisesystemperformance(Alves,2002;Caccia,2006).Variousoptionsofcontrolinclude:
• Controlinthehorizontalplane:fromsimpleheadingcontroltomorecomplextechniques.
• Trajectory tracking: Capability of the vessel to follow a time-parametrised reference curve in two-
dimensions(controlofthepositionofthevesselatspecificinstants),whichisespeciallychallengingin
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presenceofexternaldisturbance(waves,wind,currents).Timeconstraintsaremorerelaxedinpractical
applications.
• Path following: Capability of the vessel to follow a reference curve in two-dimensions, without
temporalconstraints.Adesiredtemporalspeedprofilewillstillbeapplied.
• Cooperative motion control: Capability of the vessel to adapt its trajectory in the presence of
automatedmarinevehicles. Interestingapplicationsare theuseofanASVasa communication link
betweenanAUVandasupportvessel(leader-followerproblem)orthecoordinationofmultipleASV
forhydrographicsurveying.
• Missioncontrol:AMissionControlSystem(MCS)allows theendusertoexecuteandsupervise the
progressofoneormultiplevehicles.Missionplanningandcontrol is limitedtothedefinitionofthe
trajectorybymultiplepointsandafewemergencyactions(e.g.abortandstop).
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Table7:ListofpublishedstudiesinvolvingUASwithapplicationinthemarineenvironment.
Reference Objective UASType Sensor Datatype Studyconclusions Country
Bevanetal.,
2015
Evaluatethe
effectivenessofalow-
costcommercially
availableUASfor
identifyingbothadultand
hatchlingseaturtlesin
near-shorewaters
adjacenttonesting
beaches.
DJIPhantom
1
quadcopter
GoProand
GPSenabler
Video TheresultsindicatethatthisUASsystemprovidesapracticalandeffective
methodofconductingdaytimesurveysinnear-shorewatersfor
monitoringseaturtleabundanceandmovements.Eveninturbidnear-
shorewaters,theywereabletomonitorthesurfacingofadultfemalesas
theyleftthenestingbeach.WhiletheUASwascontrolledfromshorein
thecurrentstudy,itcouldalsobelaunchedandcontrolledfromaboatfor
monitoringturtlesinoffshoreareassuchasforaginggrounds.
Mexico
Cameronet
al.,2009
NOAAtestsofaUASto
determineits
effectivenessfor
surveyingsubarcticpack
iceforiceseals.
ScanEagle
aircrafts
SingleLens
Reflex(SLR)
NikonD300
andvideo
Photoand
video
TheScanEagleperformedwellinavarietyofweatherconditions,andthe
imagescollectedhavethenecessaryresolutiontodistinguishthedifferent
species,ages,andoccasionallyeventhegenderoficeseals.
Beringsea
(USAand
Russia)
Durbanet
al.,2015
UASasanalternative
methodforsuccessfully
obtaining
photogrammetryimages
ofkillerwhalesatsea.
APH-22
hexacopter
(Aerial
Imaging
Solutions)
OlympusE-
PM2,
Olympus
M-Zuiko
25mmf1.8
lens
Videoand
photo
(triggered
manually
with12.3
MP)
TheAPH-22hexacopterhasgreatutilityforcollectingphotogrammetry
imagestofillscientificdatagapsaboutfreerangingwhales.Itissmalland
portablewithVTOLcapabilitythatenablessafedeploymentandretrieval
evenfromsmallboatplatforms,andenablesphotogrammetryinremote
locationswhereconventionalaircraftsareimpractical.Canbeflowninlow
altitudeswithoutdisturbingthewhales.Allowsdifferentiationofindividual
whalesusingnaturalmarkings,withprecisealtitudetoenablequantitative
measurements.
Canada
Hodgsonet
al.,2010
CoastalUAStestsand
mannedvsUAS-
differenttests.
Powered
Warrigul
SonyDigital
Recorder
Video ThecombinationofthetypicalUASimagingsystemweusedandthe
altitudestrieddidnotprovideimagesofhighenoughresolutiontoreliably
detectdugongsorwhales.
Australia
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Reference Objective UASType Sensor Datatype Studyconclusions Country
Hodgsonet
al.,2013
Coastalmonitoringof
DugongsusingUAS
Powered-
ScanEagle
SLRcamera
Nikon®D90
12
megapixel
Photo TheScanEaglewouldneedtofly2.8timesasmanytransectstoachieve
thesameareacoveragethatastandardmannedsurveycouldachieve.
Thislimitationcouldbeaddressedbyusingacamerathatcapturesimages
atahigherresolutionsothatonecoulduseawiderlensorflyhigher,or
onecouldusemultiplecameras.
Australia
Jonesetal.,
2006
Landandcoastal
assessmentofanUASfor
wildliferesearch.
MLBFoldBat CanonElura
2
Video AlthoughtheFoldBatUASsystemdidnotproveusefulasanoperational
wildliferesearchtool,ithadanumberofcharacteristicsthatilluminated
systemrequirementsforsucceedinggenerationsofUASdesignandmore
successfulUASmissions.TheElura2producedsatisfactoryimagesfor
wildliferesearchapplicationstargetingsmallandinconspicuousspecies
suchassmall,whitewadingbirds.However,theutilityoftheimageswas
limitedbecausetheywerenotinstantaneouslygeoreferenced.
USA
Koskietal.,
2009b
Comparisonoftraditional
offshoresurveywith
humansurveyorsvsaerial
digitalimagemonitoring.
Powered-
InsightA-20
Alticam400 Video UAShasthepotentialtoreplacemannedaircraftduringsurveysforlarge
cetaceansorlargegroupsofsmallcetaceansifthesearchareaissmall.
However,highervideoresolutionisneededbeforetheUASwouldbe
effectiveforsurveysoflargeareasorfordetectionofsmallercetaceans
andpinnipeds.
USA
Koskietal.,
2013
Comparisonoftraditional
offshoresurveywith
humansurveyorsvsaerial
digitalimagemonitoring.
Fixed-wing
manned
with
cameras
NikonD800
DSLR,video
CanonHD
XF100
HDvideo
and
DigitalSLR
photos
Theanalysessuggestthatimageryfromthesecamerasprovidessimilar
qualitydatatothosecollectedbyobserversflyinginanairplane,andthat
UASsurveyscanbeflowninsomeconditionswhenmannedaircraft
cannot,itappearsthatUAScouldreplacemannedaerialsurveysfor
marinemammals.However,thesamplesizesfortheseanalyseswerelow
anddidnotcoveralloftheconditionsthatwouldbeencounteredduring
mannedaerialsurveyssoadditionaltestswithlargersamplesizesand
coveringawiderarrayofconditionsarerecommended.
USA
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Reference Objective UASType Sensor Datatype Studyconclusions Country
Thamm,
2011
Equipmentevaluation. KiteSUSI62 Not
specified
TheSUSI62wasdesignedasarobustandsafeUASwithalargepayload
andlongflighttimefordaytodayapplications.Itscapabilitytooperate
differentsensors(e.g.optical,multispectralandthermal)atthesame
timeandtheeaseofswappingsensorsoffersfascinatingoptionsfor
researchandcommercialapplications.Theautopilotguaranteesthatthe
areaofinvestigationiscoveredcompletelywiththedesiredoverlapand
groundresolution.
Europe,
Africa
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Table8.ListofstudiesinvolvingAUVandASVwithpossibleapplicationinthemarineenvironment
Reference Objective AUV/ASVtype Sensor Datatype Summary Country
Ainleyetal.,
2015
Presence/
absence
Seaglider Imagenex
ES853
AAM AnechosoundermountedontheSeagliderwasusedtodeterminethedepth,
distributionandabundanceofprey(assumedtobekrillandfish).Thegliderwas
deployedfor78daysmakingobservationsofthephysicalandbiological
environment.ThepaperappliedthemethodofGuihenetal.2014.
Antarctica
Armstronget
al.,2006
Characterisethe
coralreefhabitat
Propellertwin-
hullAUV
Seabed
PixelflyCCD
camera
Image TheAUVSeabedequippedwithaCCDcameracapturedimagesevery2.5softhe
benthiccommunitiesthatinhabittheshelfcoralreefoftheHindBankmarine
conservationdistrict.Thedatawasusedtoprovideinformationonbenthicspecies
compositionandabundance.
St.Thomas
(USVirgin
Islands)
Baumgartner
etal.,2008
Acoustic
recordingsfrom
autonomous
platforms.
Slocumcoastal
gliders
custom
built
PAM
Obtainedgoodqualityacousticrecordingswithnodetectableflownoisewhile
profilingupordown.Glidernoisewasdetectableatthetopandbottomofeach
divepreventingdetectionofwhalevocalisationsatthesametime.
GreatSouth
Channel
Baumgartner
etal.,2013
Presence/
Absenceof
baleenwhales
Slocum DMON PAM Ahardwareandsoftwaresystemwasdevelopedtodetect,classify,andreport14
calltypesproducedby4speciesofbaleenwhalesinreal-timefromoceangliders.
Twoglidersreportedover25000acousticdetectionsandusedLFDCStoattribute
themtofin,humpback,sei,andrightwhales.Theglidersmadelotsofnoiseatthe
surface(surfacewaves,internalpumps,radiotransmissions),soDMONdetections
wereturnedoff.Detectionaccuracywashighforfinandrightwhales,modestfor
humpbackwhales,andundeterminedforseiwhales.Whalesweredetectedwith
thegliderandusedtodirecttheshiptolocatethem.
Atlantic
Baumgartner
etal.,2014b
Presence/
absence
Slocum DMON PAM ALowFrequencyDetectionandClassificationSystem(LFDCS)wasusedto
autonomouslydetectandclassifysounds,additionallyrawdatawassavedonthe
Arctic
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DMON.Bowheadwhales,walrus,beardedseals,belugawhales,airgunsandships
noiseweredetected.Real-timereviewofpitchtracksbyanalystsonshoreallowed
unambiguousidentificationofbowheadwhalecallsandairgunpulses.Theon-
boardclassificationsystemwaslesssuccessful;thiswasattributedtotheimmature
stateofthecalllibrary.
Binghamet
al.,2012
Performanceof
theWaveGlider
foracoustic
applications
WaveGlider DMON PAM Thenoisegeneratedbythewavegliderisverylow.Theambientnoiseatthe
submergedgliderissignificantlylowerthantheambientnoiseatthesurfacefloat.
Theseastatedoesnothaveastronginfluenceonthewaveglider’semittednoise.
Southcoast
ofOahu
Binghamet
al.,2012
Performanceof
theWaveGlider
foracoustic
applications
WaveGlider ITC-3013 AAM Thewaveglidermodemwasabletoreceivesignalsfromthetransmitteratdepths
of500and2,500mandatrangesinexcessof3km.Thereliabilityandtheinput
SNRvariedwiththelocalnoisefieldaroundthesurfacereceiver.
Southcoast
ofOahu
Bourgeoiset
al.,1997
Bathymetry
mapping
Semi-
submersible
ASV
SimradEM-
1000
AAM Thesemi-submersibleASVprototypeORCA,adiesel-poweredunmannedvehicle
withatallmastforairtakeandcommunications,wasusedtoacquirebathymetric
datawithamulti-beamechosounder.Thebathymetricdataisvalidatedand
correctedinrealtime,andusedbyavessel-basedoperatortore-adjustnavigation
waypointsandoptimisesensoroperatingparameters.Thismethodologyoffers
highcoverageandaccuracyfortheacquireddata.
Florida
Brierleyand
Fernandes,
2001
Fishsurvey–the
presenceof
divingbirds
changedthe
initialobjective
PropellerAUV
Autosub-1Simrad
EK500
AAM TheAUVAutosub-1wasequippedwithanupward-lookingechosounderforfishsurveysintheNorthSea.TheechogramsfromtheAUVcontainedverticaltraces
startingfromtheseasurfacecausedbydivingbirds,identifiedasNorthernGannets
byvisualobservations.Themeandivedepthwas19.7m,somewhatdeeperthan
expectedtootherstudiedgannets.Thedivingdepthshaveimplicationsonforaging
capabilitiesofgannets,sotheeffectiveverticalforagingrangeofthespeciesshould
bereconsidered.
Shetland
andOrkney
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Reference Objective AUV/ASVtype Sensor Datatype Summary Country
Brierleyetal.,
2002
Estimatekrill
densitiesunder
Antarcticice
PropellerAUV
Autosub-2Simrad
EK500
AAM TheAUVAutosub-2wasdeployedtoestimatekrillabundanceunderAntarcticice,
uptoadistanceof27kmbeyondtheiceedge.Krilldensitiesfivetimesgreater
thaninopenwaterwereregistered.
Antarctic
Ocean
Brierleyetal.,
2003
Assessavoidance
responseofkrill
toasurvey
vessel
PropellerAUV
Autosub-2Simrad
EK500
AAM TheAUVAutosub-2wasdeployedaheadtheresearchvesselJamesClarkRossintheAntarcticOceantoassesskrillavoidancetothevessel.BothJamesClarkandAutosub-2wereequippedwiththesamescientificechosoundertoassessthe
responseofkrilltothevessel.Thesimilarityofthedensitycalculatedwiththedata
acquiredbytheAUVandthevesselsuggestedthattheavoidanceofkrilltothe
JamesClarkwasnotimportantenoughtobiastheestimationofkrillabundance.
Antarctic
Ocean
Cacciaetal.,
2005
Samplesfrom
theseasurface
micro-layer
ASVcatamaran
SESAMOMultiuse
microlayer
sampler
samplers TheASVcatamaranSESAMOwasusedintheTerraNovaBayareaoftheRossSeabetweenJanuaryandFebruary2004tocollectdataandsamplesofthesea-air
interfaceinacompletelyautomatedmanner.Thecompositionofthesea-surface
microlayer(~1mm)providesvaluableinformationaboutthephysicalandchemical
interactionsbetweenthehydrosphereandatmosphere,andtheirrelationwith
climaticandenvironmentalchanges.
RossSea
Antarctic
Sea
Correaetal.,
2012
Natureofthe
waveformcoral
ridgesofMiami
Terrace
PropellerAUV
C-Surveyor-IISimrad
EM2000
Edgetech
120kHz
AAM TheAUVC-Surveyor-IIwassentona24.5hmissiontoacquire27km2ofhigh
resolutionseabedmaps(withmulti-beamechosounderandside-scansonar),sub-
surfaceprofilesandbottomcurrentdata(ADCP)tostudythenatureofthecold-
watercoralridges,withwaveformmorphology,foundatthebaseoftheMiami
TerraceintheStraitsofFlorida.
Miami
Terrace,
Straitsof
Florida
Davisetal.,
2008
Presence/
absence,
quantity
Spray Sontek750
kHzADP
AAM SprayglidersweredeployedacrosstheSouthernCaliforniaCurrentsystemand
collectedconcurrentmeasurementsoftemperature,salinity,ADCPshear,
chlorophyllafluorescenceand750kHzacousticbackscatter.Thelongtimeseries
andhighspatialresolutiondata(28months,acrossthreelinesovertwoyears)
characterisedcloselinksbetweenfrontsinphysicalandbiologicalvariables.The
ADPwascalibratedwithtungstencarbidespheres,sovariabilityinquantitative
USA
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measurementsis2dBbetweenmissions.Thestudyobserveddielverticalmigration
ofzooplanktonandmicronekton,butthesinglefrequencywasnotspeciesspecific
andthereforenotquantitative.
D'Spainetal.,
2004
Lowsignature
AUVforpassive
oceanacoustic
studies
Bluefin
PropellerAUV
not
specified
PAM Amid-sizeAUVisretrofittedwithavector-thrustsystemmanufacturedbyBluefin
Robotics,whichdecreasestheradiatedsoundlevelsbetween20and50dBinthe
20to10kHzband.Thelowacousticsignaturepermitstheuseofavehiclemounted
hydrophonearrayforpassiveoceanacousticstudies.
Not
specified
Fernandes
andBrierley,
1999
Testthe
capabilityofAUV
Autosub-1todetectfish
PropellerAUV
Autosub-1Simrad
EK500
AAM TheAUVAutosub-1equippedwithascientificechosounderwassentinto13missionstogatheracousticdatafordetectionanddensityestimationoffish
schools.TheabsenceofanavoidanceresponsebyafishschoolwiththeAutosub-17mfarfromitsuggestedthatfishisnotsensitivetothepresenceofthevehicle
beyondthatdistance.
NorthSea
Fernandeset
al.,2000
Assessthe
avoidance
responseoffish
toasurvey
vessel
PropellerAUV
Autosub-1Simrad
EK500
AAM Researchvesselsareusedfordensityandabundanceestimationoffish,andthere
isawidespreadconcernthatthenoisegeneratedbythesevesselsmaycausean
avoidanceresponseonfish,whichwillbiastheabundanceresults.TheAUV
Autosub-1wasdeployedaheadtheresearchvesselScotiaduringanacousticsurveyofherringintheNorthSea.TheAUVwasequippedwiththesameechosounder
modelastheScotiatoassess,bydatacomparison,theresponseoftheschoolas
thevesselgoespast.ThesimilarityofthedatafromtheAUVandthevesselleadsto
concludethatfishdonotavoidsurveyvessels.
NorthSea
Fernandeset
al.,2003
Fisheries&
plankton
acoustics
research
Autosub SIMRAD
EK500
AAM AUVcansamplepreviouslyimpenetrableenvironmentssuchastheseasurface,the
deepsea,andunder-seaice.However,advancesinpower-sourcetechnologyare
requiredtoincreasetherangeofoperationbeforeAUVaresuitableforthese
applications.
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Fratantoniet
al.,2011
Developafully-
integrated
autonomous
system
APEXfloat,
Slocumglider,
Z-Rayglider
DMON
PAM Trialshavebeenasuccess.Thesystemislow-noise,low-power,data-efficientand
reliable.IntheSanClementeIslandtrial,boththeglidersandthefloatswereable
tosendnumerousreal-timebeakedwhaledetections.
Fucileetal.,
2006
Time-stamped
baleenwhale
vocalizations
fromaglider
SlocumElectric
Glider
HTI-96-MIN
hydrophon
ePersistor
CF2Data
Logger
PAM
Thequalityoftheacousticdatarecordedwasverygood.Theglidermovementand
flownoisedidnotaffecttherecordings.Multipleglidersdeployedatoncemeant
thatthevocalisingwhalescouldbelocalisedtowithinafewhundredmeters.
Gulfof
Maine
Goudeyetal.,
1998
Kaya-shapedASV
usedtotracka
taggedwimming
animal
KayakASV Acoustic
transducer
inatracking
tag.
AAM Asmallkayak-shapedASV,25cmlongandpoweredbyanelectricbattery,was
usedtofollowataggedswimminganimal(fish)duringanentireday(24hours).The
smallsizeandelectricpowerwereselectedtominimisethenoiseoutputofthe
vehicle.
Not
specified
Grasmueck,
2006
morphology&
oceanographic
conditions
C-Surveyor
IITMAUV
KongsbergT
MEM1002
AAM ThedataobtainedbytheAUVwasabletoprovideinformationtofillthegap
betweenlow-resolutionsurface-basedmappingandvisualobservationsonthe
seafloor.
Straitsof
Florida
Greeneetal.,
2014
Fisheries
acousticstock
assessments.
WaveGlider BioSonics
DT-X-SUB
AAM AfleetofwavegliderscouldsurveytheWestCoastUSAEEZmuchfasterandmore
frequentlythanatraditionalfisheriessurveyvessel
WestCoast
USAEEZ
Guihenetal.,
2014
Presence/
absence,
quantity
Seaglider Imagenex
ES853120
kHz
AAM Madeacousticbackscattermeasurementsthroughthedowncastandupcastofa
Seaglidermissionusingacalibratedsinglefrequency(120kHz)echosounder.
Antarctickrillswarmswereidentifiedintheacousticdataandkrillabundancein
theWeddellSeawasestimatedduringtwoshortperiodsinJanuary2012.The
studyhighlightedthecomplexityofanimaltargetorientationandgliderorientation
anditseffectontargetstrengthsandthereforebiomassestimates.Italso
Antarctica
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highlightedtheneedforappropriatesamplingstrategiesforgliderstobeusedfor
biologicalsurveys.
Haulseeet
al.,2015
Presence/
absence
Slocum Vemco,
acoustic
receiver
AAM IntegratedVR2Cacousticreceivers(VEMCO,frequency=69kHz)intoaSlocumG2
glider.Thehydrophonesextendedoutofboththedorsalandventralhullto
increaselisteningcapabilities.Thesandtigerswerecaughtandtagged(internally)
withacoustictransmittersaspartofpreviousprojects.TheAUVdetected97%of
acoustictransmissionsfromtesttagswithinadistanceof250m.Thepercentageof
tagsdetecteddecreasedexponentiallyatdistancesgreaterthan250m.Thestudy
wasunabletodeterminetheverticalpositionofanysharkdetectedbytheAUV.
North
Atlantic
Johnson,
2012
Automatically
detectand
classify
vocalisations.
Slocumglider,
Apexprofiling
float,Drifting
surfacefloat
DMON
PAM
Beakedwhaledetectionswereexaminedtoestablishmiss-classificationratesto
improveclassificationthresholdsinthedetector.Gliderandprofilermotorswere
noisybuthadalowdutycycleandsoareconsideredviableplatforms.Profileswere
preferredwhiletheshortshallowdivesoftheglidermadeitlesseffective.
Canary
Islands
Kennishet
al.,2004
Seabedpatterns
andbenthic
habitats
PropellerAUV
Remus600kHz
side-scan
sonar
AAM Studyofthebedformsandrelatedbenthichabitatofinvertebrateanddemersal
finfishpopulationsinGreatBay(NewJersey),usinganAUV(REMUS)outfittedwithaside-scansonar.
NewJersey
Klincketal.,
2012
Presence/
absence
Seaglider HighTech
IncHTI-99-
HF
PAM TheSeagliderwasinstrumentedwithahydrophone.Acousticdatawere
compressedusingFreeLosslessAudioCodec(FLAC)andstoredonflashdrives,in
parallelacousticdatawerescreenedforbeakedwhalevocalisationsusingan
energyratiomappingalgorithm(ERMA)onboardtheglideranddetectionswere
reportedbacktoshoreonsurfacing.Beakedwhalesweredetectedon7ofthe85
dives(oneevery27.5hours),similardensitiestothoserecordedbyvisualsurvey.
Thedataconfirmedtheglideriscapableofdetectingthepresenceofbeaked
whaleswithinafewkilometers.Thehydrophonewasnotrecordingthroughoutthe
wholedive,andtheyidentifiedhowvocalizationscouldbemissedasaresultof
this.
Hawai’i
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Lucieeretal.,
2013
Classificationof
multibeam
acousticdata
PropellerAUV
SiriusStereo
camera
system
Image TheAUVSiriuswassentonfivemissionstomapthebenthichabitatofcoastalreefs
alongtheFortescuecoastusinghighresolutionimagesobtainedfromastereo
camerasystemat1sintervals.Thecameraimageswhereusedtogeo-reference
availableMBESimages.Theresultsallowedidentificationofattributesfromthe
multibeamacousticdatawhichcouldbecorrelatedwiththepresenceofbenthic
species.
NewJersey
Manleyand
Vaneck,1998
Bathymetry
mapping
CatamaranASV
ACESBasic
recreational
depth
sounder
AAM TheASVACESisasmallcatamaranpoweredwithagasolineengineandequipped
withacontrolsystemforautomaticnavigation.Thisstudydescribestheinitialtests
ofACESasaninstrumentforhydrographicsurveys(bathymetrymapping).The72%
ofthecollecteddatametClass1standardswhencomparedtoprevioushighly
accuratebathymetrydatacollectedbytheUSACEinthesamearea.
Massachuse
tts
Matsumoto
etal.,2013
Compare
acousticfloatto
NavyM3R
system
QUEphone
acousticfloat
HTI92WB
hydrophon
eERMA
detection
algorithm
PAM
TheQUEphone’sdetectionswerecomparabletothatofM3R’s,concludingthatthe
floatiseffectiveatdetectingbeakedwhales.
AUTEC
(Bahamas)
Ohmanetal.,
2013
Presence/
absence,
quantity
Spray Sontek750
kHzADP
AAM ObservationsofzooplanktondielverticalmigrationintheCaliforniaCurrent
system.Thedeeperdaytimedepthsandlargeramplitudeofzooplanktondiel
verticalmigrationbehaviourassociatedwiththeoffshorewatersatfrontal
transitionsareattributabletoafaunisticchangeacrossthefrontandassociated
spatialchangesinlightmediatedpredationrisk.
California
Powelland
Ohman,
2015a
Presence/
absence,
quantity,
behaviour
Spray Sontek750
kHzADP
AAM SixyearsofSprayglidertransectsacrosstheCaliforniacurrentsystemare
presented.Watersonthedensersideofafrontcontainedhigherchlorophyllaand
acousticbackscatterthanwatersonthelessdenseside.Dielverticalmigration
behaviourofzooplanktonincreasedoffshoreandcovariedwithoptical
transparencyofthewatercolumn.Differencesinacousticbackscatteracrossthe
California
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frontwerevalidatedwithnetsamplesthatshoweddifferencesinzooplankton
compositionandsizeclassesacrossthefront.
Powelland
Ohman,
2015b
Presence/
absence,
quantity,
behaviour
Spray Sontek750
kHzADP
AAM SixyearsofSprayglidertransectsacrosstheCaliforniacurrentarepresented.Mean
VolumeBackscatterStrength(MVBS)recordedfroma750kHzacousticDoppler
profilerwereusedtoobservechangesinzooplanktoncoincidentwithdensity
fronts.MVBSwassignificantlycorrelatedwithdensitygradients,anditwas
hypothesisedthatlargemobilepredatorsforaginginthevicinityofsuchfeatures
couldlocatehabitatwithhigherzooplanktonbiomassconcentrationsupto85%of
thetimebytravellinguplocaldensitygradients(i.e.,towardratherthanawayfrom
densersurfacewaters).
California
Subergetal.,
2014
Presence/
absence,
quantity
Slocum Imagenex
ES853
AAM /
PAM
TwoslocumglidersequippedwithCTDandfluorometers,onecarriedan
echosounderforresolvingzooplanktonandfish,theothercarriedamodifiedd-tag
sensor.Strongtidalcurrentsdeflectedbothgliders,whichultimatelyledto
recoveryandredeploymentofthegliders.In6weeks,thegliderscompleted5,474
divesandtravelled2,389km.Fishmarkswereobservedinthe120kHzacoustic
data,althoughnospeciesidentificationwaspossible.Harbourporpoiseclicksand
dolphinclicksandwhistlesweredetectedduringalimitedsubsectionofthe
missionbeforethed-tagpowercablewasdamaged.Noiseattributabletotheglider
pumpwasalsorecorded.
Islesof
Scilly
Vanecketal.,
1996
Bathymetrydata
acquisitionin
littoralregion
ASV(surface
craft)ARTEMISnot
specified
AAM Theprototypeautonomoussurfacecraft(ASC)ARTEMISisusedtocollectbathymetricdatainlittoralregions,forpotentialfutureuseincostalsurveysand
monitoring,minecountermeasuresandoceanographicsurveys.Accurate
geolocationofbathymetricpointsisachievedthroughaguidancecontrollerwith
differentiallycorrectedGPS.
Not
specified
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Walletal.,
2012
Presence/
absence,
quantity
Slocum Digital
Spectrogra
mRecorder
PAM Ahydrophonewasintegratedintothecowlingofaslocumglidertomeasurefish
sounds.Redgrouperandtoadfishsoundswererecorded,aswellas3other
biologicalsoundssuspectedtobefish.Soundswererecordedalongglidertransects
toprovidebiogeographyofthesefishes.TheDigitalSpectroGramRecorder(DSG)
recordedsoundfor25severy5minutesatasamplerateof70,000Hz.Datawere
storedona16GBSDcard.TheDSGclockwassynchronisedtotheglider’s
computertostoreembeddedtimestampsintotherawdatafilestructure.Fish
soundsweremanuallydetected,asglideraltimeter,pumpandat-surfaceIridium
noisehamperedautomateddetectionmethods.
Gulfof
Mexico
Walletal.,
2014
Presence/
absence,
quantity
Slocum Digital
Spectrogra
mRecorder
PAM Threepassiveacousticglidermissionswereconductedoffwest-centralFlorida,two
inaredtide,inordertoassesswhetherredtidesinfluencesoniferousfish.In
additiontodetectionsofredgrouperandtoadfish,acuskeelsoundwasalso
identifiedandrecorded.Differentfishtypenoiseswereassociatedwithdifferent
depthlayersandwithdifferentdiurnalfrequency.
Gulfof
Mexico
Williamset
al.,2010
Studyof
drownedreef
alongtheGRB
PropellerAUV
SiriusImagenex
DeltaT
AAM Aship-basedseafloormappingsupportedbyAUVsurveyinganddredgingsamples
wascarriedoutinQueensland,tostudythedrownedcoralreefsalongtheshelf
edgeoftheGreatBarrierReef.TheAUVSiriuswasusedtogatherhigh-resolutionseafloorimageryatcloserangefromtheseabedtovalidatetheinterpretationsof
thesonardatafromtheship.
Australia
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9.2.1 Cooperativesystems
Autonomousaerial, surfaceandunderwatervehiclesmay track individualanimalsandobtaindata fromthe
robotic platforms in a coordinated effort or a network (see Figure 12 as example). This can improve our
understandingof the individual technologies' limitations and acquire detailed information about the animal
understudyanditssurroundingenvironment.Thoughthisrequiresalotofefforttocoordinateallthesystems,
it isneverthelesshighlyvaluableforimprovingthecurrentknowledgeofindividualsystemsandhowcurrent
innovation can be applied with a broader approach to the marine environment. At the same time, this
coordinationusingglobalcommunicationanddatacentralisationallowsforagreatersituationalawarenessby
usingsatellite,radio,andinternetcommunications.InitiativessuchastheOceanObservingsystems(e.g.,ONR
Noise Observing System, NSF Ocean Observatories Initiative Coastal & Global Scale Nodes, Global Ocean
Observing System, Ocean Networks Canada, NOAA Integrated Ocean System) provide multi-scale,
multidisciplinary ocean observing systems using the interactive capability of advanced sensors and
platforms, and real-time access to data and visualization tools(NSF,2009).Thesenetworksareabletocollect
oceanand seafloordata, togetherwith informationabout theeffectsof anthropogenic activitiesonmarine
species(e.g.,Clarketal.,2008),athighsamplingratesthatcanlastyearstodecades.
Modernsurveysandobservingsystemsutilise,forexample,multiplegliderstoobservetheoceans.Technology
isbeingintegratedtocoordinateandcontrolmultipleplatformsandtotrackdynamicfeatures.Forexample:
TheGliderMonitoring,PilotingandCommunication(GLMPC)system(Jonesetal.,2011b),theAdaptiveSampling
andPrediction(ASAP)researchinitiative(Leonardetal.,2010),andtheAdaptiveAutonomousOceanSampling
Networks(AAOSN)17(seealsoZhangetal.,2007;Alvarez,2009).Thisincludestrackingtaggedorganismssuch
asfishandsharks(e.g.MASSMOproject18).Thesesystemsenvisageinterfacingautonomousdecisionmakingfor
multipleautonomousvehiclesofdifferenttypes,andarecurrentlybeingtestedinmodelandfieldscenarios19.
17 http://noc.ac.uk/sbri 18 http://projects.noc.ac.uk/exploring-ocean-fronts/robotic-vehicles-successfully-track-tagged-fish-plymouth 19 http://noc.ac.uk/SBRI
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Figure12.SunfishTrackingproject(Pinto,2014)20
9.3 Operationalaspectstoconsider
TheoperationalaspectsoftheuseofautonomousvehiclesduringE&Pactivitiescoverthepracticalimplications
of deploying autonomous vehicles as well as trade-offs that need to be considered based on the level of
technology,abilitiesofsystemsandthecapabilitytointegratewithexistingsystemsormonitoringefforts.
Enhanceddata,surveyquality,andmissionversatilityarecost-benefitadvantagestousingautonomousvehicles.
Theprimeadvantageofthesevehicleshoweverwouldbetoreducethedeploymentofconventionalplatforms
and personnel in the field. Hypothetically, environmental monitoring could be completed by autonomous
systemswithout the need for a conventional vessel leaving harbour or an aircraft taking off. The offshore
environmentisinherentlyhazardousandtheimplicationthatanautonomousvehiclecouldremovetheneed
forapersontoleavethesafetyoflandissignificant.Majorconcernsareraisedwithmanaginganyvessel/aircraft
offshoreandconsiderableriskanalysiswouldberequiredforunmannedcraftinanareaofindustryoperations.
Thispotentialreductioninhealthandsafetyrisksislikelytobeextremelyappealingtoindustry.
However,thepossible introductionofalternativehealthandsafetyrisk intoat-seafieldofoperationswould
delaythematurationofautonomousvehiclesinanE&Poperationalsetting.Ahighlevelofunderstandingand
confidencewillberequiredregardingoperationalhealthandsafetyrisks.Proceduresformanagingunmanned
deviceswouldneedsignificantdevelopmentanddetailedriskassessmentsandwouldneedtobeconductedon
acasebycasebasis.
20 https://bts.fer.hr/_download/repository/Jose_Pinto.pdf
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Safetywillbetheoverarchingthemeasweconsideroperationalpracticalitiesofutilisingautonomousvehicles
in an industrial setting. The vast range of possible contexts requires that we take a general approach in
attemptingtocompareacrossplatforms.Thecategorieswithinwhichwewillexploretheseissuesare:
• TechnicalReliability:Extensivetrialsandproventrackrecordarelikelytoberequiredofanycraftbefore
usageinindustrialfieldofoperations.
• MissionDuration: Any autonomous vehicle that canmaintain longmission durationwith complete
independencewillofferacrucialadvantage.
• Logistics:Logisticalfactorsmustbetakenintoaccount,albeitonaprojectbyprojectbasis,forsafety
concernsandalsocostimplications.
• RemoteOperation:Technicalandsafetyassurancewillberequiredregardingthedependabilityofthe
linkandresponsiveabilityofremoteoperation.
• Interaction with other marine/airspace users: Clear code of practice is likely to be required and
disseminatedtopotentialinteractorsintheareainadvance.
Additionally,practicalrealitiesofoperations,suchasdeployingandretrievingthecraft,tendtofalloutsidethe
scope of studies and very little published informationwas found exploring these issues. However, practical
knowledgegainedfromfirst-handexperienceoftrialsisincluded.
9.3.1 TechnicalReliability
Confidence in reliabilitywouldbe required inmechanicalandmanoeuvrabilityperformance.Any riskof, for
instance,ASVbreakdownaheadofaseismicvesselandassociatedequipment,wouldhavetime,costandsafety
implications.Supportmightbeavailableviaachasevesselbutthisisnottobeguaranteed.Closeattentionto
accuratewaypoint-markingandremoteoperation,includinganover-ridefacilityincaseofanymalfunctionis
required.Ofthehighestimportanceisthatanyautonomousvehicleoperatinginthevicinityhassufficientpower
andmanoeuvrabilitytobewhereitneedstobeattheappropriatetime(oratleastnottobewhereitshouldn’t
be).Absolutelykeytoindustryoperationsisthatanyadditionalhardwaremustnotinterferewiththeindustrial
operations taking place. In the case of ASV, trials to date have invariably involved close support from an
accompanying(conventional)vessel.ThestageissetforanASVmodeltoproveitscapabilitytoindependently
completeextendeddurationmissions.Forunderwaterbuoyancygliders,Brito,(2014)calculatedriskprofilesfor
shallow and deep gliders (combiningmanufacturer’s platforms). They found that the probability of a glider
survivingitsmissiondeploymentwithoutaprematuremissionendwas0.5and0.59fordeepandshallowgliders
respectively.Thisidentifiesthatcurrentlyotherfactorsthanbatterypowerarelimitinggliderperformance.Such
analysesshouldbecompletedforallautonomousplatforms(AUV/ASV/UAS)intendedforsurveysconducted
fortheO&Gindustry,astheyidentifytheamountofredundancyrequired(intermsofnumberofvehiclesto
completethemission),aswellasinformonlikelihoodoftechnicalfailure.Onecurrentchallengeisthatmanyof
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theseplatformsareconstantlyevolving.Extensivetrialsandproventrackrecordarelikelytoberequiredofany
craftbeforeusageinindustrialfieldofoperations.
9.3.2 MissionDuration–Powerconsiderations
Themeansofpowerunderpinsmuchoftheoperationalaspectsdiscussedinthisreview.Therearearangeof
power methods available (often as hybrid options on the same craft). Additionally, the power budget is
frequentlytailoredtoprojectspecifics.However,inbroadterms:conventionalfuelrequiresrefuelling,which
limitsmissionduration.Renewableharvesting(e.g.solarenergy)addressesthisbutrequiresaconsistencyin
supplythatcannotbeguaranteedduetovariabilityinenvironmentalconditions.Powerbywavemotionoffers
thegreatadvantageofalmostlimitlessmissionduration,albeitatlowspeedsandlowermanoeuvrability.The
meansofrefuelling/rechargingalsorequiresconsideration.InthecaseofASV/AUV,asupportvesselwould
berequired,incurringmarineoperationlogistics,costsandrisksthatanyASV/AUVintendstoreduce.Inmany
instances,particularlyforUAS,themissiondurationwilldefinetheoperation.Withinthisdefinedmissiontime
limittherewouldhavetobesignificantcontingency.Anautonomousvehiclethatcangenuinelymaintainlong
missionduration,whollyindependentlyofanysupportvessels,offersacrucialadvantagebyremovingtheneed
forin-fieldlogisticalsupport.
9.3.3 Logistics
Thedeploymentandrecoveryofautonomoussystemsisanimportantconsideration.Completeindependence
wouldbepreferred,wherebyacraftexitsandarrivesatasurveysiteatseaunderitsownpower-apossibility
forsomeASVandAUV,andmanyunderwaterbuoyancygliders(albeitslowlyinthelattercases).Shortofthis
capability,mechanismssuchasasupportingvesselfittedwithliftingequipmentandsuitablelandingareaswill
berequired.ThesewillbringfurtherHSEconcerns,especiallyinpoorweather.Transportationofcraftisalsoa
considerationtobemade.Thereisahugerangeofsizesinautonomousvehicles(Table13)butallwouldlikely
berequiredtobefreightedtotheoperationsarea.Thismightwellbeoverseasandissuesincludingimportation
requirementsandinparticulartransportationoflithiumbatteriesmaycomeintoplay.Logisticalfactorsmustbe
takenintoaccount,albeitonaprojectbyprojectbasis,forsafetyconcernsandalsoimplicationsoncost.
9.3.4 RemoteOperation
A large range of, often sophisticated, remote control mechanisms and options are covered through the
unmannedvehiclesreviewedhere.Howtheywouldbeimplementedinpracticeinanoperationalareaneedsto
beaddressed.Theoperationofavehiclefromaremotelocation,suchasbysatellitelink,opensthewayforcraft
beingmanoeuvred in an E&P survey area with several other vessels or installations on-site by a specialist
thousandsofmilesaway.Communicationprotocolswillbevital,toensurethat“ground-truthed”information,
especiallynavigationalandenvironmental, issuppliedtoassisttheremoteoperatorandfull transparencyof
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suchremoteoperations,not leastpositioninginformation, istransmittedtotherelevantpersonneloffshore.
Thisissueadditionallyimpactsinteractionwithothermarine/airspaceusers.
Thecontrol,handlingandmaintenanceofautonomousvehiclesmayrequirespecialistknowledge.Thiswould
therefore entail the provision of specialists into the offshore working environment. A key implication of
additionalpersonnelisphysicalbunkspace- invariablyatapremiuminoilandgasoperations.Alternatively,
specialisttrainingcouldberolledoutforexistingcrew.
Technicalandsafetyassurancewillbe required regarding thedependabilityof thecommunication linksand
responsiveabilityofremoteoperation.
9.3.5 Interactionwithothermarine/airspaceusers
Safety isparamountwith thenumberanddiversityofotherassets ina fieldofoperation. Theoperationof
autonomousvehicleswithinanindustrialfieldofoperationsmeansthatit’shighlylikelythatotherplatforms
willbeinthearea.Also,thehardware(whetherdeployedfromexistingvesselsorfromautonomousplatforms)
must not interferewith the industrial operations taking place. Of the highest importance is, asmentioned
previously, that any autonomous vehicle is technically reliable in itsmanoeuvrability. Smaller, lowpowered
vehicles,whichareeasiertodeploy,arelikelytohaveproblemsinthisarea,inwhichcasemeasureswouldneed
tobetakentorapidlyrecoveranyvehiclewhich isposingahazardtootheractivities.Thoughgovernmental
regulationsforsafeusemaybeinplace,theseareinearlystageofdevelopment(especiallyincaseofmaritime
activities).Considerationwouldneedtobegiventothespatialrequirementsofindividualautonomousvehicles
inanygivensetting,relativetoothermarine/airspaceusers.Sensorsandmechanismsforcollisionavoidance
willbeofhighpriority,aswillmethodsforpositioningandreportingthatposition.Datatransmissionsystems
will have to comply with local regulations and restrictions and there should be minimised risk of cross-
interference.Remoteoperationisakeycomponentherewiththequestionarisingofwhetheraremoteoperator
isrequiredtocommunicatewith,potentially,thirdpartymarine/airspaceusers.Ifso,themeansbywhichto
achieve such communicationwith thirdpartyuserswouldneed tobedefined. In summary, a clear codeof
practiceislikelytoberequiredanddisseminatedtopotentialinteractorsintheareainadvance.
9.4 Regulatory/politicalbarriers
9.4.1 Regulationsfortheairspace
ThereareseveralregulatoryandpoliticalconsiderationsinvolvedintheuseofUASinoffshoreoperations.For
instance,studysitesnearairportsorpopulatedareasandnocturnalsurveysmaybemorerestrictedthanremote
sitesanddaylightsurveys,andmaydemandspecialpermitsbylocalauthorities.However,regulationsgoverning
the use of UAS are currently changing, and users should seek updated information in due time before the
plannedresearchoperations(Wattsetal.,2010).Withoutanon-boardpilot,thereisasignificantrelianceon
the commandand control link, anda greater emphasison the lossof functionality associatedwith lost link
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communicationwiththegroundstation.Furthermore,forAirTrafficControl(ATC)operationsrequiringvisual
meansofmaintainingin-flightseparation,thelackofanon-boardpilotdoesnotpermitATCtoissueallofthe
standardclearancesorinstructionsavailableunderthecurrenteditionofFAAOrder7110.65,ATC(FAA,2013).
Consequently,toensureanequivalentlevelofsafety,UASflightoperationsrequireanalternativemethodof
compliance(AMOC)orriskcontroltoaddresstheir“see-and-avoid” impedimentstosafetyofflight,andany
problemstheymaygenerateforATC.Currently,thelackoftechnologytodetectandavoidothervehiclesintheir
vicinity,isoneofthemainlimitationsfortheuseofUAS,which,ifimplemented,wouldbehighlyvaluablein
preventingcollisionswithotheraircraftparticularlyduringoperationsbeyond-Line-Of-Sight(BLOS).Infuture,
permanentandconsistentmethodsofcompliancewillbeneededforUASoperationsintheNationalAirspace
(NAS)withouttheneedforwaiversorexemptions(FAA,2013).
Depending on the type of operation and the air space regulationswithin the country of operation, certain
requirementsmustbefulfilled(seeFigure13).InNorway,forinstance,allapprovedRemotelyPilotedAircraft
System(RPAS)operatorscanoperatewithinVLOSandtypicallybelow400ftwithoutapplyingforapermitfrom
the civil aviation authority. For a VLOS operation, visual contact without any optical devices, other than
prescription glasses,must bemaintained at all time. Themaximum VLOS (vertical) distance from the pilot
dependsonweatherconditions,timeofdayandtypeofbackground.Itcanvaryfrom200to300mforsmall
multi-rotorsandupto3to4kmforfixedwingaircraftsfittedwithhighvisibilitypaintorlighting21.Itispossible
toextendthisrangebyutilisingoneormoreremoteobserverssothattheunmannedaircraftiswithinvisual
sightofanobserveratalltimes.Theoperationisthenreferredtoasanextendedvisiblelineofsight(EVLOS)
operation. Critical flight information is relayed via radio for assisting the remote pilot in maintaining safe
separationfromotheraircrafts.RPASoperationsbeyondvisiblelineofsight(BLOS)isusuallyonlyapprovedin
areaswheresafeseparationtootheraircraftscanbeensured,suchasremoteorsegregatedairspace.BLOS
operations require an approval from the civil aviation authorities, which has to be applied for in advance
(typically3to4monthsinNorway).
Currently,allUASoperatorsshouldensuretheiroperationsmeetallapplicableNationalCivilAviationAuthority
(NCAA),local,stateandfederalregulatorylegalrequirements.TheIOGPlaunchedasetofguidelinesinearly
2015, specifically designed forUAS operations. These guidelines include issueswhich are highly relevant to
ensuresafeaerialoperationssuchasmaintenance,communication,andtraining(FAA,2013).Theseguidelines
alsospecifythatoperationsshouldbeincorporatedintoaSafetyManagementSystem(SMS)consistentwith
theOilandGasProducer’sAircraftManagementGuidelines(AMG)(Luftfartstilsynet,2015).
ThemainlimitationtotheuseofUASistheacceptanceofthetechnologybyregulatorybodies,aviation-related
restrictions on flyingUAS and responsiveness by systemmanufacturers. It is yet to be assured that current
monitoringandmitigationmethodscanbereplacedbyUAS,andthisisoneofthecriticalpointsthatstimulate
21 http://www.acuo.org.au/industry-information/terminology/how-do-we-see-them/
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moreresearchanddatacollectionaboutdifferentsystems.Atthesametime,theneedforfurtherresearchis
limitedbytheavailabilityofpermitsandregulationsthatallowsit.However,countrieslikeNorway,Canadaand
Australiahaveinvestedindevelopingnewsystemsandnewmethodsofsurveillance,whichinturncanfacilitate
theacceptancebymorerestrictivejurisdictions.ThishasalreadystartedtochangeincountriessuchasUSA,
wherethemainaviationauthority,theFederalAviationAdministration(FAA),hasstartedtotakemeasuresfor
safeUASintegrationintheirregulations.
Country-specific regulations are under development, considering mainly commercial applications. The
continuousfocusonairspaceregulationsthataremoreintegrativeofUASworldwideisaclearsignthatthis
technologyisheretostay.
Figure13.CurrentcommercialsmallUASrulesincountrieswithstrongandgrowingmarketsforUASsystems(Bonggay,
2016)22.
9.4.2 Regulationsforthesea
Theregulatoryframeworkformarineautonomousvehiclesisinastateofflux.Thereisaclearneedforgreater
clarity around legal issues and diplomatic clearance as applied for the different users, e.g.military,marine
scientificresearchor-increasingly-commercialsurveys.Unmannedsystemsarebecomingubiquitousinthe
oceans.Internationallawgoverningactivitieson,overandundertheseaemergedwellbeforethedevelopment
22 http://media.precisionhawk.com/topic/commercial-drones-faa/ (accessed 2/10/2015)
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of unmanned systems. As ASV and AUV become more advanced – already autonomous, expendable and
“intelligent” robotsareemerging, formingnetworkedsystems– legalandpolicy issuesarebecomingacute.
Legitimate concerns over insurance and recoverable loss are also raised. Kraska (2010) points out that as
“aircraft” and “vessels,” unmanned naval systems fit within the existing legal architecture for peacetime
maritimeoperations, including the 1944 Chicago Convention on Civil Aviation and the 1982UnitedNations
ConventionontheLawoftheSea.Thesetwotreatiesandtheirprogenyprovideguidancefortheuseofmostof
the global commons, and reflect a liberal legal architecture for unmanned air/sea vehicles and systems.
Furthermore,the1982UnitedNationsConventionontheLawoftheSea(UNCLOS)playsakeyroleinthelegal
frameworkforAUV/ASVdeployment.However,theavailableStatepracticeconcerningthedeploymentand
operation is not uniform or universal in scope with regard to customary international law
(CORDIS_result_171941).EventhelegaldefinitionofanAUV/ASVremainsunclear,i.e.whetherornotacraft
isclassedasa“ship”(GROOM,2013;Wynnetal.,2013).
Asaresult,thelegalviewsrelatingtotheoperationaluseofautonomousmarinevehiclesarevariedandtend
to be driven by scenario (e.g. requirement for navigational safety) or by class of operator
(defence/academic/commercial)(Rogers,2012).Assuch,itisimportanttotrackcurrentprogressinforminga
pragmaticCodeofPracticefortheuseofAUV/ASV,alsoknownasMaritimeAutonomousSystems(MAS).At
thepresenttime,suchcraftaretypicallyrelativelysmallvessels,operatingonboththesurfaceandunderwater
(ASVandAUV).Wenotethatresearchiswellunderwayonmuchlargercommercialvessels,ledbycompanies
suchasRollsRoyceandtheEUMUNINproject.
IntheUK,theMASRegulatoryWorkingGroup(MASRWG)isaddressingthisspecificneed.Itwasformedin2014
withtwomainaims:
4 ToformulatearegulatoryframeworkthatcouldbeadoptedbytheUKandotherStates,aswellasthe
internationalbodieschargedwiththeresponsibilitytoregulatethemarineandmaritimeworld;and
4 TodevelopaCodeofPracticeforthesafeoperationofMAS.
AninitialInformationPaper,preparedbytheMASRWG,waspresentedbytheUKtotheInternationalMaritime
Organisation(IMO)inJune2015.Thiswasacceptedasasignpostforothernationstobecomeawareofthework
the UK is undertaking on the combined requirements for regulation, equivalence, training, standards and
accreditationforASV.FollowingasuccessfulconferenceinOctober2015,thenextoutputoftheMASRWGwill
betopresentafullCodeofPracticetoIMO,throughtheUKMaritimeandCoastguardAgency.Thiswillbeat
theMaritimeSafetyCommittee(MSC)96inJune2016.
AnumberofothernationsandorganisationsarenowliaisingwiththeMASRWGto inputtotheUKCodeof
Practice;internationalconsensusisakeycomponentofthiswork.Inpractise,anincreasingnumberofAUVand
ASVarebeingoperatedindifferentpartsoftheworlddespitethelackofformalcountryspecificregulations.
Craftoperate,essentiallysafely,byworkingwithintheexistingconventionsandregulationsasfaraspossible.
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TheseincludetheCollisionRegulations,UnitedNationsConventionontheLawoftheSea(UNCLOS),Safetyof
LifeatSea(SOLAS),InternationalConventionforthePreventionofPollutionfromShips,1973asmodifiedbythe
Protocolof1978(MARPOL),NoticetoMariners,NavigationalTelex(NAVTEX)andmanymore.Riskassessments
and safety cases are a critical part of their safe operation. Understandably, ASV and AUV must earn the
confidenceofthewidermaritimecommunitytobeacceptedassafemarine-users.Toprovidesuchassurance,
hugedevelopmentalefforthasbeeninvestedintomethodsfornavigationalpositioningandcollisionavoidance.
Technologysuchasautomaticobstacleavoidance isregardedaskeytopavingthewayforestablishing legal
policiesforunmannedvessels(Campbelletal.,2012).Variousgapsintheexistingdocumentationbringscope
forconfusion,particularlywithintheplethoraofdefinitionsthatareemerging.Agoodexampleofthisiswith
thevariousdefinitionsandinterpretationsoftheword‘Autonomy’itself.TheCodeofPracticewilladdressthis
andmanyotherissues.ItmayprovelargelyunnecessaryfortheemergenceofASV/AUVtospawnasuiteofnew
regulations. Wherever feasible, they would find their place within the existing structure. The principle of
equivalenceiskeyherewherebymarinersofeverykindofvessel,mannedorunmanned,havethefreedomto
operateasdesired-underacommonunderstandingoflegalityandsafety.
TheintentionisthatanIndustry-ledCodeofPracticeforAUVandASVwillhelptomakethistransitionalprocess
assmoothaspossible.Inturn,thiswillfollowonaworldwidescalewithcountry-specificrequirements.
9.5 Technicalchallengesofmanaging,storingandanalysinglargeamountofdata
Overthecourseofaday,autonomoussensorscanacquiretensofGigabytesofrawdata.Themanagementand
sciencequestionstoanswerwiththesedataoftenrequirethereductionthedatatooneortwonumbersor
simpledecisions,e.g.“Isthereananimalintheexclusionzone?”or“Wasananimaldetectedinthe20minute
period?”,“Canwestartthesurveyline?”,“Thedensityofanimalsinthisareais…”.Thejobofthedataprocessing
chainistoextracttheserelativelysimpleanswersfromthegreatvolumeofrawdata.Dataprocessingisoften
doneinmultiplestagesandsensingsystemsoftencombineautomaticprocessingofrawdatawithdatadisplays
viewedbyahumanoperatorasanaidtofinaldecisionmaking.
Sensorpackagesforautonomousplatformscomeinmanyformats.Thesimplestsystemsconsistoflittlemore
thanthesensoritselfandsomesortofdatarecorder(Figure14A).Thesedays,therecorderisoftenalowpower
computerthatstreamsdatafromthesensortoadatastoragedrive.Whilesimple,suchsystemscanhavethe
advantageofhighreliabilityandlowpowerandareoftenidealifdataarenotneededimmediately.Slightlymore
complexsystems,oftenusedwithhighbandwidthdatawherestorageisaproblem,mightcarryoutsomeinitial
screeningof thedata inorder to removethosedatahighlyunlikely tocontainanythingof interestbutkeep
processingtoabareminimumanddonotprovidedatainreal-time(Figure14B).Ifdataarerequiredinrealtime,
thenthosedatamustbetransmittedtoshoreoranearbymannedplatform(vesseloraircraft)sothattheycan
beusedinnearreal-timedecisionmaking(Figure14C).Thiscancreatesignificantchallenges,particularlywhen
workingwithhighfrequency(andthereforehighvolume)dataoverlowpowerorwhereitisexpensivetosend
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largevolumesofdata(e.g.satellite).Whenit isnotpossibletotransmitrawdata,thensufficientprocessing
musttakeplaceonboardtheplatforminordertoreducetheamountofdatatothatwhichcanpracticallybe
transmitted(Figure14D).
Thequantityofdatathatcanbesentthroughthevariouscommunicationsdevicesvariesenormously(section
8.1.4) but suffice to say that many communications options do not have the capability to transmit high
bandwidthdata,inwhichcaseitisnecessarytoprocessdataontheplatformitselfinordertoreducetheamount
ofdatathatistransmitted(Figure14D).
Figure14.Schematicdiagramsoffourdifferentsensordatahandlingcombinations.Theseareintendedprimarilyas
examplesandmanymorecombinationsexist.A)Simplesensinganddatastorage,B)Sensor,basicprocessinganddata
storage,C)Real-timetransmissionofrawdatawithprocessingonshore,D)Onboardprocessingtoreducedatavolume
priortotransmissionofaminimalamountofdata.
Hereinliethefundamentalproblemsandlimitationsofsensorsonautonomousplatforms:forsituationswhere
dataarenotrequiredinnearrealtime,thelimitationsarestoragecapacityandpower;ifdataarerequiredin
real-time(ornearreal-time)thentheamountofdatathatcanbetransmittedaregovernedbytransmission
distance,poweravailabilityandcost.Datamustthereforebereducedbywhateverfactorisrequiredtosatisfy
thoselimitations.However,processingalgorithmsmustbesufficientlyreliableandsufficientdatamuststillbe
transmitted that an operator has enough information for accurate decisionmaking. If insufficient data are
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presentedtoanoperatorandthereisanoverrelianceonpotentiallyunreliableautomaticprocessingalgorithms,
thenerrorswilloccur:animalswhicharepresentmaybemissed,andfalsealarmswillbereceived.
Theamountofprocessingthatcanbecarriedoutontheplatformwilldependonthesensortype,thestrength
anddistinctivenessofthesignalsofinterestandalsothetypeandquantityofbackgroundnoisewhichmayvary
widelybetweenregions.Forsomesensors,speciesandsituations,researchershavebeenabletoreducedata
sufficientlyforreliableinformationtobesenttoshoreviarelativelylowbandwidthsatellitecommunications
links(e.g.Baumgartneretal.,2013).Thisdoesnot,however,meanthatsuchsystemscanbeeasilydeveloped
forotherspeciesandothersituations.Inmanycases,ithasprovennecessarytousemuchhigherpower,short
rangecommunicationssystemswhichtransmithighvolumesofdataoverrelativelyshortdistances.
9.5.1 Electro-opticalimagingsensors
Opticalsensors(RGB,IR,andvideo)maydifferintermsofdatatransferandprocessingdependingonthetype
ofsensorinconsideration.Still-photoandvideodata(eitherinRGBorIR)maybestoredon-boardanSDcard
forpostprocessingorgatheredduring real-time transmissionofavideoandmanually triggered toobtaina
photoofatarget.Theresolutionrequiredheremayplayakeyroleinthetypeofsensortobeused,sinceitis
possibletotransmitHDimagesinreal-time,buthigherresolutionsstillappeartobeunreliableforthistypeof
application.Datatransmissionwillthereforedependonthedatatype,withradiolinksaffectingtherangeto
whichitispossibletofly.Forhigherranges,itisthenrequiredtoacquireastrongerradiolink,suchasKongsberg
MaritimeBroadbandRadio.
Automateddetectionofobjectsofinterestisanadvantageincircumstanceswheretheamountofdatatobe
acquiredisverylarge.Thisishighlyrelevantparticularlyfortransect-basedsurveysinpopulationstudiesand
monitoringof areas relevant to seismicoperations. Zitterbartet al. (2013)have shown thepotentialof this
technology indetecting largecetaceansusing thermal IR systems in shipsurveysby identifyingwhaleblows
basedon their thermal signature.Additionally,workdevelopedbetween LGLAlaska, Inc, andBrainlike, Inc.
(Ireland et al., 2015) has progressed the availability of a real-time animal detection software. This type of
softwarerequiresadegreeofmachinelearning,sincesurveyconditionsmayvarybothintime,space,andinthe
type of species possible to encounter. On the other hand, algorithms that are operational for post-survey
analysis are still unreliable due to the lack of animalmovement that improves detectability.Mejias, (2013)
developedtwoalgorithmsthatseemtobepromising inmarinemammaldetection,however,theamountof
falsedetectionsremainedhigh,highlightingthestatusofdataprocessingforpost-surveyanalysis.
ThoughUASareanupcomingtechnologywithmanydifferentapplications,theamountofdigitalimagerythat
thesesystemscollectcansometimesbeoverwhelming.Dependingonthesurveydesign,thetimerequiredto
manuallyanalyse the images fromeach survey canmake thismethodology costly,highlighting theneed for
automatedanalysisprocesses.Therearemany issuesconcerning theuseofautomateddetectionofmarine
organisms; sea state condition isoneof the key issues affectingdetectability,which in termsof automated
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detectioncouldhaveamajoreffect, since thepresenceof surfwakecancreate falsepositives thatmaygo
unnoticedunlessmanuallychecked.Anotherissueisthepresenceofglareandotherenvironmentalfactorsthat
mayaffectthedetectionofanimalsbasedpurelyonbodyshape.Irelandetal.(2015)developedanautomated
process that can quickly process high resolution digital still images and identify potential marine mammal
detections, with an overall successful detection rate of approximately 70%. The time required tomanually
reviewtheautomatedprocessingoutputbasedonthisworkwaslessthan2%ofthefullmanualanalysistime,
thus significantly reducing the amount of time and associated cost of image analysis. This work, done in
collaborationwithBrainlike Inc.,hasbeen incorporated intoaerial surveyworkconductedbyShellOilusing
PixMin™, a software designed for target recognition. However, most of the work developed in automated
detectioninvolvespostsurveyanalysis,althoughKoskietal.(2013)showedthatreal-timemonitoringwithUAS
andvideocamera is feasible.Theneed for survey technology (platformsandsensors)hasbeenpushing the
developmentofaerial systemsthatcandeliveraccurateandunbiaseddataboth to industryauthoritiesand
research.Therefore,imageryanalysisremainsaconstraintthatisquicklybeingovercomebytheprogressinthe
developmentofbothsensorsandaerialplatforms,asthequalityofthedataimprovesandtheavailabilityof
detectionalgorithmsthatcanbetestedincreases.
9.5.2 PAMsystems
Table9showsthevolumeofrawuncompresseddatafromasinglehydrophonedigitizedwith16bitaccuracy.
Notethatthesearerawdatavolumesandthatitispossibletoruncompressiononaudiodatawhichwillreduce
datavolumebyaroundafactoroffourusingalgorithmssuchasFLACorX3(Johnson,2013).Ifmulti-hydrophone
systemsareconsidered(aswouldberequiredforanimallocalisation)thenthesevaluesshouldbescaledupby
thenumberofhydrophones.As canbe seen, forbaleenwhales,whichvocaliseat low frequencies,ayear’s
recordingwillgenerateonlyamodestamountofdataandevensamplingatthestandardhumanaudiosample
rateof48kHzforayearwillgeneratenomoredatathancanbefittedonasingleportableharddiskdrive.
However,forthemajorityofdolphin,beakedwhaleandporpoiseecholocationclicks,higherbandwidthsare
required,whichgeneratesignificantvolumesofdata.
Eveninsituationswherelargedatavolumescanbestored,transmittingthosedatatoaremoteplatformmay
beimpracticalduetobandwidthandpowerlimitations.Ifsufficientpowerisavailable,radiolinkscanbeused
overrangesofafewkilometres;forlargerdistancesinremotelocations,itmaybenecessarytousesatellite
communicationssystems,inwhichcasepower,bandwidthandcostallbecomeproblematic.Inthiscasetheonly
optionistopre-processdataonboardtheautonomousplatformandtransmitonlyasummaryofthedetection
data,whichishopefullysufficientforaccuratedecision-making.Insomecases(withlowfrequencydataorhighly
distinctivespecies)thishasbeenprovenpossible,howeverforspecieswhicharedifficulttodistinguishfrom
backgroundnoise,orforwhichcomplextrackingisrequired,itiscurrentlynotpossibletotransmitsufficient
datathroughlowbandwidthsystemsforaccuratereal-timedecisionmaking.
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Table9.Datavolume(Gigabytes)fordifferentrecordingbandwidthsandrecordingdurationsforasinglehydrophone
recordedat16-bitaccuracy.
SampleRate(Hz)
Bandwidth(Hz)
GigabytesperDay
GigabytesperWeek
GigabytesperMonth
GigabytesperYear
SpeciesAccessible
250 125 0.04 0.28 1.21 14.69Blue and some other largebaleenwhales
2000 1000 0.32 2.25 9.66 117.48 Baleenwhales
48000 24000 7.72 54.07 231.74 2,819.54Sperm whales, most dolphinwhistlesandsomeclicks
500000 250000 80.47 563.26 2,413.99 29,370.19Dolphin, beaked whale andporpoiseclicks.
Anincreasingnumberofalgorithmsarenowavailablefortheautomaticdetectionofmarinemammalsounds
withconsiderablemomentumtothefieldbeinggivenbybiennialworkshopsfundedbytheUSOfficeforNaval
Researchandothersandpublishedas special issuesof various journals23.However,manyof thealgorithms
presentedintheliteraturehavebeentunedtoveryspecificdatasetscoveringalimitedrangeofspecies,and
nearlyallrequirehumanvalidationoftheiroutput,particularlywhenworkinginavariablenoiseenvironment.
Researchgroupsprocessinglargedatasetsgenerallytrytofindabalancebetweenautomaticprocessingand
humanvalidation,puttingconsiderableeffort into thedevelopmentofdatavisualisation toolswhichenable
operatorstohomeinoncandidatedetectionsorareasofinterestinthedata(e.g.Tritonsoftwaredevelopedby
SCRIPPSWhaleAcousticsLab,theRavenandXBatpackagesfromCornellandPAMGuardfromSMRU).Forsome
soundtypes,detectoroutputdata,containingonlyveryshortsnipsofrealdata,aresufficientfordatachecking.
Forothers,thecontextualinformationthatisonlyavailablefromaspectrogramofalongersectionofrawdata
can bemore important. For example, Figure 15 shows false and realwhistle detections from an automatic
detector bothwithout andwith the full spectrogram information. From thedetectedwhistle contours, it is
impossibletosaywhetherthedetectionsarerealorfalse.Byviewingspectrogramsoftherawdataandalsoby
listeningtothem,itwasclearthatthefirstsoundismechanical(inthiscasethehydraulicsystemloweringthe
outboardengineonavesseldeployingaPAMequippedWaveglider)andthatthesecondexampleisagenuine
animalsound.However,thefull5secondsofrawaudiodatashowninthisexamplewouldrequirearoundhalf
amegabyteofdatarelay,whereasthedetectiondataaloneonlyrequiresatinyfractionofthis.
23For example: Canadian Acoustics, Vol 32, No 2 (2004), Canadian Acoustics, Vol 36. No. 1 (2008), Applied Acoustics, Volume 71, Issue 11, Pages 991-1112 (November 2010), J. Acoust. Soc. Am. Special issue on Methods for Marine Mammal Passive Acoustics , J. Acoust. Soc. Am. 134 (2013)
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9.5.3 AAMsensors
Theamountofdatacollectedbyactiveacousticmethods(singlebeam,splitbeam,omni-directional,wideband
andmultibeamechosounders)isdependentontheresolution(e.g.pingrate,numberofbeams),thecomplexity
ofthedata(e.g.single/multifrequencyorwideband)andtherangecollectedto.Datavolumeiscontrolledby
varyinganyof these three criteria. Forexample,10pingsof120kHz single frequencydata (1.024mspulse
length)toarangeof100mwithanEK80generatesa~300kBdatafile,whereas10pingsofwidebanddata(95
–160kHz,centralfrequencyof120kHz,2.048mspulselength)toarangeof100mgeneratesa~5mBdatafile.
Asaresultofthishighdatavolumecreation,theAAMsensorstypicallystoredatalocallyforretrievalandanalysis
oncetheplatformisrecovered,unlesstheyarepartofacabledobservatory24.Thelogicalsolutionistolookfor
methodstoreduceorcompresstherawdataortopre-processdataon-boardtheplatform(althoughthishasa
payload and power cost to the platform) for transmission of summary statistics.We are not aware of any
publishedexamplesofdatacompression,althoughthereareexampleswhereitisalludedtoduringtheuseof
24 e.g. http://www.interactiveoceans.washington.edu/story/NSF_Ocean_Observatories_Initiative
A
B
Figure15.Examplesofrealandfalsewhistledetectionsbothwithoutandwithfullspectrograminformation.A)A
falsedetectioncausedbythehydraulicsofanearbyengine.B)Adolphinwhistle.
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anEK60onaHuginAUV(Patel,2004)andforreal-timetransmissionoftunadetectionusingtheZunibalTunabel
e7buoy25.
Methodsforthedetectionandestimationofanimalbiomass, inparticularfish,fromactiveacoustics iswell-
developed (e.g. Korneliussen and Ona, 2003; Petitgas et al., 2003). However, like the PAM systems, these
methods are typically tuned to specific features and species and frequently require human validation.Data
processingandanalysis is typicallyundertakenusingeithercommerciallyavailableacousticsoftware2627, in-
housebuilt(e.g.MOVIES3D,citedinTrenkeletal.,2009)ortoolboxesdevelopedforMatlab28.Thenextstepis
toimplementtheprocessingofdata(withaspecificgoal)on-boardanAUVtosendsummarystatisticsbackto
thebasestation.Weareunawareofanycurrentactivitiesthatwouldenablethis,but it isanareaofactive
researchinterest.
TritechInternationalLtdhasdevelopedthesoftwareGeminiSeaTecmarineobjecttrackingandtargetdetection
system29inclosecooperationwithSMRULtd(nowSMRUConsulting)asadatareductiontoolforbehavioural
monitoringandresearch.Italsoallowsforearlywarningofthepresenceofmarinemammalsaroundrenewable
tidaldevices(Hastie,2012;Hastie,2013).Thissystemusesclassificationalgorithmsbasedonvariablessuchas
size, shape and swimming characteristics that provide a probabilistic indication of the identity of individual
targetsasvalidmarinemammaltargets.ResearchersatSMRUarecurrentlydevelopingimprovedalgorithmsfor
theautomaticdetectionandtrackingofmarinemammaltargetsusingtheTritechGeminimultibeamsystem(C.
Sparling,pers.Comm).Thissystemisalsocurrentlybeingadaptedforsharkdetectiontoprovideanalternative
tocurrentsharkdefencesystems.
10 Acknowledgements WewouldliketothankIOGPforfundingthiswork.WearegratefulforthehelpandsupportprovidedbyGordon
HastiewithregardstoAAMsensorsandmarinemammaldetectionaswellastoCarolSparlingforherreviewof
aformerversionofthisreport.
Wewouldalsoliketosincerelythankallcompaniesthatprovidedsysteminformationforinclusioninthisreport:
ALSEAMAR, ASL Environmental Sciences Inc., ASV, BioSonics Inc., Brainlike Inc., C-ASTRAL Aerospace Ltd.,
CheloniaLtd.,CornellUniversity,EmbeddedOceanSystems,ImagenexTechnologyCorp.,KongsbergMaritime
AS, Kongsberg Underwater Technology Inc., Liquid Robotics Inc., Marine Micro Technology Corp., MOST
(AutonomousVessels)Ltd.,NortekAS,OceanInstrumentsNZ,RTSYS,SCRIPPS,SeicheLtd.,Simrad,StAndrews
InstrumentationLtd.,TritechInternationalLtd.,UTCAerospaceSystems,Vemco,WildlifeAcousticsInc.
25 http://zunibal.com/en/product/tunabal-e7-buoy/ 26 Echoview, https://www.echoview.com/ 27 LSSS, http://www.marec.no/english/index.htm 28 http://hydroacoustics.net/viewtopic.php?f=36&t=131 29 http://www.tritech.co.uk/news-article/gemini-seatec-marine-object-tracking-and-target-detection
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11 Appendix 11.1 Listofcriteriaandmetricsforplatform,sensoranddatarelay
TheevaluationcriteriadefinedandexplainedinsectionCriteriaandmetricsweregroupedintothefollowing
categories:general information,technicaldetails,costs,surveycapabilities,operationalaspectsandmanning
requirementsforboththeplatformsandsensors.Thesegeneralcategoriesfacilitatedtheorganisationofthe
hugeamountofinformationgatheredforeachsystem.Below,abulletpointlistofthecriteriaasdefinedabove
aswellasalinktothecomparisonmatrixtableisgivenforaneasiernavigation.
11.1.1 Platform
Criteria/Metric Description
GeneralInformation Table12includesgeneralinformation:
General Includesthetypeofplatform,platformnameandthemanufacturerand/orthedesignersaswellastechnologyreadinesslevelasdefinedinTable10.
TechnicalDetails Table13andTable14includethefollowingtechnicaldetails:
MissionDuration MaximummissiondurationDurationLimitations FactorslimitingthemissiondurationSpeed Minimum,maximumandtypicalspeed
VerticalRange Minimumandmaximumverticalrange(heightabovesea level foraerialvehiclesanddepthforunderwatervehicles)
EnvironmentalFactors EnvironmentalfactorslimitingtheplatformperformanceSize&Weight OperationalandpackagedsizeandweightHazardClass HazardclassasdefinedinTable11NoiseLevel Noiselevelemittedbytheoperatingplatform
LimitingFactors Factorspotentially interferingwith theperformanceof theplatform (other thanenvironmental)
Interfaces TypesofinterfacesAdditionalSensors PresenceorabsenceofadditionalsensorsontheplatformCTD DeploymenttypeofCTDsensor:mounted,towedorwinchdeployment
Costs Table15containsthecostrelatedcriteria:
Purchase Purchaseprice
Rental Rentalpriceoftheplatform,requestedasper3monthsforAUV/ASVanddailyrateforUAS
Operational Operationalcostsintermsofpilotingmanpowerandpilotingtelemetrycosts
Maintenance Maintenance costs and the costs of batteries and/or fuel used to power theplatform
SurveyCapabilities Table16entailsthesesurveyspecificcriteria:
TrackSetting Availability and kind of a track setting option such as programmed waypoints,manualpiloting,bothoptionsoranyotheroption
EnvironmentalLimitations Environmentallimitationstotrackkeepingsuchascurrentorweather(wind,rain)
Self-Correct Availabilityoftheinstrumenttoself-correctitspositionwhendivertingfromtrack
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Criteria/Metric Description
StationKeeping Abilityforstationkeeping(oratleastperformturnstoessentiallystayinthesameplace)
AutonomousDecisions AbilityofautonomousdecisionmakingwithmultiplesurveymodesClockSync Abilityforclocksynchronisation
OperationalAspects Table17coversthefollowingoperationalcriteria:
Fuel Fueltype
Failsafe Availabilityofafailsafemodeandkindofmode:bearing,location,endpoint,keeptrackorstop,includinganyotherimportantinformation
Detect&Avoid Transponder,detectandavoidcapacityInterface GroundstationinterfacesuchasIridium,RForBluetoothAutonomy Levelofautonomy:waypointsorcontinuouscontrolPayloadPower PayloadpowerinJoulesandPayloadcapacitydimensionsandweightDeploy&Recover DeploymentandrecoveryprocedureManning
RequirementsTable18coversthemanningrequirements:
Manning Number of people/pilots required to operate the platform and the trainingrequirementsnecessary.
11.1.2 Sensor
Criteria/Metric Description
GeneralInformation Table20includesgeneralinformation:
General forthesensors:thesystemclass,suchasAAM,PAMorVideo,sensornameandthemanufactureraswellastechnologyreadinesslevelasdefinedinTable10.
Technicaldetails Table21includesdetailssuchas:
Size&Weight Operationalandpackagedsizeandweightoftheplatforms
HazardClass HazardclassasdefinedinTable11,whichisespeciallyimportantforshippingandtransportissues
InterferingFactors Factors potentially interfering with the performance of the sensor (other thanenvironmental)
Interface TypeofinterfaceCosts Table22coversthecostrelatedcriteria:
Purchase Purchaseprice
Rental Rentalpriceoftheplatform,requestedasper3monthsforAUV/ASVsensorsanddailyrateforUASsensors
Operational OperationalcostsintermsofoperatormanpowerMaintenance MaintenancecostsperyearIntegration IntegrationofsensorintoplatformSurveyCapabilities Table23andTable24containthecriteriadefiningthesensor’ssurveycapabilities:
EnvironmentalLimitations
Environmental factors limiting optimal sensor performance such as current orweather(wind,rain)
TypeofDataTypeofcollecteddatatounderstandifdataarestoredprocessedwithanon-boardprocessingprocedureorasrawdata
SpeciesID Abilitytoforspeciesidentificationfromcollecteddatatounderstandifthesensorhasalreadybeenusedtoidentifymarineanimalspeciesand,ifso,whichkind
Bearing Abilityforestimatingbearingtoanimalfromasinglevehiclewiththedatacollected
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Criteria/Metric Description
DirectRange Ability for direct range estimation from a single vehiclewith the data collected,neglectinganypossiblebearingambiguityorswimmingdepthofanimal
HorizontalRange Ability for horizontal range estimation from the animal to a track line / vehiclelocationfromasinglevehiclewiththedatacollected
Real-Time Abilityforreal-timedatatransmissionofanimaldetections
PAMAbility of PAM sensors for estimation of received levels of the detected animalvocalisation or ambient noise levels, or simultaneous CTD data collection (PAMsensorsonly)
ClockSync Ability for clock synchronisation to be synchronised with other sensors and itslimitationstotheaccuracy
OperationalAspects Table25includesthefollowingitems:
Autonomy Levelofautonomy:waypointsorcontinuouscontrolDeploy&Recover Deploymentandrecoveryprocedure
PayloadPower Payload power in Watts as well as internal and external payload capacitydimensionsandweight
Manning
RequirementsTable26coversmanningcriteria:
ManningSuchasthenumberofpeoplerequiredtooperatethesensor,thevesselaswellasmanningrequirementsfordeployingandrecoveringthesensors,andthetrainingrequirementsnecessary.
Furtherinformation Table27givesinformation
Further
Informationontherawdatavolumeandthereal-timedatareductioncapabilitiesofthesensor.Furtherinformationisgivenifsensorsarerecommendedforfuturefield trials formarine animalmonitoringwith regards to E&P activities, relevantreferences(notyetmentioned),linksandfurthercomments.
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11.1.3 Datarelay
Criteria/Metric Description
GeneralInformation Table28includesgeneralinformation:
GeneralThegeneralinformationrequestedinthedatarelaysectionincludingthesystemclasssuchasIridium,thesystemnameandthemanufactureraswellastechnologyreadinesslevelasdefinedinTable10.
Technicaldetails Table29includesdetailssuchas:
EnvironmentalLimitations Environmental limitations to optimal system performance such as current or weather(wind,rain)
Size&Weight OperationalandpackagedsizeandweightofthesystemPower PowerData Databandwidthinbitspersecond(bps)Transmission TransmissionrangeCosts PurchaseandoperationalcostsPowerConsumption PowerconsumptionRestrictions UsagerestrictionssuchasgeographicalorpoliticalrestrictionsDelay Datadelay
Training TrainingneedsRequirements Platformrequirements
Table10TechnologyReadinessLevelsusedintheevaluationmatrices*
TechnologyReadinessLevel Description
TRL1:Basicresearch Principlespostulatedandobservedbutnoexperimentalproofavailable.
TRL2:Technologyformulation Conceptandapplicationhavebeenformulated.
TRL3:Appliedresearch Firstlaboratorytestscompleted;proofofconcept.
TRL4:Smallscaleprototype Builtinalaboratoryenvironment("ugly"prototype).
TRL5:Largescaleprototype Testedinintendedenvironment.
TRL6:Prototypesystem Testedinintendedenvironmentclosetoexpectedperformance
TRL7:Demonstrationsystem Operatinginoperationalenvironmentatpre-commercialscale.
TRL8:Firstofakindcommercialsystem Manufacturingissuessolved.
TRL9:Fullcommercialapplication Technologyavailableforconsumers.
*AdaptedfromtheEUFrameworkProgrammeforResearchandInnovation,Horizon2020definitions.
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Table11.Hazardclasslevelsrelatedtobatteryorfueltypeofanevaluatedsystem.Oilsorothermaterialusedin
manufacturingofsystemsthatmayhavehazardsarenotconsidered.
HazardClass Description
1 Explosives
2 Gases
3 FlammableLiquids
4 FlammableSolids
5 OxidizingSubstances
6 Toxic&InfectiousSubstances
7 RadioactiveMaterial
8 Corrosives
9 MiscellaneousDangerousGoods
11.2 Shortlistofplatformsandsensors
Thissection includesashort listofthecurrentstateofthearttechnologiesofavailableUASandAUV/ASV
platformsandsensorstechnologieslistedinthecomparisonmatrices(appendix11.1)thataremostpertinent
toO&Goperationsformonitoringmarinemammal,seaturtlesandfish.
Thecollectedinformationandmatriceswereusedtoevaluatethesystemswithregardstotheirperformance
andsuitabilityforoperationduringseismicsurveysandotherE&Pactivitiesandtheirpotentialuseforassessing
theeffectsofoperationsonmarinespecies.
11.2.1 Poweredaircrafts
11.2.1.1 UX5[TrimbleNavigationLtd]
ThisUAShasbeenunder focusmainly formappingand surveillance solutions. Ithasbeen tested in various
environmentalconditions,suchaswind,extensiveheat,andsnow.TheUX5hasincorporatedareversedthrust,
improvingaltitudemeasurementthatwillassistinaccurateandpredictablelandings.Thisconditionisidealfor
professionalsworkinginsmallareas.Theaircraftisdeployedusingacatapultsystem,andrecoveredusingbelly
landing.Themaximumflight time recorded for this system is50minutes.TheUX5 isdeliveredwithahigh-
resolutionstillcamera,whichstoresimageryon-boardforpostprocessing.
11.2.1.2 BRAMORC4EYE[C-Astral]
TheBRAMORC4EYEUASlineisdesignedforoperationswherereal-timeornearreal-timevideoobservationand
surveillancecapabilityisthemainfocus.Thissystemhasanenduranceof3hoursandastandarddatalinkof30
km. Thedeploymentmethod is similar to theUX5 though theBRAMORC4EYEhas an automatic parachute
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landing,whichallowsforasmootherrecoveryoftheequipment.Thisreducestheriskofdamageofboththe
aircraftandsensors.ItcomesdeliveredwithHDvideoorHDvideo+LWIRpayloadfrommanufacturer.
11.2.1.3 ScanEagle[Insitu]
This platform has been used inmarinemammal studies in Australia (Hodgson, Kelly, and Peel, 2013), and
demonstrateditsabilitytoconductflightsatseaandofuseformarinemammalmonitoring.Theaircraftisable
tosendlivevideofeedtothegroundstation,whichcanbestoreddirectlybythereceiverforposterioranalysis
orusedfortimelydecision-making.Thissystemdoesnotrequirenetsorrunwayarerequired,reducingspace
constrictions in choosing launchand landing locations. Insitucandeliveravarietyofpayload turrets for the
ScanEagle,wheresomearemilitaryonly.ForseamammalstudiesthevisibleEO900ortheVisible+MVIR“Dual
Imager”isrecommended.
11.2.1.4 PenguinB[Uavfactory]
ThePenguinBisacommerciallyavailablefixed-wingUAScapableofflightsofover20hoursandwithsimilar
launchingproceduresasforthepreviouslymentionedpoweredUAS.Whatdistinguishesthisaircraftfromthe
remainingisitsmodularcompositestructure,removablepayload,andneedforawiderareatolandsuchasa
runway. Thismay result in limitedoperations if vessel landing is required, though the flight capacityof this
platformisabletoovercomethisobstacleduetoitsenduranceandoperationalrange.Itcanbedeliveredwith
arangeofcameraoptions,orwithoutpayloadforintegrationofthirdpartysensors.
11.2.1.5 Fulmar[Thales]
TheFulmarUAS is classifiedasamini-UASand isahighlyversatile system,with capabilities foravarietyof
missions.It is launchedusingacatapult-basedlaunchersystemandlandedusingnetlanding.Thus, itcanbe
operated and recovered at any time with few limitations concerning weather conditions and terrain
characteristics.Thissystemhasamaximumflighttimeof12hours,andcanbedeliveredwitharangeofsensor
options,orwithoutpayloadforintegrationofthirdpartysensors.
11.2.1.6 Jump-20[ArcturusUAV]
Jump-20isafixedwingUAScapableofverticaltake-offandlanding,withnoneedforlaunchsystemorrunway.
However, Jumpaircraftcanalsobeconvertedtocatapult launchwithasimplewingchange. It iscapableof
conductingflightsupto16hoursunderharshweatherconditions.TheJump-20canbedeliveredwiththeTASE
400gimbals(orthesmallerversions)fromCloudCap,orwithoutpayloadsforintegrationofthirdpartysensors.
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11.2.2 Kites
11.2.2.1 SwanX1Kite[FlyingRobotsSA]
TheSwanX1Kite(FlyingRobotsSA)consistsofadeliverytricycleequippedwithasoftwingfollowingtheconcept
oftwo-seatermicrolight.Thissystemiscomposedby:
• AnaerialplatformFRSWANXI;
• Anautopilotthatallowsfortake-off,flight,andlandingautonomously;
• A ground station that allows communication between ground operators via UHF/VHF data in case
manualcontroloftheaircraftisneeded.
ThisUASisstillademonstrationsystemanditisnotyetavailablecommercially.However,furthertestingwill
indicatethesuitabilityofthisequipmenttobeaccessibleforfuturescientificendeavours.
TheSwanX1isaversatilesystemthatcanbeequippedwithdifferentsensorsthatareintegratedinthehardware
and software platform. It is capable of carrying a video kit and omnidirectional antenna (supplied by the
manufacturer),whichallowsforreal-timeimagetransmissiontothegroundstation.
11.2.3 Lighter-than-airaircraftsystems
11.2.3.1 OceanEye[MaritimeRoboticsAS]
TheOceanEyeballoon,producedbyMaritimeRoboticsAS,isahelium-filledballoonabletocarryasensorunit
forpersistentaerialsurveillance.Itisatetheredaerostatsystemthatlocalizesthesurveillancetechnologyon-
boardamissionvessel.Inotherwords, it isavehiclewithanaerostaticorbuoyantliftthatdoesnotrequire
movementthroughthesurroundingairmass.Itisdependentonthepresenceofasupportingvesseltowhichit
is tetheredandtransmits its imageryand flight information.This systemhasnotyetbeentested foranimal
monitoring; thoughmost of the applications of theOceanEye are conducted at sea for petroleum-industry
relatedoperationssuchasoilspillmonitoring.
11.2.3.2 DesertStar10[AllsoppHelikitesLtd]
Helikiteaerostatsareacombinationofaheliumballoonandkite (alsoknownaskitoons),whichareableto
overcometheshortfallsofnormaltetheredballoonsandkites.TheHelikitesaretetheredballoonswithawing
thatallowsittosustainharshwindconditions.Theseplatformshavebeentestedinseveralconditionsandseem
tobeveryversatileintermsoftheiradaptabilitytoenvironmentalconditions,areavailableinvarioussizes,and
arereadytotakeondifferentchallenges.
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11.2.4 Propellerdrivenunderwatercraftandpoweredsurfacecraft
11.2.4.1 REMUSFamily[Hydroid,KonsbergMaritime}
TheREMUSisapropeller-driven,torpedo-shapedAUVdevelopedbyHydroid,aKonsbergMaritimecompany.
ThedifferentREMUSmodelshavebeenextensivelytestedincommercialandnavaloperationsinabroadrange
ofenvironments,fromshallowanddeepwaterstoarcticandtropicalregions.REMUS100isacompact,man-
portableAUV,thesmallestofallmodelsavailable;REMUS600isamid-size,highlyversatileandfullymodular
AUV;andREMUS6000isthelargestmodel,designedfordeepwateroperations.
AllmodelsarebatterypoweredanduseaDCmotorattachedtoa2-3bladepropeller.Theycantravelatan
approximatemaximumspeedof4knots,withmissiondurationsbetween8hours(fortheREMUS100)to24
hours(fortheREMUS600).Thevehiclesareoutfittedwithasetofstandardsensors,whichincludeanacoustic
Doppler current profiler (ADCP), a sidescan sonar (SSS), a CTD device, long baseline transponders (LBL), an
inertialnavigationsystem(INS)anddatacommunicationdevices(acousticmodem,IridiumsatelliteandWiFi).
The standard sensor configuration varies between models; additional sensors like cameras, environmental
samplersoradvancedsonardevicescanbeinstalledinthevehicle.TheREMUSfamilyusesacommonsoftware
application(VIPorVehicleInterfaceProgram)formissionplanning,visualizationofsurveyprogressandsystem
statusmonitoring.
11.2.4.2 HUGINFamily[KonsbergMaritime]
The HUGIN is a propeller-driven AUV developed jointly by KonsbergMaritime and the Norwegian Defence
ResearchEstablishment(FFI).Currently,therearethreemodelsavailablecommercially:HUGIN1000,HUGIN
3000andHUGIN4500.Allofthemsharethesametechnologybaseintermsofnavigation,control,payload,
communication, propulsion and emergency systems; themain differences are size, battery technology and
endurance, and sensor configuration. TheHUGIN has a long history of successful operations in commercial
applications, with more than 150.000 km covered in a period of ten years. It was designed to satisfy the
requirementsofcivilianandnavalapplications, forwhichrobustness,dataqualityandoperationalefficiency
werethemaingoals.
The three models use a large blade propeller for high electrical-to-hydrodynamic efficiency and low noise
emission;thenavigationspeedsrangebetween2and4kts(excepttheHUGIN1000,whichcanreach6kts).The
AUVcanoperateinthreemodes:autonomous,semi-autonomousandsupervised.Thenavigationiscontrolled
byanadvancedreal-timeaided inertialnavigationsystem(AINS),whichutilises information froman inertial
measurementunit(IMU),Dopplervelocitylog(DVL),depthsensor,ultra-shortbaselinetransponders(USBL)and
GPS.Datatransmissionandcontrolfromthemothershipcanbesetthroughanacousticmodem,radioorWLAN
link,orIridiumsatellitecommunications.TheHUGIN1000isequippedwithaLithiumpolymerbatteryof54MJ,
whichprovidesanenduranceof24h,lessthanhalfofwhattheAlHPsemi-fuelcellbatteriesofthelargermodels
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canoffer.Themodelnumbergivesthemaximumoperationaldepthinmeters.Thestandardsensorsinstalledin
thethreeAUVareessentiallythesame,butthelargermodelscanbeoutfittedwithamorecapablesensorsuite.
Thestandardsensorconfigurationincludesamulti-beamechosounder(MBE),asidescansonar(SSS)orsynthetic
aperturesonar (SAS),asub-bottomprofiler (SBP),anacousticDopplercurrentprofiler (ADCP),aCTDunit,a
turbiditysensorandacamera.
11.2.4.3 MUNIN[KongsbergMaritime]
TheMUNINisacompactpropeller-drivenAUVdesignedbyKonsbergMaritimeforhighqualitydataandposition
accuracy.TheMUNINcombinesthemostrelevanttechnologyofHUGINandREMUSfamilies,alongwithsome
newdevelopments.Thetorpedo-shapedvehicleconsistsoffivemodularsections:1)thenose,withconductivity-
temperature(CT)andforward-lookingsonar(FLS);2)replaceablebatterymodule;3)navigationandpayload
module;4)controlandpermanentbattery;5)tailwithcNODEelectronicsandtransponder.Thevehicleusesthe
sameoperatingsoftwareanduserinterfaceasHUGINmodels.
TheMUNINincludestwo18MJbatteries,oneofthemremovable,foranenduranceof12to24hours.TheAUV
reachesamaximumspeedof4.5kts.Theinformationofaninertialmeasurementunit(IMU),aDopplervelocity
log(DVL),GPSandsingleormulti-transponderacousticcommunications(UTP,SSBL(SuperShortBaseline),SBL
(ShortBaseline)andLBL(LongBaseline))isassimilatedbyareal-timeaidedinertialnavigationsystem(AINS)for
accurate navigation. The communication is established through an acoustic link (cNODE),WiFi and Iridium
satellite. The suite of sensors available for the MUNIN include a multibeam echosounder (MBE), a dual-
frequencysidescansonar(SSS),aninterferometricsyntheticaperturesonar(ISAS),asub-bottomprofiler(SBP),
aconductivity-temperatureunit(CT)andastillcamera.
11.2.4.4 BluefinFamily[BluefinRobotics]
The Bluefin is a propeller-driven AUV developed by Bluefin Robotics for civil, commercial and military
applications.FivemodelsformtheBluefinfamily:thecompact,two-manportableAUVBluefin9and9M;the
modularandversatileAUVBluefin12Dand12S;andthehighpayloadcapacityAUVBluefin21.Bluefin9,9M
and12Sarebettersuitedforinshoreoperations,duetotheirlimiteddepthrangeof200to300m;theBluefin
12DandBluefin21canbeoperatedindeepwaters,upto4500mforthelastmodel.
Each model is powered with a different number of Lithium-Polymer batteries, according their weight and
requiredendurance.The9and9Mmodelscanbesentona10to12hourmission;thelargermodelsofferan
improvedenduranceof25-30hours.TheBluefin9and12Sareequippedwithasimplernavigationsystembased
onaninertialmeasurementunit(IMU),aDopplervelocitylog(DVL),asoundvelocitysensor(SVS),acompass
andaGPSunit;therestofthemodelsuseaninertialnavigationsystem(INS)combinedwithIMU,DVLandSVS
units.Allmodelsreachamaximumspeedof5kts.Thecommunicationssystemiscommontoallmodelsand
includesanRFlink,acoustictrackingandIridiumsatellitecommunications.Thesensorpayloadvariesfromone
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model toanother,but typicalavailablesensorsaredual-frequency, interferometricanddynamically focused
sidescansonars(SSS),syntheticaperturesonar(SAS),multibeamechosounder,conductivity-temperatureprobe
(CT),backscattersensorandturbidityprobe,amongothers.ThesoftwareprovidedwiththeAUVprovidesan
interfaceforallphasesofamission:planning,execution,monitoringandpost-processing.
11.2.4.5 AFamily[ECAGroup]
A9-MandA18-Darepropeller-drivenAUVdevelopedbytheECAgroup.AlthoughtheAfamilyofAUVconsists
ofseveralmodels,thetwopresentedinthissectioncoverthetechnologyrequirementsformostapplications.
TheA9-M is a compact,man-portable vehiclewith lowmagnetic and acoustic signatures designed to cover
depth-rangesofupto200m;theA18-Disamid-sizedeepwaterAUVwithanextendedenduranceandflexible
sensorpayload.
With3timesmorepowercapacitythentheA9-M,theA18-Doffersanenduranceof24h,comparabletothe
maximum20hoftheA9-M.Themaximumspeedinbothmodelsapproximates5kts.Thenavigationsystem
includesand inertialmotionsensor (INS),aDopplervelocity log(DVL),GPSand, intheA18-D,anultra-short
baseline unit (USBL). Both models are equipped with RF link, WiFi, acoustic tracking and Iridium satellite
communications.ThepayloadcapacityofA18-DishigherthaninA9-Dandsupportsalargernumberofsensors,
whichareoptionalforthecompactmodel.Thesuiteofpossiblesensors,bothstandardandoptional,includesa
singleordual-frequencysidescansonar(SSS),asoundvelocityprofiler(SVP),aforward-lookingsonar(FLS),a
multibeam echosounder (MBE), a CTD unit, environmental sensors, a sub-bottom profiler (SBP) and an
interferometricsidescansonar.
11.2.4.6 ARTEMI,ACESandAutoCat[MIT]
ARTEMIS,ACESandAutoCatwerethefirstautonomoussurfacevehicles.DesignedanddevelopedattheMIT
SeaGrantCollegeProgrambetween1993and2000,theseearlydesignsinspiredotherASVprogramsbeyond
MIT[Manley,2008].Thegoalwastodevelopalightautonomoussurfacevehicletoperformasanavigationand
communicationlinktoanAUV,capableofaccuratesurveyingandsuitableforeducationalpurposes.
ARTEMIS is a 1.37 m long scale replica of a fishing trawler developed in 1993, originally designed to
autonomouslycollectbathymetrydataintheCharlesRiver, inBoston(Manley,2008;Vanecketal,1996). Its
smallsizelimiteditsenduranceandseakeeping,butmadeiteasytobetransported,deployedandrecovered.
Thevehiclewasequippedwithanelectricmotorandservoactuatedrudderandincludedautomaticheading
control and DGPSway point navigation. The installation of a radiomodem allowed for human supervisory
control.
ACES (AutonomousCoastal ExplorationSystem) is a small catamaran1.4m longand0.4mwidedeveloped
between1996and1997.Thevehiclewasoutfittedwithhydrographicsurveysensors.Moreversatileandstable
thantheARTEMIS,theACESwasequippedwithtwocommercialhullslinkedbyasteelstructure,navigationand
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controlsystems,a3.3hpgasolineengineforpropulsion,batteriestoprovidepowertothecomputers,anda
generatortorechargethebatteries.Thevehiclewascharacterisedbycruisingandmaximumspeedsof2and
2.25kts,a30kgpayloadand4hoursendurance(Manley,1997).
AutocatistheupgradedversionoftheACEScatamaranandwasfirsttimetestedinthesummerof2000(Manley,
2000;2008).AutoCatis1.8mlongandcantravelatspeedsof8m/sthankstoanelectrictrollingmotor,which
replacedtheoriginalgasolinepropulsionsystemoftheACES.PayloadsensorsincludeDGPSandoceanographic
equipment,butcanbeoptionallyoutfittedwithasub-bottomprofiler,communicationslinkandSONAR.Last
trialswerecarriedoutin2000anddespitetheirhistoricalvalue,itisunlikelythatthesemodelsarestillavailable
foroperation.
11.2.4.7 MeasuringDolphin[MESSIN]
TheMeasuringDolphiniscatamaran-shapedASVwithfiberglasshullsdevelopedundertheMESSINprojectin
Rostock(Germany)andsponsoredbytheGermanBMBF(Cacciaetal.,2009;Caccia,2006).Designedfortrack
guidanceandcarryingmeasuringdevicesinshallowwater,thisvehiclewasequippedwithhighlyaccurateDGPS
positioning,compassandautomaticcoursecontrol.TheMeasuringDolphinwasequippedwithonepropeller
on each hull and a hybrid energy supply system – batteries and internal combustion for electric power
generation.
11.2.4.8 Delfim[DSORLab]
TheDelfimisasmallcatamaran-shapedASVdesignedanddevelopedbytheDSORlabofLisbonIST-ISR,under
theEUfundedprojectASIMOV(AdvancedSystemIntegrationforManagingthecoordinatedoperationofrobotic
OceanVehicles) (Alves, 2002; Caccia et al., 2009; Caccia, 2006; Pascoal, 2000) This vehiclewas designed to
performautomaticmarinedataacquisitionandtoserveasanacousticcommunicationrelaybetweenanAUV
and a support vessel. TheDelphimwas equippedwith an 80 km rangeRF link for communicationwith the
supportvessel,afixedGPSstationandtheon-shorecontrolcenter.
11.2.4.9 Springer[UniversityofPlymouth]
TheSpringerisacatamaran-shapedASVdevelopedbytheUniversityofPlymouth(UK)fortracingpollutants.
Thevehicle is 3m longand1.5mhigh. SpringerwasdevelopedbyMarineand IndustrialDynamicAnalysis
ResearchGroup(MIDAS)atPlymouthUniversityasacosteffectiveandenvironmentally friendlyASV. Itwas
designedprimarily forundertakingpollutanttracking,andenvironmentalandhydrographicsurveys inrivers,
reservoirs,inlandwaterwaysandcoastalwaters,particularlyshallowwaters.Springeralsoservesasatestbed
platformforresearchinintelligentnavigationsystemsandsensorandinstrumentationtechnology.
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11.2.4.10 ROAZandROAZII[LSA]
ROAZIandROAZIIaretwocatamaran-shapedASVdevelopedbyLSA(LaboratóriodeSistemasAutónomos)at
ISEP (Instituto Superior de Engenharia do Porto). ROAZ I is a small fiberglass ASV designed to perform
environmentmonitoring,bathymetrymappingandsupport integratedoperationswithmultipleautonomous
vehicles in riverineandestuarineareas;ROAZ II is amedium-sizeHDPEASVdesigned foroceanoperations,
namelyhydrographicandbathymetrysurveying,andsupporttosecurity,searchandrescueoperations.Both
vehicles were used as a test beds for the development of navigation, control and collaborative operations
algorithms(Sonnenburg,2013;LSA30).
11.2.4.11 C-Enduro[ASV]
DevelopedbyASV, theC-Enduro is ahighendurance carbon fibre, self-righting catamaran. It isdesigned to
operateinallmarineenvironmentswithanenduranceofupto3monthsandhasgreatversatilityforarangeof
applications.Itssensorpayloadincludespassiveacousticmonitoring(PAM)andacommunicationlinkbetween
underwatervehicleandmonitoringstation.Forpowergeneration,theC-Enduroincorporatesdiesel,solar,wind
andwave powered options. ASView systemoffers threemodes of operation: direct, semi-autonomous and
autonomouscontrol(ASV31).
11.2.4.12 Viknes[NTNU&MarineRobotics]
TheViknes is anASVboatdesignedanddevelopedby theNorwegianUniversityof ScienceandTechnology
(NTNU) in conjunctionwithMarine Robotics. The vehicle is equippedwith inboard Yanmar 184 HPmotor,
boastingatopspeedofupto20kts(Sonnenburg,2013;Loe,2008;Viknes32].
11.2.4.13 Mariner[NTNU&MarineRobotics]
TheMariner is an automated inflatable-hull craft designed and developed by the Norwegian University of
ScienceandTechnology (NTNU) inconjunctionwithMarineRobotics.TheMariner isequippedwithadiesel
enginewithwaterjetpropulsion,withatopspeedofmorethan30knots.VehicleControlStation(VCS)isthe
interfaceusedformissioncontrolandmonitoringon-board.ThevehicleincludesaVHF/UHFradiolinkwitha
rangeofupto15km(MaritimeRobotics33).
30 http://www.lsa.isep.ipp.pt/roaz_home.html 31 http://asvglobal.com/product/c-enduro/ 32 http://www.viknes.no/ 33 http://www.maritimerobotics.com/systems/mariner/
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11.2.4.14 C-Worker6[ASVUnmannedMarineSystems]
TheC-Worker isanunmannedrigid-hullcraftdevelopedbyASVUnmannedMarineSystemsforsecurityand
surveillanceasprimaryapplications,andisthemostversatilemodeloftheC-Workerfamily.Thisvehicleishighly
survivable,lightandrobustandischaracterisedbyamodularpayloadbayforeasyintegrationofstandardor
custompayloads.TheC-Worker6hasbeendeployedwithaSeichePAMarrayandremotemonitoringsystem,
makingseveralreal-timedetections.
11.2.4.15 C-Stat[ASVUnmannedMarineSystems]
TheC-Stat is a small single hull automated vehicle developed byASVUnmannedMarine Systems,with the
capability toremainonstationforextendedperiods.Amongthepossibleapplicationsarethepositioningof
subseaequipment,surfacetounderwatercommunications,oceanographicdatacollectionandsecuritysupport.
TheC-Statispoweredbydieselgeneratortochargethebattery,offeringanenduranceofupto4days.
11.2.4.16 ASV-300[C&CTechnologies&ASV]
The ASV-3000 high-endurance unmanned semi-submersible vehicle, akin to a small single-hull platform,
designedbyC&CTechnologiesandASV.PreviousdesignsofthistypeweretheORCA(OceanographicRemotely
Controlled Vehicle) fromNRL and theRMS (RemoteMinehunting System) from LockheedMartin (Wolking,
2011).
11.2.5 Autonomousunderwaterbuoyancygliders
11.2.5.1 SlocumG2electric[TeledyneWebb]
TheSlocumG2electricgliderismanufacturedbyTeledyneWebbResearch(TWR).Ithasa1000mdepthrating
witheitherdeepwaterorlittoralbuoyancyenginestooptimiseefficiency,amodularpayloadcapacitywitha
largevarietyofsensorsalreadyavailableonthemarketandaproventrackrecordindeliveryandoperation.It
hasahighenduranceof25toover365days,usingeitheralkalineor lithiumbatteries.Communicationisvia
Iridium/freewave or acoustic modem. A number of passive and active acoustic sensors have already been
integratedintotheSlocumgliderandusedtodetectmarinemammalsandfish(Table8).Ahybridversionofthis
glider exists which utilises a thruster with a collapsible propeller to provide momentum in sub-optimal
conditions.AllnewvehiclesareshippedwiththiscapabilityasastandardfeatureandexistingG2gliderscanbe
upgraded.Finally,althoughnotyetavailableascommercialofftheshelf(COTS)technology,athermalgliderhas
beendeveloped,wherepropulsionisfuelledbychangesintheoceantemperatureratherthanbatterypower
thatallowsgreaterlongevitytoanyonemission(3to5years).
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11.2.5.2 Seaglider[Kongsberg]
DesignedattheUniversityofWashington(Eriksenetal.,2001),andmanufacturedbyKongsbergMaritime,the
Seaglider has a flooded aft section used to carry self-contained instruments. Steering of the vehicles is
undertakenbymovementoftheinternalmasses(e.g.batteries)bothforeandaftandforrotationaroundthe
axis.TheSeagliderispoweredbylithiumbatterieswithdeploymentsofupto10months,andcommunication
mayeitherbeusingIridiumorthroughacousticmodem.Anumberofpassiveandactiveacousticsensorshave
alreadybeenintegratedintotheSeagliderandusedtodetectmarinemammalsandfish(Table8).
11.2.5.3 Spray[ScrippsInstitutionofOceanography]
Designed at Scripps Institution of Oceanography (Sherman et al., 2001), the Spray gliderwas commercially
manufacturedbut isnotcurrently.TheSprayglider is similar to theSlocum,butwithamorehydrodynamic
shapeandhasbeenoptimisedforlong-rangeandadepthratingof1,500m.Turningisinitiatedbyrolling,like
theSeaglider.TheSprayuseslithiumbatteriesandmaximummissiondurationis~330days.
11.2.5.4 SeaExplorer[ACSA-ALCEN]
TheSeaExplorer,manufacturedbyACSA-ALCEN,hasbeendesignedtobepoweredbyrechargeablelithiumion
batteries, and to be faster (1 knot) than the other buoyancy gliders. It has a modular design including an
independent payload section located at the front of the vehicle. The SeaExplorer does not havewings and
communicationisbyIridium,radiooracousticmodem.Therechargeablebatteriesenableamaximummission
durationof~2months(1,200km)ononebatterycharge,refuellingtimeis20hoursandbatteryreplacement
every10years.SimilartotheSlocumhybrid,athrusteroptionisavailabletoenabletheglidertooperate in
powered AUV mode. A number of sensors including an acoustic recorder are already integrated into the
SeaExplorer.
11.2.5.5 eFòlaga[Graal-tech]
TheeFòlagaisahybridgliderconceptuallydesignedattheInteruniversityCentreofIntegratedSystemsofthe
MarineEnvironment(ISME),andengineeredbyGraal-tech(Caffaz,2009).Itisalight-weight,lowmaintenance
vehicleoptimisedforhighmanoeuvrabilitytoundertakecoastaloceanography.TheeFòlagahasbeendesigned
totransitbetweenstationsusingjetpropulsion(inpoweredAUVmode)andthenundertakesverticalprofiles
throughchangingitsballast(likeabuoyancyglider).ItusesaNiMhbattery,andhasanenduranceof6hours(at
maxpower),cantravelatspeedsupto4knotsandhasamaximumdepthof80m.Communicationisthrough
eitheraradiolinkoracousticmodem.TheeFòlagahasapayloadbaythatcanhostCOTSsensors,typicallyCTDs
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havebeentestedsofar.However,trialsoftheeFòlagatotowapassiveacousticarrayhavebeenundertaken
formaritimesurveillance34.
11.2.5.6 Coastalglider[ExocetusDevelopmentLLC]
TheCoastalgliderwasdevelopedbyAlaskaNativeTechnologies,asamilitarizedgliderproduct (Imlachand
Mahr,2012).Thesegliders,oftentermedExocetus,weredesignedtooperateincoastalwaterswithhighdensity
gradientsandfastcurrentspeeds.Theyhaveabuoyancyengine7timeslargerthantheSlocumandSeaglider,
enablingthemtooperateoveralargerangeofwaterdensities.Alkalineorlithiumbatteriesenable14-60days
missionduration,andthebuoyancyglidercanaccommodatecurrentspeedsupto2knots.Communicationis
implementedthroughIridium,freewave(lineofsight),wifioracousticmodem.Anumberofdifferentsensors
havealreadybeenintegratedintotheCoastalglider,includingomni-directionalactiveacousticsensors(Imlach
andMahr,2012).OceanSonicssmarthydrophoneshavealsobeenintegratedintotheCoastalgliderandused
toprovideaproof-of-conceptvesselandfishinggearcollisionavoidancetechniqueusingthemeasuredradiated
noisefromthefishingvessels(Wood,2016).
11.2.6 Self-poweredsurfacevehiclesanddriftingsensorpackages
11.2.6.1 WaveGlidersSV2andSV3[LiquidRobotics]
OneofthemostestablishedvehiclescurrentlyavailablearetheLiquidRoboticsSV2andSV3Wavegliders.These
consistofasurfacefloat,aboutthesizeofasurfboard,andarackoffinssuspendedseveralmetresbelowthe
surface.Asthegliderliftsonawave,thefinstiltupwardsslightlyandsurgeforwardthroughthewater.When
thewaverelaxes,thefinstiltbacktothehorizontalandtheweightofthestructuresupportingthefinsagain
causesittosurgeforwards.Payloadscanbemountedinthefloatinghull,attachedtothesub-surfaceunitor
towedasternofthesub-unit. TheSV3 isaslightly largerversionoftheSV2andalsohasasmallelectrically
drivenpropelleronthesub-unitwhichcanbeusedincalmconditionswhennowavepowerisavailable(solar
power for this is generallymoreavailableon calmdays).Waveglidershavebeen successfully trialledwitha
numberofPAMsensorsformarinemammaldetection.
11.2.6.2 AutoNaut[MOST]
DevelopedbyMOST (AutonomousVessels) Ltd35,AutoNaut is another self-powered,wave-energypropelled
vehicle.Availableindifferentsizes:largerboatsshowhigherspeeds,greatercarryingcapacityandmorepower
forpayload.Thesevehiclesaregenuinelyself-poweredwithprimarypropulsionbydirectwavepropulsion(pitch
androll)usingWaveFoilTechnology.Solarpanelsharvestenergytopowerthesensorpackagewithbackup
34 http://www.sea-technology.com/features/2014/0214/8.php 35 http://www.autonautusv.com/
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providedbybatteryandmethanolfuelcells.TheAutoNatdisplaysminimalnoise,issimpletodeploy/retrieve
andhasamissionenduranceofseveralmonths.TheAutonauthasperformedwellinseatrials,e.g.withPAM
sensors.
11.2.6.3 DASBRDriftingBuoy[NOAA]
TheDASBRbuoyhasbeendevelopedbyEmilyGriffithsandJayBarlowofNOAASouthWestFisheriesasalow
cost alternative to autonomous surface vehicles. Components, including an autonomous recorder and two
satellite trackers, can be purchased for around $5,000. Once deployed the buoys drift with the current.
Considerablecostscanthereforebeincurredintrackingdownandrecoveringthebuoys.
11.2.7 PAM
11.2.7.1 Decimus[SAInstrumentationLtd]
Thisisatthehighpowerendofthespectrumintermsofpowerconsumptionbutalsocontainsasophisticated
arrayofprocessingalgorithmswhichcanworkonuptofourchannelsata500kHzsamplerate.Ithassuccessfully
beenintegratedintobuoysandWavegliders.Datacanberelayedinnearreal-timeusingwirelessmodemsor
uploadeddailythroughcellphonenetworks.
11.2.7.2 DMON[WHOI]
DevelopedbyMarkJohnsonusingtechnologiesfromtheDTAG(Johnson,2003),thisultra-lowpowerdevicehas
successfullybeenprogrammedandintegratedintoaSlocumunderwaterglidertoautomaticallydetectbaleen
whales (Baumgartneretal.,2008;Baumgartneretal.,2013;Baumgartneretal.,2014b;Baumgartner,2014;
BaumgartnerandFratantoni,2008).TheDMONissubjecttoexportcontrolrestrictionsfromtheUS.
11.2.7.3 SoundTrapHFandSoundTrap4Channel[MarkJohnson]
Also developed by Mark Johnson, using similar technology to the DMON, this is another ultra-low power
recordingdevicewhichiscommerciallyavailablethroughOceanInstrumentsNewZealand.Thelatestsoftware
allows simultaneous recording at low frequencies combinedwith high frequency click detection,which can
extendrecordingdurationtoseveralmonths.Afour-channelversioniscurrentlyunderdevelopment.
11.2.7.4 SeicheReal-timeSystem[Seiche]
TheSeichewirelessPAMsystemisahighlyconfigurablesystemwhichcanbeutilisedfortruereal-timedata
transmission,withawirelesstransmissionrangeofupto10kms,typicallyusing2.4GHz/5GHzbands.Ithastwo
modestoenablereal-timemonitoring;Inthefirstmode,ananaloguetodigitalsamplingdeviceisinstalledwithin
theunitonthetransmittingplatform(e.g.anASV)andthefulldataset istransferredtoaPCatthereceiver
station (e.g. a support vessel) for processing in PAMGuard. The operator can then view and utilise the full
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requiredfrequencyrangeformonitoringintruereal-time.Thisconfigurationrequireshigherbandwidthtoallow
receiptoffulldatasetatthereceiverstation.Inthesecondmode,anelectronicprocessingunitisinstalledon
thetransmittingplatformwithintheunitwheretheaudiosignalisprocessedthroughPAMGuard.Theoperator
atthereceivingstationhasfullcontrolandviewingaccessofincomingLFandHFdatawithinPAMGuarduser
interfaceviaa remote software link. This configuration runson lowerbandwidthandoffersa longer range.
Additionally,nodatalossissufferedshouldthelinkbelostasitispossibletorecorddataatthebaseunit.Both
configurationshavebeensuccessfullytrialledontheASVC-WorkerandC-Enduro,resultinginanumberoflive
detectionsofarangeofspecies.
11.2.7.5 WISPRBoard[OregonStateUniversity/Kongsberg]
TheWISPR(WidebandIntelligentSoundProcessor&Recorder)boardwasdevelopedandismanufacturedby
EmbeddedOceanSystems.TheWISPRsystemisamixedsignalmotherboardthatprocessesandlogsacoustic
signals.Itwasspecificallycreatedforapplicationswherebothspaceandpowerarelimitedsuchasfloats,gliders
andmoorings.
11.2.7.6 C-PODandC-POD-F[CheloniaLtd]
TheC-PODisawell-establishedclickloggingtechnologywidelyusedtostudyharbourporpoiseandothersmall
odontocete species around theworld. A simple processor logs key descriptors of each transient sound and
offlineanalysissoftwareclassifiestheseintoclicktrains.TheC-PODwastobetrialledontheC-Endurovehicle,
butthevehiclefailed.TheC-POD-F,whichincorporatesbetterprocessingandcollectsmoreaccuratedataon
eachloggedevent,willsoonbeavailable.
11.2.7.7 AutoDetectionBuoys[Cornell]
DevelopedforthedetectionofNorthAtlanticRightWhalesfrombuoysintheapproachestoBostonharbour,
software searches for specific sound types and sends a sampleof candidatedetections to shore forhuman
verification in near real time. To date, this has only been used onmoored buoys, butmay be suitable for
deploymentonautonomoussurfacevehicles.
11.2.7.8 A-tag[JapaneseScienceandTechnologyAgency]
TheA-tagisdevelopedbyTomomariAkamatsuoftheJapaneseScienceandTechnologyAgency,andis,similar
totheC-POD,aclickevent logger.Twochannelsareusedwhichenables ittocalculatebearingstodetected
sounds. It operates fully autonomously and is small enough that it could potentially be incorporated into
submarinegliders.
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11.2.7.9 SM2M/SM3M[WildlifeAcoustics]
Thisisawell-establishedrecordingonlyunitwhichcansampleatupto384kHz.Theyareusuallyusedinmoored
systemsbuthavealsobeenusedindriftingbuoysystems.
11.2.7.10 AUSOMS-mini[AquaSound]
This isanotherrecordingonlysystem,samplingat44.1kHz. Ithasrelativelyshortrecordinglifetimebuthas
beendeployedongliders.LittleinformationisavailableoutthisproductsincethewebsiteisonlyinJapanese.
11.2.7.11 SDA14andSDA416[RTSYS]
DevelopedbyRTSYS,thesedevicescansampleatupto1MHz.Fourchannelsareavailableperboardandboards
canbeconnectedformorechannels.Thesystemscanmeasurenoise in1/3octavebandsandperformclick
eventdetection.TheyhavebeendeployedonsmallsizeAUV,Profilers,Surfacegliders,andBuoys.
11.2.8 AAM
11.2.8.1 ES853[Imagenex]
ThisisafullycommerciallyavailablesystemthathasalreadybeensuccessfullyintegratedintotheSlocumG2
gliderandSeaglider(ogive).Ithasafrequencyof120kHz,itsmaximumoperationdepthis1,000mandithasa
maximumdetectablerangeof100m.Itisprogrammabletohaveeitherafixed0.25Hzpingrate(inglidermode
forworkingonaSeaglider),ortobepolledbytheglider(Slocumscenario).Itsoperationalweightis1kginair
anditiscategorisedashavingnohazardclass.Theonlyinterferingfactortotheoperationofthissensoristhe
presenceofotheractiveacousticinstrumentsinthevicinity.
11.2.8.2 WBATandWBATmini[Kongsberg/Simrad]
ThesearebothfullycommerciallyavailablesystemsbuthavenotyetbeenintegratedintoanyAUVplatforms.
TheWBAT is a fully autonomousWideBand Autonomous Transceiver that stores data internally, operates
autonomously,andhasan internalbattery. It isastand-aloneunit thatcouldbedeployedonamooring,or
attachedtoanyROVorlargerAUV.TheWBAToperatesatthestandardSimradfrequenciesof38,70,120,200
and333kHz.TheWBATminiisbeingspecificallydesignedtobeintegratedintoverysmallvehiclessuchasa
SeagliderorsmallAUV.TheWBAToperatesusingbothnickelrechargeablebatteriesandlithiumbatterieswhich
affectsitshazardclass,whilsttheWBTminidoesnothavealistedhazardclass.Theonlyinterferingfactorto
theoperationofthesesensorsisthepresenceofotheracousticinstrumentsinthevicinity.
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11.2.8.3 SontekADP[Sontek]
The Sontek ADP (Acoustic Doppler Profiler) is a fully commercially available system that has already been
successfullyintegratedintotheSCRIPPSSprayAUVglider(PowellandOhman,2015).Ithasamaximumprofiling
rangeof180mandoperatesat250kHz.
11.2.8.4 NortekADCP[Nortek]
The Nortek ADCP (Acoustic Doppler Current Profiler) is a fully commercially available system designed to
measurewatercolumncurrentsusingacousticDopplertechnology.A1MhzAquadoppprofilerhasalreadybeen
successfullyintegratedintotheSlocumG2glider(BaumgartnerandFratantoni,2008).TheAquadoppprofiler
weighslessthan3.5kg,hasamaximumprofilingrangeof100mandcanbeprogrammedtoeitherrecorddata
internallyortotransmitdatabyphoneorradiomodem.
11.2.8.5 DT-X-SUB[BioSonics]
ThisisafullycommerciallyavailablesystemandaversionofithasbeenintegratedintotheWaveglider(Greene
etal.2015).TheBioSonicsDT-X-SUB isa self-containedunit,designed forautonomousdeployments,witha
programmabledutycyclewhichenablesittobedeployedforlongtermstudies.Theunitcontrolsafullyfunction
split-beamechosounderandworkswithtransducersoperatingat38,70,120,200and420kHz.Environmental
performance limits include wind and wave conditions as this can impact the stabilisation of the towed
instruments.Theonlyotherinterferingfactortotheoperationofthissensoristhepresenceofotheracoustic
instrumentsinthevicinity.
11.2.8.6 AZFP[ASL]
The Acoustic Zooplankton Fish Profiler (AZFP) is currently commercially available as amoored system. It is
availablewith38,67.5,125,200,455,769and2,000kHztransducers,hasadepthratingupto1,000m.The
AZFPcanoperateinaninternallyrecordingmodeoritcanmakedataavailableinrealtime.Thecompanyoffers
compactAZFPpackagesforintegrationintoAUVandtowedbodies,butwearenotawareofthishavingbeen
testedyet.
11.2.8.7 ModularVR2CandVMT[Vemco]
TheVEMCOVR2Cacousticreceivers(frequency=69kHz)havebeenintegratedintoaSlocumG2glider(Haulsee
etal.2015).Theenvironmentalperformancelimitsofthesesystemsisthemaximumdepthatwhichtheycan
operate,whichis500mforthemodularVR2Cor1,000mfortheVMT.Theybothworkbydetectingthepings
fromdeployedacoustictagswhichcanbeattachedtoanyanimallargeenoughtobetagged.
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11.2.8.8 Gemini720i[Tritech]
Thisisafullycommerciallyavailablemultibeamimagingsonarsystemwhichissuitablefordeploymentonavery
small ROVor AUV. The standard unit has environmental performance limits of 300mdepth and operating
temperatures between -10 and 35°C, however, a 4,000m depth ratedmodel is available for deeperwater
operations.ObjectdetectionandtrackingisavailablewiththeGeminiSeaTechsoftwarewhichallowstargetsto
beclassified.Thissystemhasbeenpreviouslyusedtodetectandclassifymarinemammaltargetsinreal-timeto
allowformitigationactionstobetakeninatimelymanner.
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11.3 Comparisonmatrices
11.3.1 Listofplatforms
Table12.Autonomousplatformsincludedinthisreview,theirsystemclass(poweredandunpoweredASV,propellerandgliderAUV,lighter-than-airaircrafts(l-t-a)UASs,kitesandpoweredfixedwingUASs),systemname,manufacturerand/ordesigner/originatinguniversityaswellastheirtechnologyreadinesslevelasdefinedinTable10.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.
Systemclass
Systemname ManufacturerDesigner/
TechnologyreadinesslevelOriginatingUniversity
ASV
-po
wered
ASV-6300
ASV
Prototypesystem
C-Cat2 Firstofakindcommercialsystem
C-Enduro
FullcommercialapplicationC-Stat
C-Target3
C-Worker4 Firstofakindcommercialsystem
C-Worker6 Fullcommercialapplication
C-Worker7Firstofakindcommercialsystem
C-WorkerHydro
Delfim InstituteforSystemsandRobotics(Lisboa) AppliedResearch
Mariner NTNUandMaritimeRobotics Fullcommercialapplication
MeasuringDolphin UniversityofRostock(Germany) UniversityofRostock(MESSIN) Firstofakindcommercialsystem
ROAZI LaboratóriodeSistemasAutónomos Prototypesystem
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Systemclass
Systemname ManufacturerDesigner/
TechnologyreadinesslevelOriginatingUniversity
ROAZII
RTSYSUSV RTSYS Demonstrationsystem
ASV
-un
powered
AutoNaut2
MOST(AutonomousVessels)Ltd FirstofakindcommercialsystemAutoNaut3
AutoNaut5
AutoNaut7
WavegliderSV2LiquidRobotics Fullcommercialapplication
WavegliderSV3
AUV-glider
ALBAC TokaiUniversity Smallscaleprototype
Coastalglider Exocetus AlaskaNativeTechnologies Fullcommercialapplication
Deepglider UniversityofWashington Demonstrationsystem
eFòlagaIII Graal-Tech IMEDEAInstitute Firstofakindcommercialsystem
LiberdadeXwing/Zray ONR Prototypesystem
Petrel TianjinUniversity,China n.p.
SeaBird KyushuInstituteofTechnology Prototypesystem
SeaExplorer ACSA Fullcommercialapplication
Seaglider(ogive) Kongsberg UniversityofWashington Fullcommercialapplication
SlcoumG2hybrid
TeledyneWebb Webb/WHOIFullcommercialapplication
SlocumG2glider
SlocumG2thermal Demonstrationsystem
Spray SCRIPPS Fullcommercialapplication
153
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
Systemname ManufacturerDesigner/
TechnologyreadinesslevelOriginatingUniversity
Sterneglider EcoleNationaleSuperioreD'IngenieursBrest n.p.
TONAI:TwilightOcean-ZonalNaturalResourcesandAnimalInvestigator
OsakaPrefectureUniversity,TaijiWhaleMuseum,Cetus
Basicresearch
AUV-prop
eller
A18-DECAGroup Fullcommercialapplication
A9-M
Bluefin-12D
BluefinRobotics Fullcommercialapplication
Bluefin-12S
Bluefin-21
Bluefin-9
Bluefin-9M
HUGIN1000(1000mversion)
KongsbergMaritime Fullcommercialapplication
HUGIN1000(3000mversion)
HUGIN3000
HUGIN4500
MUNIN n.p.
REMUS100
Hydroid(KongsbergMaritime)+OSL(WHOI) FullcommercialapplicationREMUS3000
REMUS600
REMUS6000
RTSYSAUV RTSYS Demonstrationsystem
UAS
kite
SwanX1 FlyingRobotsSA Demonstrationsystem
154
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
Systemname ManufacturerDesigner/
TechnologyreadinesslevelOriginatingUniversity
UAS
l-t-a DesertStar10. AllsoppHelikitesLtd. Fullcommercialapplication
OceanEye MaritimeRoboticsAS Fullcommercialapplication
UAS-p
owered
-fixed
BRAMORC4EYE
C-Astral FullcommercialapplicationBramorgEO
BramorrTK
Fulmar Thales Fullcommercialapplication
Jump20 ArcturusUAV Fullcommercialapplication
PenguinB Uavfactory Fullcommercialapplication
ScanEagle Insitu Fullcommercialapplication
UX5 TrimbleNavigationLimited Fullcommercialapplication
155
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
11.3.2 Platformtechnicaldimensions
Table13.TechnicaldimensionsoftheautonomousplatformslistedinTable12.Givenaretheirmissiondurationandfactorslimitingthemissionduration,theirminimum,maximumandtypicalspeed,theirverticaloperationalrange,theirenvironmentalperformancelimits.Theiroperationalsizeandweightaswellaspacking/freightweightandsizearegivenandtherangetheycanbecontrolledwithin.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:l-t-a:lighter-than-airaircrafts,NA:notapplicable,n.p.:notprovided,LWH:length,width,height.*unlessotherwisespecified
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
ASV
-po
wered
ASV-6300 2-4days Fuelcapacity n.p. 6 n.p. NA NA n.p. ?x?x6.3 n.p. n.p. n.p. n.p.
C-Cat2
3-9hoursdependentonbatteryoption
Dieselcapacity
Stationkeeping(0)
5 3 NA NA
-10to45C(applicationspecificrangeavailable)
2.4x1.2x0.8LWH(0.2draft)
1002.4x1.2x0.8LWH
n.p.~2utilisingRFcomms
C-Enduro 90days
Energycapacity,sunlightanddiesel
7 3 NA NA4.2x2.4x2.8LWH(0.4draft)
500 5x2.6x2 n.p.
UnlimitedviaIridiumorothersatellitecomms/2-20+utilisingRFcomms
C-Stat 4days Fuelcapacity n.p. 3.7 n.p. NA NA n.p. ?x1.2x2.4 450 n.p. n.p. n.p.
C-Target3
Upto12hoursdependentonapplication
Dieselcapacity
3 25 6 NA NA
-10to45C(applicationspecificrangeavailable)
3.5x1.4x1.2LWH
3253.5x1.4x1.2LWH
n.p.2-20+utilisingRFcomms
C-Worker4 48hoursDieselcapacity
Stationkeeping(0)
9 6 NA NA4.1x1.6x1.7LWH(0.4draft)
7004.1x1.6x1.7LWH
n.p.UnlimitedviaIridiumorothersatellitecomms/2-20+utilisingRFcomms
C-Worker630days,Stationkeeping
Dieselcapacity
6 4 NA NA5.8x2.2x4.75LWH(0.9draft)
4,0006.3x2.2x2.2LWH
n.p.
C-Worker7Dependentonapplication
Dieselcapacity
6 4 NA NA7.2x2.3x4.2LWH(0.9draft)
5,3007.5x2.3x2.5LWH
n.p.
156
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
C-WorkerHydro
6daysDieselcapacity
3 10 8 NA NA5.5x1.8x1.8LWH(0.9draft)
1,9005.5x1.8x1.8LWH
n.p.
Delfim n.p. Batterylife n.p. 5 n.p. NA NA n.p. ?x2x3.5 320 n.p. n.p. 80
Mariner50hours@5kts
Fuelcapacity n.p. >30 5 NA NA n.p.2.05x2.05x5.85
1,700 n.p. n.p. 15
MeasuringDolphin
3hours(batterypowered)or10hours(hybridpower)
Batterylife 0.5 4 n.p. NA NA n.p.3.3x1.8x1.5
250 n.p. n.p. n.p.
ROAZI n.p. Batterylife n.p. n.p. n.p. NA NA n.p.1.5x1x0.52
n.p. n.p. n.p. 3
ROAZII n.p. n.p. n.p. n.p. n.p. NA NA n.p.4.5x2.2x0.5
200 n.p. n.p. 3
RTSYSUSV 6hours Batterylife 1 6 3 0 0 Seastate 2x1x0.5 602.5x1.03x0.85
80 1
ASV
-un
powered
AutoNaut2 2weeks75WpPVpanel
0.25 2 1
surfacesensors;0.1
Towedsensors;20
Fullyoceancapable;stormproven
1x0.5x0.3 60 EuroPallet n.p. 5
AutoNaut3
3+months
Fuelformethanolfuelcell;anti-foulingsystem
0.5
3 2Towedsensors;50
2x0.4x05 1201.5x0.75x1.5
n.p.
oceanic;satellitecomms
AutoNaut5 4.5 3Towedsensors;100
3x0.8x1 2502.0x1.0x2.5
n.p.
AutoNaut7 6 4Towedsensors;200
4x1.1x1.5 4002.0x2.5x2.5
n.p.
WavegliderSV2
1year+Biofouling,Sunlightavailablity
0 2 1.25 NA NA
SurvivedSeaState8-9.Nopowerinflatcalm
~0.5x1x2x6depth
~100MultipleCrates,AirFreightOK
n.p. unlimited
157
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
WavegliderSV3
0.3 2.5 1.75 NA NA
SurvivedSeaState8-9.Electricalpropellersinflatcalmifsufficientsunlight.
~0.5x1x3x5depth
~150 n.p.
AUV-glider
ALBAC1dive(30minutes)
Releaseofdiveweight
n.p. n.p.0.5-1m/s
n.p. 300
Currents,verticaldensitygradients
1.4(length)x0.24(diameter)x1.2(wingspan)
45 n.p. n.p. n.p.
Coastalglider
14days(alkaline),60days(lithium)
Batterylife n.p.1m/s
1m/s Surface 200 Currents2.87(length)x0.33(diameter)
109
2.44x0.85x0.72shippingcarton
n.p. unlimited
Deepglider 380days Batterylife n.p.0.45m/s
0.25m/s
Surface 6,000
Currents,verticaldensitygradients
1.8(length)x0.3(diameter)x1.0(wingspan)
62 n.p. n.p. unlimited
eFòlagaIII 6-8hours Batterylife n.p. n.p. 1-2m/s Surface 50-100
Currents,verticaldensitygradients
2.0(length)x0.16(diameter)
31 n.p. n.p. n.p.
LiberdadeXwing/Zray
6months Batterylife n.p. n.p.0.5-1.5m/s
n.p. 300
Currents,verticaldensitygradients
6.1(wingspan)
680 n.p. n.p. n.p.
Petrel n.p. n.p. n.p. n.p.
0.5m/s(2.0m/sthrust)
n.p. 500 n.p.
3.2(length)x0.25(diameter)x1.8(wingspan)
130 n.p. n.p. n.p.
SeaBird n.p. Batterylife n.p. n.p. n.p. n.p. 10Currents,vertical
0.45(length)x0.71
5 n.p. n.p. n.p.
158
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
densitygradients
(width)x0.14(height)
SeaExplorer 2months Batterylife n.p. 1 1 Surface 700
Currents,verticaldensitygradients
2.0(length)x0.25m(diameter)x0.56(wingspan)x0.7(antenna)
59 n.p. n.p. unlimited
Seaglider(ogive)
10months Batterylife n.p. n.p.0.25m/s
Surface 1000
Currents,verticaldensitygradients
1.8-2.0(length)x0.3(diameter)x1.0(wingspan)
52 n.p. n.p. unlimited
SlcoumG2hybrid
360/50days(lithium/alkaline)
Batterylife
n.p.1m/s
0.25m/s
Surface200/1,000
Currents,verticaldensitygradients
1.5-2.15(length)x0.22(diameter)x1.2(wingspan)
54 n.p. n.p. unlimited
SlocumG2glider
360/50days(lithium/alkaline)
n.p.0.38m/s
Surface200/1,000
54 n.p. n.p. unlimited
SlocumG2thermal
3-5years n.p.0.38m/s
Surface 1,200 60 n.p. n.p. unlimited
Spray 330days Batterylife n.p. n.p.0.25m/s
n.p. 1,200
Currents,verticaldensitygradients
2.0(length)x0.20(diameter)x1.2(wingspan)
51
Boxdimensions1.6x0.6x0.6
92 unlimited
Sterneglider n.p. n.p. n.p. n.p. 1.3m/s n.p. n.p. n.p.4.5(length)x0.6(diameter
900 n.p. n.p. n.p.
TONAI n.p. n.p. n.p. n.p.0.2-0.5m/s
n.p. 60 n.p.1.65(length)x0.2(diameter)x
92 n.p. n.p. n.p.
159
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
1.03(wingspan)
AUV-prop
eller
A18-D 24hours
Batterylife
n.p. 6 3 5 3,000 n.p.0.5x0.5x5.2
550-650 n.p. n.p. n.p.
A9-M
20hourswithtwoenergysections
n.p. 5 3 3 200 n.p.0.23x0.23x1.98
70 n.p. n.p. n.p.
Bluefin-12D
30hours@3ktswithstandardpayload
Batterylife
n.p. 5 n.p.
Surfacenavigation(~0)
1,500 n.p.0.32x0.32x4.32
260 n.p. n.p. n.p.
Bluefin-12S
26hours@3ktswithstandardpayload
n.p. 5 n.p. 200 n.p.0.32x0.32x3.77
213 n.p. n.p. n.p.
Bluefin-21
25hours@3ktswithstandardpayload
n.p. 4.5 n.p. 4,500 n.p.0.53x0.53x4.93
750 n.p. n.p. n.p.
Bluefin-9
12hours@3ktswithstandardpayload(SSS,cameraandprobes)
n.p. 5 n.p. 200 n.p.0.24x0.24x1.75
60.5 n.p. n.p. n.p.
Bluefin-9M
10hours@3ktswithstandardpayload(SSS,cameraandbackscattersensor)
n.p. 5 n.p. 300 n.p.0.24x0.24x2.5
70 n.p. n.p. n.p.
160
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
HUGIN1000(1000mversion)
24hours@4kts(withMBE,SSS,SBPandCTD)
Batterylife(dependsonspeed,sensorconfiguration,environmentandmissionprogram)
2 6 n.p.
Surfacenavigation(~0)
1,000 n.p.0.75x0.75x4.5
850 n.p. n.p. n.p.
HUGIN1000(3000mversion)
24hours@4kts(withMBE,SSS,SBPandCTD)
2 6 n.p. 3,000 n.p.0.75x0.75x4.7
850 n.p. n.p. n.p.
HUGIN3000
60hours@4kts(withMBE,SSS,SBPandCTD) Supplyoffuel
usedinthesemi-fuelcell
2 4 n.p. 3,000 n.p. 1x1x5.5 1,400 n.p. n.p. n.p.
HUGIN4500
60hours@4kts(withMBE,SSS,SBPandCTD)
2 4 n.p. 4500 n.p. 1x1x5.5 1,900 n.p. n.p. n.p.
MUNIN 12-24hours
Batterylife(dependsonspeed,sensorconfiguration,environmentandmissionprogram)
n.p. 4.5 n.p.
1,500(available600version)
n.p.0.34x0.34x4
~300 n.p. n.p. n.p.
REMUS100
8hours@5kts;22hours@3kts
Batterylife(dependsonspeed,sensorconfiguration,environmentandmissionprogram)
n.p. 4.5 3
Surfacenavigation(~0)
100
Canbedeployedinroughweatherconditions
0.19x0.19x1.6
37 n.p. n.p. n.p.
REMUS3000
44hours@4kts(allsensorsoff)33hours@4kts(sonar
n.p. 4 3 3,000 n.p.0.36x0.36x3.7
335 n.p. n.p. n.p.
161
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
+cameraon)
REMUS60045hours@4kts(allsensorson)
n.p. 5 4
600(available1,500configuration)
n.p.0.32x0.32x3.25
240 n.p. n.p. n.p.
REMUS6000
22hours@4kts
n.p. 5 n.p.
6,000(available4,000configuration)
n.p.0.71x0.71x3.84
862 n.p. n.p. n.p.
RTSYSAUV 20hours Batterylife 3 15 4to10 2 250Seastateforrecovery
150mm(diameter)x2m(long)
30Pelicase2.3
50 5
UAS-
kite
SwanX1 n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.3.5x2.3x2.3
n.p. n.p. n.p. n.p.
UAS-l-t-a DesertStar
10.n.p. n.p. n.p. n.p. n.p. n.p. n.p.
Wind<72km/h
12x9 n.p. n.p. n.p. n.p.
OceanEye days n.p. n.p. n.p. 0 n.p. n.p. n.p. n.p. n.p.1.2x0.8x1.6
425 n.p.
UAS-p
owered
-fixed
BRAMORC4EYE
3hoursWind,airtemperature,sensortemperature,humidity,visibility,cloud,fog,rain,snow
32 58.32 58 30,000 5,000 Wind<15m/s0.02x2.30x0.96
4.5
Twoflightboxes.Catapultbox:125x45x30;Bramorbox:115x55x45
Catapultbox:28;Bramorbox:35
30
BramorgEO 2.5hours
BramorrTK 2.5hours
Fulmar 6-12hours n.p. n.p. n.p. 54 n.p. 800,000Wind<70km/h
n.p. 20 n.p. n.p. 70-90
Jump20 9-15hours n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
PenguinB 20+hours n.p. n.p. 69.98 43 n.p. n.p. n.p.?x3.3x2.27
21.5 n.p. 10 n.p.
162
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Missionduration Speed(knots)*Verticalrange
(m) Operational Packing/freight
Systemclass
Platforms MaxLimitingfactors
Min Max Typical Min MaxEnvperformancelimits
Size(m)*Weight(kg)
Size(m)*Weight(kg)
Controlrange(km):
ScanEagle 24+hours n.p. n.p. 80 50-60 n.p. 5,944 n.p.?x3.11x1.55
22 n.p. 14-18 101.86
UX5 50minutesRain(onlytolerateslightrain),wind
n.p. n.p. 43 60,000 5,000 Wind<18m/s1.05x1.00x0.65
2.5 n.p. n.p. <5
163
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
11.3.3 Platformothertechnicaldetails
Table14.OthertechnicaldetailsoftheautonomousplatformslistedinTable12.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:l-t-a:lighter-than-airaircrafts,NA:notapplicable,n.p.:notprovided,SSS:sidescansonar,CTD:conductivity,temperature,depth,IR:infrared,SVTP:sound,velocity,temperatureandtemperature,SUNA:SubmersibleUltravioletNitrateAnalyzer,LND,COTS:CommercialOff-The-Shelf,PAR:PhotosyntheticallyActiveRadiation,RiNKO:phosphorescentDOsensor.
Systemclass
Platforms Hazardclass Noiselevel Interferingfactors Typeofinterface AdditionalsensorsCTDsensordeployment
ASV
-po
wered
ASV-6300 n.p. n.p. n.p. n.p. MBE,SSS,SBE,SAS,CT,video n.p.
C-Cat2Class9:MiscellaneousDangerousGoods
Noisefromelectricpropellers
Propulsiondrives(Jet)Ethernet,Serial,ROS,proprietary
Sensorpackagesuserdefinable
Surface,winchindevelopment
C-EnduroGenerator,propulsiondrives
Winchandsurface
C-Stat n.p. Verylow n.p. n.p. Fullycustomizable n.p.C-Target3
None Low
Engine
Ethernet,Serial,ROS,proprietary
Sensorpackagesuserdefinable
NAC-Worker4 Propulsiondrive(Jet)
Surface,winchindevelopment
C-Worker6Generator,propulsiondrives
C-Worker7Generator,propulsiondrives
C-WorkerHydro Propulsiondrive
Delfim n.p. n.p. n.p. n.p.Single-beamimagingsonar(SBS),sidescansonar(SSS)
n.p.
Mariner n.p. n.p. n.p. n.p.EO/IRcamera,radar,oceanographicinstruments,SBES,MBES,sonar
n.p.
MeasuringDolphin n.p. n.p. n.p. n.p. Ultrasonicdepthfinder,ADCP n.p.ROAZI n.p. n.p. n.p. n.p. SSS,SonarAltimeter,CTD,Camera
WinchROAZII n.p. n.p. n.p. n.p.
SSS,SonarAltimeter,CTD,Camera,IRCamera
RTSYSUSV None n.p. n.p. Ethernet/serial n.p. Winchdeployed
ASV
-un
powered
AutoNaut2
None Silent None EthernetorSerialVerywiderangeofsensors;customerintegrationpossible
Hull/strutmounted;towedAutoNaut3
AutoNaut5 Hull/strutmounted;towed;winchedAutoNaut7
WavegliderSV2 n.p. VeryQuiet n.p. Serial 50+ Mounted
164
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
Platforms Hazardclass Noiselevel Interferingfactors Typeofinterface AdditionalsensorsCTDsensordeployment
WavegliderSV3 n.p. n.p. Ethernet,SerialMounted,Winchcomingsoon
AUV-glider
ALBAC n.p. n.p. n.p. n.p. Temperature,velocity Mounted
Coastalglider n.p. n.p. n.p. n.p.
CTD/SVTP,acousticaltimeter,omnidirectionalsmarthydrophones,Wetlabswaterqualitysensors,RINKOdissolvedoxygen,SeabirdpumpedCTD,SUNAnitrate,LNDgammaray
n.p.
DeepgliderClass9:MiscellaneousDangerousGoods
n.p. n.p. n.p.SeabirdCTD,dissolvedoxygen,Wetlabsfluorometeropticalbackscatter
Mounted
eFòlagaIII Class8:Corrosives n.p. n.p. n.p. AnycustomorCOTSdevice Mounted
LiberdadeXwing/Zray
n.p. n.p. n.p. n.p.SPAWAR's32elementGliderTowedArraySystem(GTAS),on-boardpassiveacousticmonitoringsystem,DMON
n.p.
Petrel n.p. n.p. n.p. n.p. n.p. n.p.SeaBird n.p. n.p. n.p. n.p. n.p. n.p.
SeaExplorerClass9:MiscellaneousDangerousGoods
n.p. n.p. n.p.
CTD,dissolvedoxygen,turbidity,chlorophyll,CDOM,Phycobilins,HydrocarbonMinifluo-UVc,Methane,Metalstraces,Nitrate,acousticrecorder,altimeter…
Mounted
Seaglider(ogive)Class9:MiscellaneousDangerousGoods
n.p. n.p. n.p.
BiosphericalInstrumentsPAR,Aanderradissolvedoxygen,Seabirdelectronicsdissolvedoxygen,TurnerInstrumentsfluorometer/turbidimeter,Rocklandturbulencesensor,passiveacousticmonitoring,seabirdCTD,NortekacousticDopplercurrentprofiler,Wetlabsbackscattermeter/fluorometer,Controldissolvedoxygen,ImagenexES853
Mounted
SlocumG2hybrid
Class9:Miscellaneous
DangerousGoods(iflithiumbatteryused.Alsousesalkalinebatteries)
n.p. n.p. n.p. SeabirdpumpedCTD,Wetlabsfluorometer(chlorophylla),CDOM,turbidityandvolumescattering,Rinkoopticaloxygen,Vemcofishtrackerhydrophone,Satlanticmicro4channelirradianceandradiancesensors,BiosphericalInstrumentsPAR,SatlanticPar,Aanderaaoxygenoptode,ImagenexES853,TeledyneRDIADCP,SUNA
MountedSlocumG2glider n.p. n.p. n.p.
165
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
Platforms Hazardclass Noiselevel Interferingfactors Typeofinterface AdditionalsensorsCTDsensordeployment
nutrientanalyser,RocklandscientificMicroRider
SlocumG2thermal n.p. n.p. n.p.
SeabirdpumpedCTD,Wetlabsfluorometer,chlorophylla,turbidityandvolumescattering,Rinkoopticaloxygen,Vemcofishtrackerhydrophone,Satlanticmicro4channelirradiance,andradiancesensors,BiosphericalInstrumentsPAR,SatlanticPar,Aanderaaoxygenoptode,ImagenexES853,TeledyneRDIADCP,SUNAnutrientanalyser,RocklandscientificMicroRider
SprayClass9:MiscellaneousDangerousGoods
n.p. n.p. n.p.
CTD,SeabirdCTD,SeapointOpticalBackscattersensor,Seapointchlorophyllfluorometer,TritechPA200acousticaltimeter,SontekArgonautAcousticDopplerCurrentProfiler,zooplanktoncamera
Mounted
Sterneglider n.p. n.p. n.p. n.p. n.p. n.p.
TONAI n.p. n.p. n.p. n.p.
RINKO-Profilerincludedepth,watertemperature,conductivity,salinity,dissolvedoxygen,chlorophyll-a,turbidity.A-Tag,camera
n.p.
AUV-prop
eller
A18-D n.p. n.p. n.p. n.p.Standardpayload:SSS,MBE,video,FLS(ForwardLookingSonar),CTDandenvironmentalsensors.
n.p.
A9-M n.p.
Lowmagneticandacousticsignature(STANAG1364compliant)
n.p. n.p.Standardpayload:SSS,video,SVP(CTDandenvironmentalsensorsonrequest)
n.p.
Bluefin-12D n.p. n.p. n.p. n.p.Variousavailablepayloads.Customizable
n.p.Bluefin-12S n.p. n.p. n.p. n.p. n.p.
Bluefin-21 n.p. n.p. n.p. n.p.Standardpayload:SSS,SBPandMBE.Customizable
n.p.
Bluefin-9 n.p. n.p. n.p. n.p.Standardpayload:dual-freq.SSS,camera,CTprobe,turbidityprobe.Customizable.
n.p.
Bluefin-9M n.p. n.p. n.p. n.p.Standardpayload:dual-freq.SSS,cameraandbackscattersensor.Customizable
n.p.
166
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REPORTCODE:SMRUC-OGP-2015-015
Systemclass
Platforms Hazardclass Noiselevel Interferingfactors Typeofinterface AdditionalsensorsCTDsensordeployment
HUGIN1000(1000mversion)
n.p. n.p. n.p. n.p.
Broadrangeofsensors,customizableBodymounted
HUGIN1000(3000mversion)
n.p. n.p. n.p. n.p.
HUGIN3000 n.p. n.p. n.p. n.p.HUGIN4500 n.p. n.p. n.p. n.p.MUNIN n.p. n.p. n.p. n.p. NosemountedREMUS100 n.p. silent n.p. n.p.
Broadrangeofsensors,customizable
NosemountedREMUS3000 n.p. n.p. n.p. n.p.
REMUS600 n.p. n.p. n.p. n.p.Broadrangeofsensors,customizable(fullymodular)
REMUS6000 n.p. n.p. n.p. n.p. Broadrangeofsensors,customizable
RTSYSAUVClass9:MiscellaneousDangerousGoods
Lessthan100dBµPa
Speedandpayload Ethernet-WiFi Depth,Temperature n.p.
UASkite SwanX1Class3:FlammableLiquids
n.p. n.p. n.p. n.p. NA
UAS DesertStar10. Class2:Gases n.p. n.p. n.p. n.p. NAl-t-a OceanEye None n.p. n.p. n.p. n.p. NA
UAS-p
owered
-fixed
BRAMORC4EYE n.p.NoiselevelfortheBramorplatformis61.6dB(A),asmeasuredinSeptember2014.ThisiswellbelowtheEUnoisethresholds,whichis82dBinAustriaforexample.
Datacommunications
Radiofrequency,ifinthesamebandastheselecteddatatransmissionfrequency
Navigationlights,strobe,AN/PVS--7B/D,AN/PVS--14andAN/AVS--9compatibleIRbeacons,heatedpitot,multipleairvehiclecontrolfromsingleGCS,airpollution,radiation,hazardousandnon-hazardousgassensorADS--Btransponder
NA
BramorgEO n.p.
Include(butnotlimitedto):23.5MPRGBcamera,CIR,NDVI,4band,5bandor7bandMultispectralsensors,RikolaHyperspectralsensor,airpollution,radiation,hazardousandnon-hazardousgassensor(lasermassspectrometer),transponder,locatorbeacon.
NA
BramorrTK n.p.Include(butnotlimitedto):23.5MPRGBcamera,CIR,NDVI,4bandor5bandMultispectralsensors,locatorbeacon.
NA
Fulmar None n.p. n.p. n.p. n.p. NAJump20 None n.p. n.p. n.p. n.p. NAPenguinB None n.p. n.p. n.p. n.p. NA
167
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
Platforms Hazardclass Noiselevel Interferingfactors Typeofinterface AdditionalsensorsCTDsensordeployment
ScanEagle None n.p. n.p. n.p. n.p. NAUX5 None n.p. n.p. n.p. n.p. NA
11.3.4 Platformcosts
Table15.CostdetailsoftheautonomousplatformslistedinTable12.Givenistheplatformpriceforpurchaseorrental,thecostsforoperation,maintenance,battery/fuelcostsandcostsforpilotingtelemetry.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Pricesareforvehiclesonlywithoutsensors.Additionofsensorpackagescansignificantlyincreasetheprice.Abbreviations:l-t-a:lighter-than-airaircrafts,NA:notapplicable,n.p.:notprovided.
Price Costs
Systemclass
Platforms Purchase Rental Operation Maintenance Battery/Fuel Pilotingtelemetry
ASV
-po
wered
ASV-6300 n.p. n.p. n.p. n.p. n.p. n.p.C-Cat2 n.p. n.p. 2peopleforlaunchandrecovery,1
persononcontinuouswatch(usually4-6hourwatches)
Dependentonapplication
Rechargeablebatteries
DependentonapplicationC-Enduro n.p. n.p. 90Lforfulltanks
C-Target3 n.p. n.p. 40LC-Stat n.p. n.p. n.p. n.p. n.p. n.p.C-Worker4 n.p. n.p.
2peopleforlaunchandrecovery,1persononcontinuouswatch(usually4-6hourwatches)
Dependentonapplication
100L
DependentonapplicationC-Worker6 n.p. n.p. 1100LfulltanksC-Worker7 n.p. n.p. 1200LC-WorkerHydro
n.p. n.p. 780Ltank
Delfim n.p. n.p. n.p. n.p. n.p. n.p.Mariner n.p. n.p. n.p. n.p. n.p. n.p.MeasuringDolphin
n.p. n.p. n.p. n.p. n.p. n.p.
ROAZI n.p. n.p. n.p. n.p. n.p. n.p.
ROAZII n.p. n.p. n.p. n.p. n.p. n.p.
RTSYSUSV $20,000-$50,000 $2,000-$5,000 $1,000-$2,000 $1,000-$2,000 $1,000-$2,000 $1,000-$2,000
ASV
-un
powered
AutoNaut2 $50,000-$100,000 $20,000-$50,000
Dependsonoperationalarea/requirement
$2000 $1000 UHF/WifiAutoNaut3
$100,000+ $50,000-$100,000$10,000
$2000SatellitecommsaccountAutoNaut5 $3000
AutoNaut7 $15,000 $5000
168
Title:AutonomousTechnology
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REPORTCODE:SMRUC-OGP-2015-015
Price Costs
Systemclass
Platforms Purchase Rental Operation Maintenance Battery/Fuel Pilotingtelemetry
WavegliderSV2
n.p. n.p. n.p. n.p. NA n.p.
WavegliderSV3
n.p. n.p. n.p. n.p. NA n.p.
AUV-glider
ALBAC n.p. n.p. n.p. n.p. n.p. n.p.Coastalglider
n.p. n.p. n.p. n.p. n.p. n.p.
DeepgliderNotyetacommercialproduct
Notyetacommercialproduct
NotyetacommercialproductNotyetacommercialproduct
Notyetacommercialproduct
Notyetacommercialproduct
eFòlagaIII n.p. n.p. n.p. n.p. n.p. n.p.LiberdadeXwing/Zray
n.p. n.p. n.p. n.p. n.p. n.p.
Petrel n.p. n.p. n.p. n.p. n.p. n.p.SeaBird n.p. n.p. n.p. n.p. n.p. n.p.
SeaExplorer $100,000-$150,000 n.p. n.p. n.p.$0(rechargeablebatteries)
n.p.
Seaglider(ogive)
$100,000+ $50,000-$100,000 n.p.~$20,000(includingbatteries)
~$9,000/set ~$2,000-$3,000permonth
SlocumG2hybrid
n.p. n.p. n.p. n.p. n.p. n.p.
SlocumG2glider
n.p. n.p. n.p. n.p. n.p. n.p.
SlocumG2thermal
n.p. n.p. n.p. n.p. n.p. n.p.
Spray
SpraywithCTD:$50,000-$100,000,withZooCamera$100,000-$150,000
Donotrent Pilotingis~$10/day/glider ~$10,000/year/glider
Batteryandrefurbishment~$12,000per4-5monthmission
Pilotingtelemetry~$2/daydatatransmissioncostscanbesignificantbutweusuallybringhomelargedatasetsonrecordedmedia.
Sterneglider
n.p. n.p. n.p. n.p. n.p. n.p.
TONAI n.p. n.p. n.p. n.p. n.p. n.p.
AUV-
prop
eller A18-D n.p. n.p. n.p. n.p. n.p. n.p.A9-M n.p. n.p. n.p. n.p. n.p. n.p.Bluefin-12D n.p. n.p. n.p. n.p. n.p. n.p.Bluefin-12S n.p. n.p. n.p. n.p. n.p. n.p.
169
Title:AutonomousTechnology
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REPORTCODE:SMRUC-OGP-2015-015
Price Costs
Systemclass
Platforms Purchase Rental Operation Maintenance Battery/Fuel Pilotingtelemetry
Bluefin-21 n.p. n.p. n.p. n.p. n.p. n.p.Bluefin-9 n.p. n.p. n.p. n.p. n.p. n.p.Bluefin-9M n.p. n.p. n.p. n.p. n.p. n.p.HUGIN1000(1000mversion)
n.p. n.p. n.p. n.p. n.p. n.p.
HUGIN1000(3000mversion)
n.p. n.p. n.p. n.p. n.p. n.p.
HUGIN3000
n.p. n.p. n.p. n.p. n.p. n.p.
HUGIN4500
n.p. n.p. n.p. n.p. n.p. n.p.
MUNIN n.p. n.p. n.p. n.p. n.p. n.p.REMUS100 n.p. n.p. n.p. n.p. n.p. n.p.REMUS3000
n.p. n.p. n.p. n.p. n.p. n.p.
REMUS600 n.p. n.p. n.p. n.p. n.p. n.p.REMUS6000
n.p. n.p. n.p. n.p. n.p. n.p.
RTSYSAUV $100,000+ n.p. $2,000-$5,000 $2,000-$5,000 $1,000-$2,000 $1,000-$2,000
UAS
kite
SwanX1n.p. n.p. n.p. n.p. n.p. n.p.
UAS
l-t-a DesertStar
10.$50,000-$100,000 n.p. n.p. n.p. n.p. n.p.
OceanEye n.p. n.p. n.p. n.p. n.p. n.p.
UAS-p
owered
-fixed
BRAMORC4EYE
$100,000+startingprice(dependingonoptions)
TBDdependingonlocationandduration.Systemleaseispossibleviadifferentserviceproviders,notviathemanufacturer.
TBDdependingonlocationandduration
Completesparespartspackageis~$22,000for~1000flighthours
Includedinmantainancecosts.Onebatterypack(with2units)~$1,500
IncludedinOperationcostsBramorgEO
BramorrTK
Fulmar n.p. n.p. n.p. n.p. n.p. n.p.Jump20 n.p. n.p. n.p. n.p. n.p. n.p.PenguinB n.p. n.p. n.p. n.p. n.p. n.p.ScanEagle n.p. n.p. n.p. n.p. n.p. n.p.
170
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Price Costs
Systemclass
Platforms Purchase Rental Operation Maintenance Battery/Fuel Pilotingtelemetry
UX5 n.p. n.p. n.p. n.p. n.p. n.p.
171
Title:AutonomousTechnology
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REPORTCODE:SMRUC-OGP-2015-015
11.3.5 Platformsurveycapabilities
Table16.SurveycapabilitiesoftheautonomousplatformslistedinTable12.Givenisthecapabilityoftheplatformsfortracksetting,includingitsimplementationdetailsandlimitations,theirabilityforstationkeeping,autonomousdecisionmakingandclocksynchronisationincl.theirlimitations.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:l-t-a:lighter-than-airaircrafts,NA:notapplicable,n.p.:notprovided.
Systemclass
PlatformsTracksetting
Implementationdetails
Trackkeepinglimitations
Selfcorrection
ExplanationStationkeeping
Limitationstostationkeeping
Autono-mousdecisionmaking
Decisionlimitations
Clocksynchro-nisation
Synchlimitations
ASV
-po
wered
ASV-6300
Yes Setwaypoint
n.p. n.p. n.p. n.p. n.p.
Yes
n.p. n.p. n.p.C-Cat2
Trackinglimitedbymaxspeedandendurancerequirements
Yes
SystemusescontrolsystemandGPSpositiontoprovideprecisetrackcorrection
Location
Trackinglimitedbymaxspeedandendurancerequirements
Thisisavailablethroughtestedandimplemented3rdpartysoftware
YesRequiressatellitevisibility
C-Enduro
C-Target3
C-Stat
No(stationkeepingbuoy)
None n.p. No NA Yes Seastate n.p. n.p. n.p.
C-Worker4
Yes Setwaypoint
Trackinglimitedbymaxspeedandendurancerequirements
Yes
SystemusescontrolsystemandGPSpositiontoprovideprecisetrackcorrection
Location
Trackinglimitedbymaxspeedandendurancerequirements
Thisisavailablethroughtestedandimplemented3rdpartysoftware
YesRequiressatellitevisibility
C-Worker6C-Worker7
C-WorkerHydro
Delfim Yes Setwaypoint n.p. Yes
Usespathfollowingtechniques,tomakethevehiclefollowaspecifictrackatspecifiedspeed.
n.p. n.p. Yes n.p. n.p. n.p.
Mariner Yes Setwaypoint n.p. n.p. n.p. No n.p. Yes n.p. n.p. n.p.
172
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
PlatformsTracksetting
Implementationdetails
Trackkeepinglimitations
Selfcorrection
ExplanationStationkeeping
Limitationstostationkeeping
Autono-mousdecisionmaking
Decisionlimitations
Clocksynchro-nisation
Synchlimitations
MeasuringDolphin
Yes Setwaypoint n.p. n.p. n.p. n.p. n.p. Yes n.p. n.p. n.p.
ROAZIYes Setwaypoint
n.p. n.p. n.p. n.p. n.p.Yes
n.p. n.p. n.p.
ROAZII n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
RTSYSUSV Yes Setwaypoint Wave No n.p. Yes n.p. Yes n.p. Yes GPSsignal
ASV
-un
powered
AutoNaut2
Yes Setwaypoint
0.5ktcurrent
Yes Setwaypoint
Waypoints
<25mradiusfromwaypoint
Yes
Levelofautonomyprogrammedbeforemissioncommences
YesGPStimestamp;pre-missionsynchronisation
AutoNaut3 <2ktcurrent
AutoNaut5 <3ktcurrent Yes<35mradiusfromwaypoint
AutoNaut7 <4ktcurrentWaypoints
<45mradiusfromwaypoint
WavegliderSV2
Yes SetwaypointCurrents,Cloud,VesselTraffic
Yes Setwaypoint Yes<50mCEP
Yesn.p.
Yesn.p.
WavegliderSV3
<30mCEP NA n.p.
AUV-glider
ALBAC No n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.Coastalglider
Yes Setwaypoint Current n.p. n.p. Yes Turnradius n.p. n.p. n.p. n.p.
Deepglider Yes Setwaypoint Currents Yes Other YesTurnradius,current
Yes n.p. n.p. n.p.
eFòlagaIII n.p. n.p. n.p.
Nounderwaternavigation/trackcorrection
n.p. n.p. n.p. n.p. n.p. n.p. n.p.
LiberdadeXwing/Zray
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
Petrel n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.SeaBird n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.SeaExplorer n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
173
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
PlatformsTracksetting
Implementationdetails
Trackkeepinglimitations
Selfcorrection
ExplanationStationkeeping
Limitationstostationkeeping
Autono-mousdecisionmaking
Decisionlimitations
Clocksynchro-nisation
Synchlimitations
Seaglider(ogive)
Yes Setwaypoint Currents Yes Other YesTurnradius,current
Yes n.p. n.p. n.p.
SlocumG2hybrid
Yes Setwaypoint Currents Yes Other Yes
Turnradius,current
Yes
n.p. n.p. n.p.
SlocumG2glider
Turnradius(7m,Davisetal2002),current
n.p. n.p. n.p.
SlocumG2thermal
Turnradius,current
n.p. n.p. n.p.
Spray Yes Setwaypoint Currents Yes Other YesTurnradius,current
Yes n.p.Seefootnote36
n.p.
Sterneglider
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
TONAI n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
AUV-prop
eller
A18-DYes Setwaypoint
n.p. n.p. n.p. n.p. n.p.Yes
n.p. n.p. n.p.A9-M n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
Bluefin-12D
Yes Setwaypoint
n.p. n.p. n.p. n.p. n.p.
Yes
n.p. n.p. n.p.
Bluefin-12S n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.Bluefin-21 n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.Bluefin-9 n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.Bluefin-9M n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.HUGIN1000(1000mversion)
Yes Setwaypoint
n.p. n.p. n.p. n.p. n.p.
Yes
n.p. n.p. n.p.
HUGIN1000(3000mversion)
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
HUGIN3000
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
36 Spray communicates with Iridium and GPS. While it is not programmed to do it, it could certainly "calibrate" its clock to milliseconds or better every 3-5 hours when it surfaces and share the calibration with a central site. We don't know much about its clock's stability.
174
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
PlatformsTracksetting
Implementationdetails
Trackkeepinglimitations
Selfcorrection
ExplanationStationkeeping
Limitationstostationkeeping
Autono-mousdecisionmaking
Decisionlimitations
Clocksynchro-nisation
Synchlimitations
HUGIN4500
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
MUNIN n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.REMUS100
Yes Setwaypoint
n.p. n.p. n.p. Yes Canholdstationeveninstrongcurrents(nolimitationsspecified)
Yes
Fullyauto-nomousbasedonpredefinedmissionplan
n.p. n.p.REMUS3000
n.p. n.p. n.p. Yes Yes n.p. n.p.
REMUS600 n.p. n.p. n.p. Yes Yes n.p. n.p.REMUS6000
n.p. n.p. n.p. Yes Yes n.p. n.p.
RTSYSAUV Yes SetwaypointDriftofsubseanavigation
No n.p. No NA YesRemotecontrolandmonitoring
Yes GPSorPTP/NTP
UAS
kite
SwanX1 YesManualpiloting
Wind YesManualpiloting
n.p. n.p. No n.p. n.p. n.p.
UAS
l-t-a DesertStar
10.no Other n.p. n.p. Other
Notintegrated
n.p. No n.p. n.p. n.p.
OceanEye no Other Wind No Other n.p. n.p. No n.p. n.p. n.p.
UAS-p
owered
-fixed
BRAMORC4EYE
Yes
Programmedwaypoints.Orautomatictargetfollowingfromoperatorselectiononvideoscreen.
Wind,airtemperature,sensortemperature,humidity,visibility,cloud,fog,rain,snow
Yes
Whenafailsafeisactivated,thesystemwillrespondaccordinglyandthenreturntoitslastmissionpositiontocontinueitsplannedflightmissionafterfailsafealertisinactive
n.p.
Timeofloiteringabovestationislimitedonlytobatterylifetime
No
NA
Yes
RecordedtimeoftheIRandRGBsensorsvideocanbesynchronizedwiththeGPSdata
BramorgEO
n.p. NA
BramorrTK n.p. NA
Fulmar Yes Setwaypoint n.p. Yes Setwaypoint n.p. n.p. No n.p. Yes
Controlcapacityforupto3UAVs,controltransfer
175
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
Systemclass
PlatformsTracksetting
Implementationdetails
Trackkeepinglimitations
Selfcorrection
ExplanationStationkeeping
Limitationstostationkeeping
Autono-mousdecisionmaking
Decisionlimitations
Clocksynchro-nisation
Synchlimitations
betweenstations
Jump20 Yes Setwaypoint n.p. Yes Setwaypoint n.p. n.p. No n.p. n.p. n.p.PenguinB Yes Setwaypoint n.p. Yes Setwaypoint n.p. n.p. n.p. n.p. n.p. n.p.ScanEagle Yes Setwaypoint n.p. Yes Setwaypoint Location n.p. No NA n.p. n.p.UX5 Yes Setwaypoint Wind,rain Yes Setwaypoint No NA No NA n.p. n.p.
176
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
11.3.6 Platformoperationalinformation
Table17.OperationalinformationontheautonomousplatformslistedinTable12.Givenisthefueltype,failsafemodetype,transponder,detectandavoidcapacity,groundstationinterface,itslevelofautonomy,thepayloadcapacityintermsofpower,spaceandweightaswellasthedeploymentandrecoveryprocedure.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:l-t-a:lighter-than-airaircrafts,NA:notapplicable,n.p.:notprovided.
Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
ASV
-po
wered
ASV-6300 Diesel n.p. n.p. n.p. RFlink Waypoints n.p. n.p. n.p.ASV-6300SledL&Rsystem.Crane
ASV-6300SledL&Rsystem.Crane
C-Cat2 BatteryUsersettable
Failsafemodeisusersettable,dependingonrequirementsthesystemcanreturntowaypoint,orbitinplaceorstop
NoneIPradio,UHF
Waypoints
Upto250Wdependentonendurancerequirements
n.p. 50Slipway,Craneorhandlaunched
Slipway,Craneorhandrecovered
C-EnduroDieselElectrichybrid
n.p.
UsingAISsystemdetectandavoidavailable.radarandcameradetectandavoidindevelopment.
IPradio,Iridium,UHF
100Wcontinuous,250Wpeak
n.p. ~50Slipwayorcranelaunch
Slipwayorcranerecovery
C-Stat Diesel n.p. n.p.Optional(customizablepayload)
RF,satellite
Full(automatedstationkeeping)
n.p. n.p. 20 n.p. n.p.
C-Target3 PetrolUsersettable
Failsafemodeisusersettable,dependingonrequirementsthesystemcanreturntowaypoint,orbitinplaceorstop
NoneIPradio,UHF
Waypoints
Dependantonendurancerequirements
n.p.Dependentonapplication
Slipwayorcranelaunch
Slipwayorcranerecovery
C-Worker4 Diesel n.p. UsingAISsystemdetectandavoidavailable.radarandcameradetectandavoidindevelopment.
IPradio,Iridium,UHF
1000W
n.p. 80C-Worker6 Diesel
ElectricHybrid
n.p. n.p. 500
CraneLaunch CraneRecoveryC-Worker7 n.p. n.p. 500
C-WorkerHydro
Diesel n.p. 800W n.p. 120
Delfim None n.p. n.p. NoRFlink,80kmrange
Waypoints n.p. n.p. n.p.On-shoredeploymentorundocking
On-shorerecoveryordocking
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REPORTCODE:SMRUC-OGP-2015-015
Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
Mariner Diesel n.p. n.p. n.p. RF,Iridium Waypoints n.p.>1m3
n.p.On-shoredeploymentorundocking
On-shorerecoveryordocking
MeasuringDolphin
None(2x400Wbatteriesforpropulsion,1x300Wbatteryforelectronicsandsensors)
n.p. n.p.
Collisionavoidancethroughforwardlookingechosounder
RDS(UHF/VHFradiolink)
Waypoints n.p. n.p. 100On-shoredeploymentorundocking
On-shorerecoveryordocking
ROAZI
None(variablenumberof12V/3700Ahbatterypacks)
n.p. n.p.
Collisionavoidancethroughvideo
RF(WiFi802.11a/b/g)
Waypoints,targettracking
n.p. n.p. 50
On-shoredeploymentorundocking
On-shorerecoveryordocking
ROAZII
None(4xAMG12V/56Ahbatterypacks)
n.p. n.p.RF(WiFi802.11a/b/g)
n.p. n.p. n.p.
RTSYSUSV Location n.p.Transponder,noavoidance
RF-WifiContinuouscontrol
n.p. n.p.50x40x30
5 Manual Manual
ASV
-un
powered
AutoNaut2 None
Endpoint
Homewaypointcanbesetpre-missionifrequired.
Radartransponder;AISClassBorC;autonomouscollisionavoidancebasedonAIStargetinformation
UHF;Wifi
Waypoints
5MJ
n.p.
20 Launchfromslipway/beachbyoneperson;launchfromsupportvesselwithdavit/crane
reverseoflaunch
AutoNaut3
Methanol,ifrequired
Acousticmodem,Iridium;UHF;wifi
50MJ 40
LARS(LaunchandRecoverySystem);operationsvan
AutoNaut5
Acousticmodem,Iridium;Inmarsat;UHF;wifi
175MJ 130
Launchfromslipway/beachbytwopeople;launchfromsupportvessel
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Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
withdavit/crane
AutoNaut7Iridium;Inmarsat;UHF;wifi
300MJ 200
Launchfromslipway;launchfromsupportvesselwithdavit/crane
Reverseoflaunch
WavegliderSV2
NAKeeptrack
Willcontinueonlastprogrammedcourseorheadinguntilcommsarerestored
AISReceiver
Xbee,Acousticcommunications,iridium,WiFi,GatewayBuoy
Waypoints
Upto130W
n.p.
45+~2towed(neutrallybuoyant)
One-mandeployment
n.p.
WavegliderSV3
AISReceiver,fullyautonomousvehicleavoidance,optionalacousticreceiver
Acousticmodem,iridium,Wifi,Cell
Upto400W
60+~2towed(neutrallybuoyant)
LARS(LaunchandRecoverySystem);operationsvan
LARS(LaunchandRecoverySystem);operationsvan
AUV-glider
ALBAC Ni-Znbattery n.p. n.p. n.p. n.p. n.p. n.p. 3l n.p. n.p. n.p.
Coastalglider
Alkalineorlithiumbattery
n.p.
Emergencyrisemanoeuvre,emergencydive(heading)
altimeter,usehydrophoneforvesselavoidance
Iridium,ARGOS,FreewaveUHF,Wifi,acousticmodems
Waypoints
14MJ(alkaline),67MJ(lithium)or29.5MJ(rechargeable)
8L 5 Crane/davit Crane/davit
Deepglider
Lithiumsulfurylchloridebatteries
Location
Argos,bearing,location
Altitudesensorforbottomdetection.Icecopingalgorithm/behaviourcontrol
Iridium,acousticmodem
Waypoints17MJ(2*24V),4.4MJ(10VDC)
n.p. 25 Rib/crane Lasso
eFòlagaIIILeadacidorNiMhbatteries
n.p. n.p. n.p.
GPRS(cellular),wifi,GSM,24GHzradiolink
Waypointsandcontinuous
3.1MJ n.p. n.p. n.p. n.p.
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Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
LiberdadeXwing/Zray
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
Petrel n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. Crane/davit Crane/davitSeaBird Ni-MH n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
SeaExplorerRechargeablelithiumion
n.p.Autonomousdropweight,locationpinger,argos
n.p.Iridium,Radiofrequency,GPS
n.p. n.p. 9l 82people-rubberboat
2people-rubberboat
Seaglider(ogive)
Lithiumsulfurylchloridebatteries(optionalrechargeable)
Location
Argos,bearing,location
Altitudesensorforbottomdetection.Icecopingalgorithm/behaviourcontrol
Iridium,acousticmodem
Waypoints 18MJ n.p.
4(Davisetal2002),25(Wood2009)
Rib/crane Lasso
SlocumG2hybrid
Alkalinebatteries,lithiumbatteries
Location
Dropweight,lastgaspmode,ARGOS
Altitudesensorforbottomdetection.Icecopingalgorithm/behaviourcontrol
Iridium,Freewave900MHz(lineofsight),BenthosATM-900Modem
Waypoints
8MJ(Alkaline)
n.p.
5perpayloadbay(2payloadspossible)
Rib/craneNoseconerecoverySlocumG2
glider
Iridium,Freewave900MHz(lineofsight),BenthosATM-900Modem,ARGOS
8MJ(Alkaline),28MJ(needscheckingseeUNM2014_white)
n.p.
SlocumG2thermal
Iridium,Freewave900MHz(lineofsight),Benthos
6MJ n.p. 2
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Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
ATM-900Modem
Spray
Lithiumsulfurylchloridebatteries
Location
Dropweight,surfacing,RFbeacon,acousticpinger
Altitudesensorforbottomdetection(Shermanetal2001)
Orbcomm,Iridiumsatellite
Waypoints 13MJ n.p. 3.5to51.8 Rib/crane n.p.
Sterneglider
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
TONAI n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p. 2 n.p. n.p.
AUV-prop
eller
A18-D
None
n.p. n.p.ObstacleAvoidanceSystem
Iridium,RF,WiFiandacoustic.Ethernet(onshore)
Waypoints
51.9MJ n.p. n.p. LARSorRHIB LARSorRHIB
A9-M n.p. EmergencypingerObstacleAvoidanceSystem(optional)
Iridium(optional),RF,WiFiandacoustic.Ethernet(onshore)
7.6MJperenergysection
n.p. n.p. Manual
Manual.LocalRemoteControlforsurfacerecovery
Bluefin-12D
None
n.p. n.p.
Acoustictrackingtransponder
RF,Iridiumandacoustic(remote).Ethernet(onshore)
Waypoints
27MJ(5x5.4MJLiPobatterypacks)
n.p. n.p.
ByA-frameorcrane
ByA-frameorcrane
Bluefin-12S n.p. n.p.
16.2MJ(3x5.4MJLiPobatterypacks)
n.p. n.p.
Bluefin-21 n.p. n.p.
48.6MJ(9x5.4MJLiPobatterypacks)
n.p. n.p.
Bluefin-9 n.p. n.p.RFandacoustic(remote).
5.4MJ(1xLiPobatterypack)
n.p. n.p. Manual Manual
181
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Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
Ethernet(onshore)
Bluefin-9M n.p. n.p.
RF,Iridiumandacoustic(remote).Ethernet(onshore)
5.4MJ(1xLiPobatterypack)
n.p. n.p.Manualorbycrane
Manualorbycrane
HUGIN1000(1000mversion)
None
Endpoint
Hominganddocking Collisionavoidance
withForwardLookingSonar(FLS)
Acousticmodem,iridium,WiFi
Waypoints
54MJ n.p. n.p.
LARS(safeuptoseastate5)
LARS(safeuptoseastate5)
HUGIN1000(3000mversion)
54MJ n.p. n.p.
HUGIN3000
Typeusedinsemi-fuelcell(e.g.hydrogen)
162MJ n.p. n.p.
HUGIN4500
216MJ n.p. n.p.
MUNIN None n.p. 18MJ n.p. n.p.MinistingerorMUNINadapterforLARS
MinistingerorMUNINadapterforLARS
REMUS100
NoneEndpoint
Missionabort,hominganddocking
Abilitytodetect,locateandidentifyobjects.
Acousticcommunications,iridium,WiFi,GatewayBuoy Waypoints
3.6MJ n.p. n.p.One-mandeployment
n.p.
REMUS3000 Acoustic
modem,iridium,WiFi
n.p. n.p. n.p. LARS(LaunchandRecoverySystem);operationsvan
LARSREMUS600 18.7MJ n.p. n.p.REMUS6000
39.6MJ n.p. n.p.
RTSYSAUVLi-Ionbattery110
Endpoint
BuiltinFailuretree
n.p.WiFi-Iridium
Waypoints 3000Kj250mmlong
4Launchingramp
Handleorcradle
182
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REPORTCODE:SMRUC-OGP-2015-015
Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
220Vcharger
-Diameter140mm
UAS-kite
SwanX1 Gasoline None n.p. n.p. n.p.Continuouscontrol
n.p.
0.84x1.23x0.88
n.p. Runway n.p.
UAS-l-t-a DesertStar
10.NA None n.p. n.p. n.p.
Continuouscontrol
n.p. n.p. 30 Boattow Tetheredline
OceanEye NA None n.p. n.p. n.p.Continuouscontrol
n.p. n.p. n.p. Boattow Tetheredline
UAS-p
owered
-fixed
BRAMORC4EYE
Electric
n.p.
Multipleflightmodesandfailsafesavailable,user-configurable.LossofcommunicationsandGPSindicatorsandauralwarning.Lowbatteryindicationandauralwarning,criticalbatteryindicationandauralwarning.Terraindatasupportwithterrainnotificationandminimumterrainobstaclefailsafeoperation.Returntohomefeatures,
S-modetransponderisavailableasanoption
SurroundingRFcancauseinterference,ifthesameastheselectedtransmitter/receiverfrequency.Radiocommsareusuallybetween800-920MHzor902-928MHzradioformilitaryapplications(canbeadaptedaccording
Waypoints 7J
8cmhigh,83cmdiameter
0.3
Automaticcatapultlaunch(elasticorpneumaticcatapultoptions)
Parachuteautomaticlanding
BramorgEO
n.p.
89.8x172.8x77cmHLW
0.8
BramorrTK n.p.
89.8x172.8x77cmHLW
0.74
183
Title:AutonomousTechnology
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REPORTCODE:SMRUC-OGP-2015-015
Failsafemode Payloadcapacity Procedure
Systemclass
Platforms Fueltype Type MoreinfoTransponder,detectandavoidcapacity
Groundstationinterface
Levelofautono-my
PowerSpace
Weight(kg)
Deployment Recovery
emergencylandingfeatures.
tocountryTRA)
FulmarGasolineandheavyfuel
Location
n.p. n.p. RF Waypoints n.p. n.p. 8 Catapult Netlanding
Jump20 GasolineLocation
n.p. n.p. RF Waypoints n.p. n.p.30(inclfuel)
Verticaltakeoff Verticallanding
PenguinB Gasoline n.p. n.p. n.p. RF Waypoints n.p. n.p. 10Catapult,runwayorcarstoplaunch
n.p.
ScanEagle Gasoline n.p. n.p. n.p. RF Waypoints n.p. n.p. 3.4Automaticcatapultlaunch
AutomaticwithSkyHook®cable
UX5 Electric n.p. n.p. n.p. RF Waypoints n.p. n.p. n.p.Automaticcatapultlaunch
Bellylanding
184
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11.3.7 Platformmanningrequirements
Table18.ManningrequirementsfortheautonomousplatformslistedinTable12.Givenisxx.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:l-t-a:lighter-than-airaircrafts,NA:notapplicable,n.p.:notprovided.
Manningrequirements
Systemclass
Platforms PilotingMinnumberofpeopleforoperation
Vesselrequirements
Deployment Recovery Trainingneeds
ASV
-po
wered
ASV-6300Autonmous,supervisednavigationorremotecontrol
1 n.p. n.p. n.p. n.p.
C-Cat2
1continuouslyonshift
1Dependentonapplication
1-2people 1-2people
4daycourseC-Target3 1-22people 2people
C-Enduro 2-3
C-Stat Autonomous 1 n.p. n.p. n.p. n.p.
C-Worker4
1continuouslyonshift
2-3
Dependentonapplication
2people 2people 4Daycourse
C-Worker6
3 3people 3peopleDependentonapplication
C-Worker7
C-WorkerHydro
Delfim Autnomous(basedonmissionplan) 1 None 2people 2people n.p.
Mariner Autonomous(basedonmissionplan) 1 n.p. n.p. n.p. n.p.
MeasuringDolphin
Autonomous(missionplan).AutopilotorRemoteControl(docking)
1 n.p. 2people 2people n.p.
ROAZI Autonmous,supervisednavigationorremotecontrol
1n.p.
2people 2peoplen.p.
ROAZII n.p. n.p.
RTSYSUSV n.p. 2 0 Trail Trail No
ASV
-un
powered
AutoNaut2 Dependsonoperationalmission/location;eg24/7monitoringinbusycoastalwaters,regularmonitoringwithautowarningsinquietoffshorewaters
1
Trainedtechnicianformaintenancebetweenmissions
1 1
Pilot&maintainertraining,3daysashorewith1dayatsea.
AutoNaut32
Possiblewith1,butusually2
SameaslaunchAutoNaut5
2AutoNaut7WavegliderSV2 Supervisionandmissioncontrol
throughVehicleIntefaceProgram(VIP),24hourpassive(alertbased)
1per20WaveGliders16mminimum,1tonneliftingcapability
2people+captain 2people+captainL&RTraining,3-dayclassWavegliderSV3 1per60WaveGliders 3people+captain 3people+captain
AU V- gli
de r ALBAC n.p. n.p. n.p. n.p. n.p. n.p.
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Manningrequirements
Systemclass
Platforms PilotingMinnumberofpeopleforoperation
Vesselrequirements
Deployment Recovery Trainingneeds
Coastalglider n.p. 2Rib/vesseltogettowaterdepth
2 2 n.p.
Deepglider 1-2people,occasionaltweaking n.p.Rib/vesseltogettowater>50m
1-2people 1-2people
Deployment/recoveryminimaltraining,pilotingrequirestrainingandcomputerskills
eFòlagaIII 1-2people,occasionaltweaking n.p. n.p. 1-2people 1-2people
Deployment/recoveryminimaltraining,pilotingrequirestrainingandcomputerskills
LiberdadeXwing/Zray
n.p. n.p. n.p. n.p. n.p. n.p.
Petrel n.p. n.p. n.p. n.p. n.p. n.p.
SeaBird n.p. 1 n.p. n.p. n.p. n.p.
SeaExplorer n.p. n.p. n.p. n.p. n.p. n.p.
Seaglider(ogive) 1-2people,occasionaltweaking n.p.Rib/vesseltogettowater>50m
1-2people 1-2people
Deployment/recoveryminimaltraining,pilotingrequirestrainingandcomputerskills
SlocumG2hybrid
1-2people,occasionaltweaking
n.p.Shore/Rib/vesseltogettowater
1-2people 1-2people
Deployment/recoveryminimaltraining,pilotingrequirestrainingandcomputerskills
SlocumG2glider n.p.Rib/vesseltogettowater>20mSlocumG2
thermaln.p.
Spray 1-2people,occasionaltweaking n.p.Rib/vesseltogettowater>20m
1-2people 1-2people
Deployment/recoveryminimaltraining,pilotingrequirestrainingandcomputerskills
Sterneglider n.p. n.p. n.p. n.p. n.p. n.p.TONAI n.p. n.p. n.p. n.p. n.p. n.p.
186
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Manningrequirements
Systemclass
Platforms PilotingMinnumberofpeopleforoperation
Vesselrequirements
Deployment Recovery Trainingneeds
AUV-prop
eller
A18-D
Autonomous 1
n.p.WhateverLARSorRHIBmayrequire
WhateverLARSorRHIBmayrequire
n.p.
A9-M n.p.2people(2liftpointslocatedforeandaft)
2people(2liftpointslocatedforeandaft)
n.p.
Bluefin-12D
Autonomous.MonitoringthroughOperatorToolSuite
1
n.p. 1liftpointinthemiddleforcranerecovery
1liftpointinthemiddleforcranerecovery
n.p.Bluefin-12S n.p. n.p.Bluefin-21 n.p. n.p.
Bluefin-9
None
2people(2liftpointslocatedforeandaft)
2people(2liftpointslocatedforeandaft)
n.p.
Bluefin-9M
2people(2liftpointslocatedforeandaft).1extrareinforcedliftpointforcranerecovery
2people(2liftpointslocatedforeandaft).1extrareinforcedliftpointforcranerecovery
n.p.
HUGIN1000(1000mversion)
SupervisionandmissioncontrolthroughHUGINOperatorSystem(HOS)
1Trainedtechnicianformaintenancebetweenmissions
1 1Pilot&maintainertraining
HUGIN1000(3000mversion)HUGIN3000HUGIN4500MUNIN
REMUS100
SupervisionandmissioncontrolthroughVehicleIntefaceProgram(VIP)
1Trainedtechnicianformaintenancebetweenmissions
1 1Pilot&maintainertraining
REMUS3000
REMUS600REMUS6000
RTSYSAUV Autonomousduringmisssion 2Nospecificrequirements
1personRigid-hulledinflatableboat
Yes
UAS-
kite SwanX1
1 1 NA n.p. n.p. UASpilotcertification
UAS
l-t-a DesertStar10. n.p. n.p. NA n.p. n.p. n.p.
OceanEye 1 1 NA n.p. n.p. UASpilotcertification
187
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Manningrequirements
Systemclass
Platforms PilotingMinnumberofpeopleforoperation
Vesselrequirements
Deployment Recovery Trainingneeds
UAS-p
owered
-fixed
BRAMORC4EYE1
1pilotand1payloadoperator(ideally)
NA 1person 1person 5daysBramorgEOBramorrTKFulmar n.p. 2 NA n.p. n.p. UASpilotcertificationJump20 n.p. n.p. NA n.p. n.p. UASpilotcertificationPenguinB n.p. 2 NA n.p. n.p. UASpilotcertificationScanEagle n.p. 2 NA n.p. n.p. UASpilotcertificationUX5 n.p. 2 NA n.p. n.p. UASpilotcertification
188
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
11.3.8 Listofsensor
Table19.Sensorsthatmaybeintegratedintoautonomousvehiclesandincludedinthisreview,theirsystemclass(AAM,PAM,Video),systemname,manufactureraswellastheirtechnologyreadinesslevelasdefinedinTable10).Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring.
Systemclass
Systemname Manufacturer Technologyreadinesslevel
AAM
Aquadopp Nortek Fullcommercialapplication
ADP Sontek Fullcommercialapplication
AZFP ASLBasicresearch
Fullcommerciallyapplicationasmooredsystem(forAUVs)
DT-XSUB BioSonics Fullcommercialapplication
ES853 Imagenex Fullcommercialapplication
Gemini720iTritech Fullcommercialapplication
Gemini720is
modularVR2CVemco Fullcommercialapplication
VMT
WBATKongsberg/Simrad
Fullcommercialapplication
WBTmini Prototypesystem
PAM
A-Tag MarineMicroTechnology Fullcommercialapplication
AUSOMS-miniBlack AquasoundInc. Fullcommercialapplication
C-POD-FChelonia
Smallscaleprototype
C-POD Demonstrationsystem
Cornell/AutoBuoys Cornell(PAM)/EOS(buoy) Ready
Decimus SAInstrumentationLtd Fullcommercialapplication
DMON WHOI AvailablefromWHOIn.p.
189
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REPORTCODE:SMRUC-OGP-2015-015
Systemclass
Systemname Manufacturer Technologyreadinesslevel
SDA14RTSYS
Demonstrationsystem
SDA416 Prototypesystem
Seicherealtimetransmissionsystem SeicheMeasurementsLtd n.p.
SM2/SM3 WildlifeAcoustics Fullcommercialapplication
SoundTrap4channelOceanInstruments
Prototypesystem
SoundTrapHF Fullcommercialapplication
WISPR EmbeddedOceanSystem n.p.
VIDEO
CM100UAVvision Fullcommercialapplication
CM202
DualImagerInsitu Fullcommercialapplication
EO900
OTUSU135HIGHDEFDST Fullcommercialapplication
OTUS-L205HIGHDEF
TASE310Cloudcaptechnology Fullcommercialapplication
TASE400HD
190
Title:AutonomousTechnology
DATE:July2016
REPORTCODE:SMRUC-OGP-2015-015
11.3.9 Sensortechnicaldetails
Table20.TechnicaldetailsofthesensorslistedinTable19.Givenaretheenvironmentalperformancelimitsofthesensors,theiroperationalaswellaspacking/freightsizeandweight,hazardclassasdefinedinTable11,anyfactorsinterferingwiththeoperationofthesensor,andthesensor’stypeofinterface.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring,NA:notapplicable,n.p.:notprovided.
Operational Packing/freight
Systemclass
Systemname
Envperformancelimits Size(cm)* Weight(kg)* Size(cm)*Weight(kg)*
Hazardclass
Interferingfactors
Typeofinterface
AAM
Aquadopp n.p.47-71(height),7.5(diameter)
2,200 n.p. n.p. n.p. n.p. RS232,RS422
ADP n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
AZFP n.p.100(length),17(diameter)+transducersize
n.p. n.p. 50 n.p. n.p. RS422
DT-XSUB
Astableplatformisnecessaryfordatacollection.ThetowedbodyisisolatedfromthemechanicalsurgeoftheWaveGliderengineviaaspecifically-engineered,complianttowcable.However,extremewind/waveconditionsmayresultindestabilizationofthetowedbody.Thesystemispoweredviasolargeneratedelectricity,thusprolongeddarknesswillresultintemporaryshutdownuntilbatteriescanberecharged.
56(length),25(diameter)+transducerseither19or26(diameter)
Inwaterweight20lbs.(10,000g)
80x30x30 45 NoneOtheracousticinstruments
USBandethernetports
ES853 n.p.94(height),83(diameter)
1000 n.p. n.p. NoneOtheracousticinstruments
R232
Gemini720i
Standardis300m.availableina4,000mdepthratedmodelfordeep-wateroperations.Temperatureranges-10to35°C(operating)
n.p.1.2(300m),7.5(4000m)inwater
48x28x46(300m),53x50x50(4000m)
14(300m),20(4000m)
None NoneEthernet,VDSL,RS232,IsolatedTTL
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Operational Packing/freight
Systemclass
Systemname
Envperformancelimits Size(cm)* Weight(kg)* Size(cm)*Weight(kg)*
Hazardclass
Interferingfactors
Typeofinterface
Gemini720is
Standardis1,000m(aluminium).availableina4,000m(titanium)depthratedmodelfordeep-wateroperations.Temperatureranges-10to35°C(operating)
11x14x281.5(1000m),3.0(4000m)inwater
48x28x46(1000m),48x28x46(4000m)
14(1000m),17(4000m)
modularVR2C
500mdepthlimitpcb21(length),2(width)
99gair n.p. n.p. n.p. n.p. Serial
VMT 1000mdepthlimit18by3.5diameter
280gair n.p. n.p. n.p. n.p.
Optical-hasadedicatedreaderorcanbeBluetooth
WBAT n.p. n.p. n.p. n.p. n.p.
Class9:MiscellaneousDangerousGoods
Otheracousticinstruments
Self-contained/USBdownloadinterface
WBTmini n.p.10x10x15+wrapping
n.p. n.p. n.p. None n.p. n.p.
PAM
A-Tag 0-40Celcius,200m NA NA NA NA NA NA
A-tagisastand-alonesystemwithoutanyinterfacetotheoutside.
AUSOMS-miniBlack
0-40Celcius,1000m NA NA NA NA NA NA
AUSOMS-miniisastand-alonesystemwithoutanyinterfacetotheoutside.
C-POD-FAllmarinewatertemps;0-100m.Or0-2000m(DeepC-POD)
NA NA NA NA NA NA Noneyet
C-POD NA NA NA NA NA NANone,exceptviathirdparty
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Operational Packing/freight
Systemclass
Systemname
Envperformancelimits Size(cm)* Weight(kg)* Size(cm)*Weight(kg)*
Hazardclass
Interferingfactors
Typeofinterface
Cornell/AutoBuoys
NA NA NA NA NA NA NA Ethernet,serial
Decimus -10-60degc NA NA NA NA NA NASerial,Ethernet,WiFi
DMON n.p. NA NA NA NA NA NA n.p.
SDA140°C-80°C
NA NA NA NA NA NA Serial,ethernet,wifi,uhfSDA416 NA NA NA NA NA NA
Seicherealtimetranmissionsystem
n.p. NA NA NA NA NA NA n.p.
SM2/SM3Maximumdepthis150mfortheshallowversion,800mforthedeep-waterversion
NA NA NA NA NA NA None
SoundTrap4channel
Temp:-10to>50degC,depth:<1,000m
NA NA NA NA NA NA
RS232,RS485SoundTrapHF
NA NA NA NA NA NA
WISPR NA NA NA NA NA NA NA Ethernet,serial
VIDEO
CM100 n.p. 190x100 800g n.p. n.p. None n.p. n.p.
CM202 n.p. 295x190 3.5 n.p. n.p. None n.p. n.p.
DualImager
n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
EO900 n.p. n.p. n.p. n.p. n.p. n.p. n.p. n.p.
OTUSU135HIGHDEF
n.p. 185x135 1.5 n.p. n.p. n.p. n.p. n.p.
OTUS-L205HIGHDEF
n.p. 256x205 2.6 n.p. n.p. n.p. n.p. n.p.
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Operational Packing/freight
Systemclass
Systemname
Envperformancelimits Size(cm)* Weight(kg)* Size(cm)*Weight(kg)*
Hazardclass
Interferingfactors
Typeofinterface
TASE310OperatingTemperatureRange:-20°Cto+60°C
Size:178x178x267mmTurretDiameter:178mm
3 n.p. n.p.None
n.p.ControlInterface:RS-232,CAN,Ethernet(withadaptor)
TASE400HD
3.5 n.p. n.p. n.p.
11.3.10 Sensorcosts
Table21.CostssensorslistedinTable19.Givenarethepurchaseandrentalprice,aswellasoperational,maintenance,battery/fuelaswellasintegrationcosts.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring,NA:notapplicable,n.p.:notprovided.
Price Costs
Systemclass
Systemname Purchase Rental Operation Maintenance Battery/Fuel Integration
AAM
Aquadopp n.p. n.p. n.p. n.p. n.p. n.p.ADP n.p. n.p. n.p. n.p. n.p. n.p.
AZFPCAD$35,000-$80,000
3-monthrentalcostisinthe$10,000-$20,000range
Batterycostsareinthe$1,000-$2,000range
n.p. n.p. n.p.
DT-XSUB$50,000-$100,000
$5,000-$10,000 $1,000-$2,000 $1,000-$2,000 $1,000-$2,000 Bothpossible
ES853 $5,000-$10,000 NA IntegrationCost Nomovingparts Requires24vdc IntegrationCostGemini720i
$30,000-$50,000
$1,000-$2,000perweekNone;setupandrun/logdataonPC
None35Wpowerrequirement Software
DevelopmentKitavailableGemini720is 25Wpowerrequirement
modularVR2C$2,000-$5,000
NA n.p. n.p.Minimal-poweredoffgliderlessthan1milliamp
n.p.
VMT NA n.p. n.p. $350forannualrebatatfactory n.p.
WBAT $30,000-$50,000
n.p. n.p. n.p.BatteryiseitherlithiumorNiMh.Costis18,000NOKforlithium
n.p.
WBTmini n.p. n.p. n.p. n.p. n.p.
PAM A-Tag $5,000-$10,000 n.p. NA NA NA NA
AUSOMS-miniBlack
$2,000-$5,000 n.p. NA NA NA NA
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Price Costs
Systemclass
Systemname Purchase Rental Operation Maintenance Battery/Fuel Integration
C-POD$2,000-$5,000 $1,000-$2,000
NA NA NA NAC-POD-F NA NA NA NACornell/AutoBuoys
TBD TBD NA NA NA NA
Decimus n.p. n.p. NA NA NA NADMON n.p. n.p. NA NA NA NASDA14 $2,000-$5,000 n.p. NA NA NA NASDA416 $5,000-$10,000 n.p. NA NA NA NASeicherealtimetranmissionsystem
n.p. n.p. NA NA NA NA
SM2/SM3 $5,000-$10,000 n.p. NA NA NA NASoundTrap4channel $2,000-$5,000
n.p. NA NA NA NA
SoundTrapHF n.p. NA NA NA NAWISPR n.p. n.p. NA NA NA NA
VIDEO
CM100 n.p. n.p. n.p. n.p. n.p. IntegratedCM202 n.p. n.p. n.p. n.p. n.p. IntegratedDualImager n.p. n.p. n.p. n.p. n.p. IntegratedEO900 n.p. n.p. n.p. n.p. n.p. IntegratedOTUSU135HIGHDEF
n.p. n.p. n.p. n.p. n.p. Integrated
OTUS-L205HIGHDEF
n.p. n.p. n.p. n.p. n.p. Integrated
TASE310 n.p. n.p. n.p. n.p. n.p. IntegratedTASE400HD n.p. n.p. n.p. n.p. n.p. Bothpossible
195
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11.3.11 SensorsurveycapabilitiesI
Table22.FirstsetofsurveycapabilitycriteriaforsensorslistedinTable19.Givenisthedatatypecollected,dataprocessingdetails,ifspeciesidentificationispossibleandifso,whichtype,andifthebearing,directand/orhorizontalrangetotheanimalisestimablewiththesensordata.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring,NA:notapplicable,n.p.:notprovided.
EstimabletoanimalSystemclass
Systemname
Collecteddatatype
ProcessesSpeciesidentification
Speciestype BearingDirectrange
Horizontalrange
AAM
Aquadopp Raw n.p. Notproven Fish/zooplankton Yes Yes NoADP Raw n.p. Notproven Fish/zooplankton Yes Yes No
AZFP Raw n.p.Ifmultifrequency,possible
Fish/zooplankton Yes Yes No
DT-XSUB Raw
On-boardprocessinggeneratesdatasummarieswhichcanbetransmittedandviewedassimplifiedechograms.Itisalsopossibletogeneratealertstriggeredbyacousticevents(detectionoftarget(s)havingTSexceedingaspecificthreshold)
Ifmultifrequency,possible
Fish/zooplankton Yes Yes No
ES853 Raw n.p. Notproven Fish/zooplankton Yes Yes NoGemini720i
RawGeminiformatimagedatalogged
SonardataiscollectedinaproprietaryTritechformat,whichcanbereplayedatthesamequalityitwasrecorded.ThirdpartysoftwarewouldneedaTritechECDfileconvertertoaccessthedatainthefiles.TheGeminiSeaTecSystemutilisestheindustrystandardGeminihardware,combinedwithGeminiSeaTecsoftware(anadaptionofTritech’sstandaloneGeminisoftware).SeaTecsoftwareprovidesintuitiveobjectdetectionandtargettrackingcapabilities.Targetsareclassifiedbasedontheprobabilityofthembeingaspecific,predeterminedtype.Forexample,marinemammals,pipesordebris.
YesMarinemammalautomaticdetection,rudimentaryfishdetection
Yes Yes YesGemini720is
modularVR2C Tagdetections AcousticpingsaredecodedintotagIDs Yes
Anyaquaticspecies-largeenoughtoholdanacoustictag
No No NoVMTWBAT
Rawn.p. Ifwideband,
probableFish/zooplankton Yes Yes No
WBTmini n.p.
PAM
A-TagSummaryclickinformation
Akamatsuetal(2005),MarineTechnologySocietyJournal39(2),3-9.
Smallodontocetes,snappingshrimps
IdentificationofDelphinidadandPhocoenidaefamily
Yes No No
196
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EstimabletoanimalSystemclass
Systemname
Collecteddatatype
ProcessesSpeciesidentification
Speciestype BearingDirectrange
Horizontalrange
AUSOMS-miniBlack
RawrecordingofLinearPCM,MP3andWMAformats
CommonlyusedcompressionformatsCetaceans,fish,shrimps
Dependsonoff-lineanalysis
No No No
C-POD
Descriptorsofultrasonictonesandclicksasdetectiondataandasinputtotraindetectionprocess.
Selectionofultrasonicclicksandtones;estimationofkeydescriptorsoftheseforstorage.
Allodontoceteclicks>20kHz
Somespeciesgroups Yes Yes No
C-POD-F
Descriptorsofultrasonictonesandclicksasdetectiondataandasinputtotraindetectionprocess.Waveformsofselectedclicksintrainstoaidspeciesidentification.
Selectionofultrasonicclicksandtones;estimationofkeyparametersoftheseforstorage.Traindetectiontoidentify'specimen'cetaceanclicksforstorageoffullwaveform.
Allodontoceteclicks>17kHz
Cornell/AutoBuoys
Compressedrawdata(FLAC),detections
n.p.
NRW,BHW,anyspeciesforwhichatrustedalgorithmexists
NRW,BHW,anyspeciesforwhichatrustedalgorithmexists
Yes,withanarrayofunits
Yes,withanarrayofunits
Yes,withanarrayofunits
DecimusRawrecordings&binarydata
Variousdetectors/noisemonitorsavailable
Harbourporpoise,Bottlenosedolphins,Spermwhales,Baleenwhales,Belugawhales,Rightwhales
Harbourporpoise,Bottlenosedolphins,Spermwhales,Baleenwhales,Belugawhales,Rightwhales
Yes No No
DMON n.p. n.p. n.p. n.p. n.p. n.p. n.p.SDA14 Raw(24bits.Wav),
.txtlogfiles;pre-computedinformation,eventdetection
Thirdoctavebands,clicklikeeventdetection,advancedspecificprocessingonrequest
Porpoise,dolphins(requirespostvisualidentification),whales
Porpoise,whales Yes Yes YesSDA416
Seicherealtimetranmissionsystem
n.p. n.p. n.p. n.p. n.p. n.p. n.p.
197
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EstimabletoanimalSystemclass
Systemname
Collecteddatatype
ProcessesSpeciesidentification
Speciestype BearingDirectrange
Horizontalrange
SM2/SM3
Rawrecordingsarestoredeitherinuncompressed.wavfileformatorwithWAC0losslesscompressionwhichsavesapproximately50%oncardspace.
n.p.
Datahasbeenusedtorecordallmarinemammaltypesincludingwhales,dolphins,porpoises,manatees,etc
n.p. No No No
SoundTrap4channel Rawand/or
detectionsclickdetector-energyinband Toothedwhales
n.p. Yes Yes Yes
SoundTrapHF
n.p. No No No
WISPRCompressedrawdata(FLAC),detections
n.p.Anyspeciesforwhichatrustedalgorithmexists
Anyspeciesforwhichatrustedalgorithmexists
Yes,withanarrayofunits
Yes,withanarrayofunits
Yes,withanarrayofunits
VIDEO
CM100 Raw n.p. n.p. n.p.Yes Yes Yes
CM202 Raw n.p. n.p. n.p.DualImager
Raw n.p. n.p. n.p.Yes Yes Yes
EO900 Raw n.p. n.p. n.p.OTUSU135HIGHDEF
Raw n.p. n.p. n.p.Yes Yes Yes
OTUS-L205HIGHDEF
Raw n.p. n.p. n.p.
TASE310 Raw n.p. n.p. n.p.Yes Yes YesTASE
400HDRaw n.p. n.p. n.p.
198
Title:AutonomousTechnology
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11.3.12 SensorsurveycapabilitiesII
Table23.SecondsetofsurveycapabilitycriteriaforsensorslistedinTable19.Givenisthereal-timedatatransmissioncapabilitiesincl.detailswatistransmitted,theabilityforclocksynchronisationanditslimitationsandforacousticsensors,ifreceivinglevelsofreceivedsignalsisestimableaswellasambientnoiselevels,andifCTDdataareobtainedsimultaneously.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring,NA:notapplicable,n.p.:notprovided.
Realtimedatatransmission Clocksynchronisation Acousticsensorspecific
Systemclass
Systemname Possible Whatistransmitted Possible Limitations
EstimationCTDdataReceiving
level
Ambientnoiselevel
AAM
Aquadopp PartlyApingcanbetransmitted,butmultiplepingscollectedonasingledive
n.p. n.p. n.p. n.p. no
ADP PartlyApingcanbetransmitted,butmultiplepingscollectedonasingledive
n.p. n.p. n.p. n.p. no
AZFP PartlyApingcanbetransmitted,butmultiplepingscollectedonasingledive
n.p. n.p. n.p. n.p. no
DT-XSUB PartlyApingcanbetransmitted,butmultiplepingscollectedonasingledive
Yes None n.p. n.p. no
ES853 PartlyApingcanbetransmitted,butmultiplepingscollectedonasingledive
Yes n.p. n.p. no no
Gemini720i
Yes
CurrentlyalarmscanbesentoverCOM(serial)orparallelports.TritechcanadaptthesoftwareasrequiredandiscurrentlydoingthisforacustomerthatrequireswarningstobesentasHTTPPOSTcommandsovertheinternet,whichresultinamobilephoneTEXTmessagewhenatargetofaspecifictypeisifentifiedwithhighprobability.
Thesoftwareisdevelopedinhouseandcanbeadaptedasrequired.Basiclevelsofsynchronisationareachievedorwithpurchaseofextrahardwarehighperformanceclocksynchronisationisavailable.
ThesoftwareexpectsaninputPPSandZDAtimestring.
No No
Additionaldatacouldbeloggedifrequiredwithsoftwareadaptation
Gemini720is
modularVR2C Yes DetectedtagIDs Yes
Ifdetectiondataistransmittedinrealtimetoon-boardcomputer,thetimestamppfthedetectioncanbecorrectedbythecurrenttimeoftheon-boardcomputer
n.p.No No
VMT No n.p. No n.p. n.p.
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Realtimedatatransmission Clocksynchronisation Acousticsensorspecific
Systemclass
Systemname Possible Whatistransmitted Possible Limitations
EstimationCTDdata
Receivinglevel
Ambientnoiselevel
WBATPartly
Apingcanbetransmitted,butmultiplepingscollectedonasingledive
n.p. n.p. n.p. n.p. n.p.WBTmini n.p. n.p. n.p. n.p. n.p.
PAM
A-Tag No NA No NA yes No n.p.AUSOMS-miniBlack
No NA No NA Yes Yes n.p.
C-POD No NA No NAYes Yes
n.p.C-POD-F yes Samedataasstoredi.e.compressed Yes Wiredlinkbetweenunits n.p.
Cornell/AutoBuoys
Yes,withiridium/cellantenna
Detections/audioclips Yes GPSYes,withcalibratedsensor
Yes,withcalibratedsensor
n.p.
Decimus yes Binarydetectiondata/noise Yes indevelopment Yes Yes n.p.DMON n.p. n.p. n.p. n.p. n.p. n.p. n.p.SDA14
YesWavfiles,precomputednoisedata Yes GPS/PPSonly Yes Yes n.p.
SDA416 n.p. Yes GPS/PPS,NTP,PTP Yes Yes n.p.Seicherealtimetranmissionsystem
n.p. n.p. n.p. n.p. n.p. n.p. n.p.
SM2/SM3 No NA Yes
UsetheSM3GPStosynchtheclockoftheSM3Mandthendetachfordeployment
YesDatahasbeenused
n.p.
SoundTrap4channel
n.p.Endusertoimplement
Yes GPSinput Yes Yes n.p.
SoundTrapHF n.p. Yes GPSinput Yes Yes n.p.
WISPRYes,withiridium/cellantenna
Detections/audioclips Yes GPSYes,withcalibratedsensor
Yes,withcalibratedsensor
n.p.
VIDEO
CM100Yes
n.p. n.p. n.p. NA NA NACM202 n.p. n.p. n.p. NA NA NADualImager
Yesn.p. n.p. n.p. NA NA NA
EO900 n.p. n.p. n.p. NA NA NAOTUSU135HIGHDEF
Yes n.p. n.p. n.p. NA NA NA
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Realtimedatatransmission Clocksynchronisation Acousticsensorspecific
Systemclass
Systemname Possible Whatistransmitted Possible Limitations
EstimationCTDdata
Receivinglevel
Ambientnoiselevel
OTUS-L205HIGHDEF
n.p. n.p. n.p. NA NA NA
TASE310Yes
n.p. n.p. n.p. NA NA NATASE400HD n.p. n.p. n.p. NA NA NA
11.3.13 Sensoroperationalrequirements
Table24.OperationalrequirementsofthesensorslistedinTable19.Givenistheirlevelofautonomy,deploymentandrecoveryprocedure,theirpayloadpower,andinternalaswellasexternalpayloadcapacityintermsofspaceandweight.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring,NA:notapplicable,n.p.:notprovided.
Procedure Internalpayload
capacityExternalpayloadcapacity
Systemclass
Systemname
Levelofautonomy Deployment Recovery PayloadpowerSpace(cm)*
Weight(kg)*
Space(cm)* Weight(kg)*
AAM
Aquadopp Self-logging,programmedintervalsMountedonglider
Mountedonglider
0.5-1.5Wat1Hz0.47-0.71length0.08mdiameter
2.2-3.4inair
n.p. 2.2-3.4inair
ADP n.p.Mountedonglider
Mountedonglider
200-1000mW n.p. n.p. n.p. n.p.
AZFP n.p.Mountedonglider/AUV
MountedongliderAUV
n.p. n.p. n.p.0.17mdiameter,1mlength
n.p.
DT-XSUB NAMountedonglider
Mountedonglider
11-24volts NA NA NA NA
ES853Self-logging,fixedpingrate,polledbyAUV/glider
Mountedonglider
Mountedonglider
<250mW n.p. 1 n.p. 1
Gemini720i Thesonarisprovidedasaplugandplaydevice.Targetidentificationandclassificationwithvariablelevelsofprobabilityiseasilyenabledthroughuserselection.
Deploymentonasubseaframeoronapole
Retrievalusingaliftingwire;noexcessivestrainoncable
35Wpowerrequirement n.p. n.p. n.p. n.p.
Gemini720is
25Wpowerrequirement n.p. n.p. n.p. n.p.
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Procedure Internalpayload
capacityExternalpayloadcapacity
Systemclass
Systemname
Levelofautonomy Deployment Recovery PayloadpowerSpace(cm)*
Weight(kg)*
Space(cm)* Weight(kg)*
modularVR2C
n.p.Embeddedinglider
Realtimetransmissionofdatathruglidercomms
24to180milliwattsPcb21(length),2(width)
99gair n.p. n.p.
VMT n.p.Mountedonglider
Mountedonglider
on-boardCcellbattery-factoryreplaceable
n.p. n.p. n.p. n.p.
WBAT Self-logging,fixedpingrateMountedonAUV
MountedonAUV n.p. n.p. n.p. n.p. n.p.
WBTmini n.p.Mountedonglider
Mountedonglider
n.p. n.p. n.p. n.p. n.p.
PAM
A-Tag Fullyautonomousaftersettingup NA NA 60mW 5x5x54 0.6inair 5x5x54 0.6inair
AUSOMS-miniBlack
Fullyautonomousaftersettingup NA NA 75mW
5.1x5.1x19.3(Diameter5.1,Length19.3,CarbonFiberCase)
0.6inair(0.08inwater)
5.1x5.1x19.3(Diameter5.1,Length19.3CarbonFiberCase)
0.6inair(0.08inwater)
C-POD400yearsofdataautomaticallyprocessin5days,SAMBAHproject
NA NA 44mWcylinder540long/80OD
n.p.90diameter,606length
n.p.
C-POD-F High NA NA 60mW,lessifquiet n.p.91diameter,640length
n.p.
Cornell/AutoBuoys
n.p. NA NA <2Watts NA NACylindricalDelrinhousing(~40x20)
5
Decimus High NA NA2/3fordevice,commsdependant
20x20x10approx.
120x20x10approx.
n.p.
DMON n.p. NA NA n.p. n.p. n.p. n.p. n.p.
SDA14
Autonomous
NA NA600Mw-1.8W(multichannels)
14x6.5 200gDependsifhousingrequired,fromrecordertobuoyorothercarrier
VariableSDA416 NA NA n.p. 14x6.6 300g
Seicherealtimetransmissionsystem
n.p. NA NA n.p. n.p. n.p. n.p. n.p.
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Procedure Internalpayload
capacityExternalpayloadcapacity
Systemclass
Systemname
Levelofautonomy Deployment Recovery PayloadpowerSpace(cm)*
Weight(kg)*
Space(cm)* Weight(kg)*
SM2/SM3Canbeeasilyprogrammedforautonomousautomatedrecordings.
NA NA
Powerusagevariesbysamplerate.Whenidle,theSM3Mconsumesaround0.5mW.Dependingonaccessories,samplerates,compression,andothervariables,theSM3Mcanuseaslittleas500mWofpowerwhenrecording.
Dimesionsofelectronics:22”longx5.25”widex4.25”high
2.95lbs
Length/Heightofhousing:90.9+/-0.8includeseyeboltandhydrophonecage.Diameter:16.8.EyeboltAnchor:2.5innerdiameter,4.3outerdiameter,5.1heightoffhousing.StandardHydrophone:6.4length,1.9diameter.
Weight(Dry)withelectronics:9.5withoutbatteries.13.5with32batteries.Buoyancy(saltwater)withelectronics:5.5,withoutbatteries.1.5with32batteries
SoundTrap4channel
n.p. NA NA 70mW9.5x4x3excludinghydrophones
0.12
21x8dia
1SoundTrapHF
n.p. NA NA 35mW9.5x4x2.5excludinghydrohone
21x6dia
WISPR n.p. NA NA Powerusage~800mW8x6.5(digitalunitonly)
<50g(digitalunitonly)
NA NA
VIDEO
CM100 NA NA NA 12W n.p. n.p. n.p. n.p.CM202 NA NA NA na n.p. n.p. n.p. n.p.DualImager NA NA NA n.p. n.p. n.p. n.p. n.p.EO900 NA NA NA n.p. n.p. n.p. n.p. n.p.OTUSU135HIGHDEF
NA NA NA 15W n.p. n.p. n.p. n.p.
OTUS-L205HIGHDEF
NA NA NA n.p. n.p. n.p. n.p. n.p.
TASE310 NA NA NAPower:20W(average)125W(max)
n.p. n.p. n.p. n.p.
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Procedure Internalpayload
capacityExternalpayloadcapacity
Systemclass
Systemname
Levelofautonomy Deployment Recovery PayloadpowerSpace(cm)*
Weight(kg)*
Space(cm)* Weight(kg)*
TASE400HD NA NA NAPower:40W(average)125W(max)
n.p. n.p. n.p. n.p.
11.3.14 Sensormanningrequirements
Table25.ManningrequirementsforthesensorslistedinTable19.Givenistheminimumnumberofpeopleforoperation,thevesselrequirements,themanningrequirementsfordeploymentandrecoveryaswellasthetrainingneedsforoperatingthesensor.Notethatcellswiththesamekindofinformationforsystemsofthesamemanufacturermaybemerged.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring,NA:notapplicable,n.p.:notprovided.
Manningrequirements
Systemclass
SystemnameMinnumberofpeopleforoperation
Vesselrequirements Deployment Recovery Trainingneeds
AAM
Aquadopp NA NA NA NA AcousticprocessingADP NA NA NA NA AcousticprocessingAZFP NA NA NA NA AcousticprocessingDT-XSUB 1 Largeenoughtosupportsmalldavit 2people 2people AcousticprocessingES853 NA NA NA NA AcousticprocessingGemini720i
1 DependsondeploymentframetypeDependsondeploymentframetype
Dependsondeploymentframetype
Lowleveloftraining(1day)Gemini720is
modularVR2C NA NA NA NA Minimal-manualavailableVMT NA NA NA NA
WBAT NA NA NA NAAcousticprocessing
WBTmini NA NA NA NA
PAM
A-Tag NA NA 1person 1person Yes
AUSOMS-miniBlack NA NA 1person 1personNotreally.Itissimplerecordingsystem
C-POD NA NA Canbestartedaheadofmission,then=nil
NilSetupandstart
C-POD-F NA NA MinimalCornell/AutoBuoys NA NA 1 1 n.p.
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Manningrequirements
Systemclass
SystemnameMinnumberofpeopleforoperation
Vesselrequirements Deployment Recovery Trainingneeds
Decimus NA NA Deploymentdependent DeploymentdependentDependsonhowindepththeytogo
DMON NA NA n.p. n.p. n.p.
SDA14 NA NADependsonmetholodogyandcarrier
Dependsonmetholodogyandcarrier
1-daytrainingprovidesautonomytooperator
SDA416 NA NA2-daytrainingprovidesautonomytooperator
Seicherealtimetranmissionsystem
NA NA n.p. n.p. n.p.
SM2/SM3 NA NA1-3personsdependingonconditions
1-3personsdependingonconditions
Minimal
SoundTrap4channel NA NA n.p. n.p. n.p.SoundTrapHF NA NA n.p. n.p. n.p.WISPR NA NA 1 1 n.p.
VIDEO
CM100 n.p. NA n.p. n.p. n.p.CM202 n.p. NA n.p. n.p. n.p.DualImager n.p. NA n.p. n.p. n.p.EO900 n.p. NA n.p. n.p. n.p.OTUSU135HIGHDEF n.p. NA n.p. n.p. n.p.OTUS-L205HIGHDEF n.p. NA n.p. n.p. n.p.TASE310 n.p. NA n.p. n.p. n.p.TASE400HD n.p. NA n.p. n.p. n.p.
11.3.15 Sensorfurtherinformation
Table26.FurtherinformationonthesensorslistedinTable19.Givenistherawdatavolume,thecapabilitiestoreducereal-timedata,linksandcomments.Abbreviations:AAM:activeacousticmonitoring,PAM:passiveacousticmonitoring,NA:notapplicable,n.p.:notprovided.
Systemclass
Systemname
RawdatavolumeRealtimedatareductioncapabilities
Links Furthercomments
AAM
Aquadopp32bytes+9*128cellsperping
Boththesontekandthenortekhavebeenputongliders-typicallystrappedonasautonomousinstruments
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Systemclass
Systemname
RawdatavolumeRealtimedatareductioncapabilities
Links Furthercomments
ADP n.p.
Boththesontekandthenortekhavebeenputongliders-typicallystrappedonasautonomousinstruments
AZFP
TheAZFPcanoperateinaninternallyrecordingmodeoritcanmakedataavailableinrealtime.Dataratesvarysubstantiallyfrom56124to1159bytesperpingdependingonthenumberoffrequencies,binsizeandrange.
n.p.
DT-XSUB 10MB/SecOnboardstoragewithlowbandwidthsummaryreporting
https://www.youtube.com/watch?v=ISMQ5fzm26I
Goodforfittingtowaveglider
https://www.youtube.com/watch?v=3A1aU2CNKhk
https://planetos.com/blog/sonars-are-going-robotic-an-interview-with-biosonics-eric-munday/
https://www.google.com/url?q=http://www.biosonicsinc.com/pdf/BioSonics%2520LR%2520Wave%2520Glider%2520DTX%2520SUB%2520FLIER.pdf&sa=U&ved=0ahUKEwiqkI6xuvDJAhWikKYKHRVlAnYQFggEMAA&client=internal-uds-cse&usg=AFQjCNG2URYhnR2Mt7KLWZvqakPMYUvDOg
ES853 256bytesperping n.p. Guihenetal.2014 Gemini720i
~3600MB/hour
Arollingstoragefacilityisavailable,whereallfilesforthelastweek(forexample)aesaved,butfilesthataredetectedtohaveahighprobabilitytargetaresavedtoapermanentlocation.Also,compressioncanbeappliedtotherecordeddata(ECD)filesthatachieves~50%reductioninsize.
http://www.tritech.co.uk/product/gemini-720i-300m-multibeam-imaging-sonar
Gemini720is
TheGemini720iswillprovideallthecurrent720ifunctionalityandadditionalimprovementsduetotechnologyadvances
modularVR2C
dependsonnumberoftaggedfish-typicallykbyespermonth
n.p.
VMT n.p. WBAT n.p. n.p. TheminiWBTforglider,theWBAT
forotherAUV.Theycanalsobeputonsurfacevehicles.WBTmini n.p. n.p.
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Systemclass
Systemname
RawdatavolumeRealtimedatareductioncapabilities
Links FurthercommentsPA
M
A-Tag1-128MBdependsonthenumberofdetectedpulseevents
Yes
AUSOMS-miniBlack
2.54GB/hourforLinearPCM yes (dependigonformat)
C-POD 3MB/h yes C-POD-F 30MB/h yes Cornell/AutoBuoys
1GB/hour/channel Yes
Decimus n.p. yes DMON n.p. n.p. SDA14
variable yes
SDA416 Seicherealtimetranmissionsystem
n.p. n.p.
SM2/SM3RecordingMBperhourwill n.p. varybysamplerate.
SoundTrap4channel
upto4GB/h Yes
SoundTrapHF
WISPR880MB/hour/channel/uncompressed
Yes
http://embeddedocean.com/passive-acoustics-2/wispr-v1-0/http://embeddedocean.com/
Alreadyintegratedintoglidersandfloats
VIDEO
CM100 n.p. n.p.http://uavvision.com/product/cm100-html/
CM202 n.p. n.p.http://uavvision.com/product/cm202-3/
DualImager
n.p. n.p.http://www.insitu.com/images/uploads/product-cards/ScanEagle_DualImager_ProductCard_PR041615_1.pdf
EO900 n.p. n.p.http://www.insitu.com/images/uploads/product-cards/ScanEagle_EO900_ProductCard_PR041615_1.pdf
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Systemclass
Systemname
RawdatavolumeRealtimedatareductioncapabilities
Links Furthercomments
OTUSU135HIGHDEF
n.p. n.p.http://uavvision.com/product/cm100-html/
OTUS-L205HIGHDEF
n.p. n.p.http://www.dst.se/gimbal/otus-l205/otus-l205-high-def
TASE310 n.p. n.p.http://www.cloudcaptech.com/products/detail/tase-310
TASE400HD
n.p. n.p.http://www.cloudcaptech.com/products/detail/tase-400-hd
11.3.16 ListofDataRelaysystems
Table27.DataRelaysystemsthatcanbeusedforautonomousvehicles.
Systemclass Systemname Manufacturer Technologyreadinesslevel
WiFi
900MhzAnalogSeicheBespokeSolution SeicheLtd
Fullcommercialapplication
1800MhzAnalog
3.65GHz NanoStation
UbiqitiWiFi2.4GHzBullet
WiFi5GHz
Satellite
Inmarsat FleetOne CobhamSailor250
Iridium MCG-101 Aurora
Iridium Pilot Iridium
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Systemclass Systemname Manufacturer Technologyreadinesslevel
IridiumL-Band L-Band
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11.3.17 Datarelaytechnicaldetails
Table28.TechnicaldetailsoftheDataRelaysystems:Theiroperationalsizeandweight,power,bandwidth,range,purchaseandoperationalcostsaswellasusagerestrictions,datadelayandfurthercomments.
Systemclass
Systemname
Environmentalperformancelimits
Operationalsize(heightxwidthxdepthincm)
Operationalweight(kg)
Power(W)
Bandwidth
RangePurchasecosts
Operationalcosts
Usagerestriction(geographical,political,etc)
Datadelay
Furthercomments
WiFi
900MhzAnalog Seiche
BespokeSolution
Suitableupto20kmRange
37x30x18 <5 30250kHz
10-20km
n.p. Freespectrum
Geographicrestrictionsapplyonfrequencychoice
Negligible
NoAntennashavebeenincludedastheyareverydependentupondeploymentrequirements
Allsystemsofthisnatureareperformancedependentuponenvironment,antennachoice,antennaheight,lineofsightetc.sowouldrequireevaluatingforsuitabilityonaperprojectbasis.
1800MhzAnalog
n.p.
3.65GHzNanoStation
15km+ 30x30x8 0.2 5.5150Mbps
15+km $150SmallLicencefee
IntheUSyouneedanon-exclusivenationwidelicence,thisismoreaformality.Presentlythebasestationandtransmittermustberegistered.
Negligible
WiFi2.4GHz
Bullet
0-50km,inrealityaround2kmforrequiredbandwidths
15x4x3 0.2 1050Mbps
Upto50km
$120
FreeSpectrum
Operatesonconsumerwififreqsomaybeoverloadedspectrumincertainenvironments
Negligible
WiFi5GHz
Upto50km,lessrangethan2.4Ghzversion
$120
Satellite
Inmarsat FleetOne Global Dia33x28 4 150128kbps
Global $10,000+
From$1,300/Monthfor250MBData
n.p.Negligible
Contractcommitmentmayberequried
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System
classSystemname
Environmentalperformancelimits
Operationalsize(heightxwidthxdepthincm)
Operationalweight(kg)
Power(W)
Bandwidth
RangePurchasecosts
Operationalcosts
Usagerestriction(geographical,political,etc)
Datadelay
Furthercomments
Iridium MCG-101
GlobalReach
Dia18cmx10
1.8 30
ShortBurstDatasolution
GlobalReach
$1,600
$40for30,000bytesofdata(Monthly)
n.p.
Negligible
Iridium Pilot Dia57x20 12.5 100128kbps
Global
$5,000+
From$2,000/monthfor200MBData
n.p.
IridiumL-Band
L-Band n.p.16.2x8.1x2.8
0.422.5(0.2standby)
2400bps
$1099+ n.p.
ContinuousDataConnectionorShortBurstData(SBD)forefficienttransferofsmalldatapacketsupto1960bytes.
small(upto20sforSBD)
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11.4 Evaluationmatrices
11.4.1 UASsensorversusplatformmatrix
Table29.CompilationofwhichVideosensorcouldpotentially(o)ordefinitivelybecombined(x),whichcannotbecombined(-),andwhichcombinationwillworkbutwithlimitedmissionduration(l).NA:notapplicable.
Video
Sensor
type
Sensorname
TASE
400H
D
TASE310
CM20
2
CM10
0
OTU
SU13
5HIGHDEF
OTU
S-L205
HIGHDEF
EO90
0
Dua
lIm
ager
UAS
TrimbleUX5 - - - - - - - -
BRAMORC4EYE
- - - - - - - -
ScanEagle - - - - - - x x
PenguinB - - - o x - - -
Fulmar o o o o o o - -
Jump20 x x o o o o - -
OceanEye - - - - - - - -
SwanX1 NA NA NA NA NA NA NA NA
DesertStar10.
- - - - - - - -
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11.4.2 ASV/AUVsensorversusplatformmatrix
Table30.CompilationofwhichPAMorAAMsensorcouldpotentiallyordefinitivelybecombined,sensor/platformcombinationsthatarealreadyintegrated,whichcombinationscannotbecombinedandwhichcombinationwillworkbutwithlimitedmissionduration.
Key:
PAMsensors AAMsensors
Systemname
A-Tag
AUSO
MS-mini
Black
C-PO
D
C-PO
D-F
Cornell/
AutoB
uoys
Decim
us
DMON
SDA14
SDA41
6
Seiche
realtim
etran
mission
system
SM2/SM
3
Soun
dTrap
4cha
nnel
Soun
dTrapHF
WISPR
Aqu
adop
p
ADP
AZFP
DT-XSU
B
ES85
3
Gem
ini720
i
Gem
ini720
is
mod
ularVR2C
VMT
WBAT
WBTmini
ASV
pow
ered
ASV-6300 o o o o o o o o o o o o o o o o o o o o
C-Cat2 o o o o o o o o o o o o o o o o o o o o o o
C-Enduro o o Y o o Y o o o Y o o o o o o o o o o o o
C-Stat o o o o o o o o o o o o o o o o o o o o
C-Target3 o o o o o o o o o o o o o o o o o o o o o o
C-Worker4 o o o o o o o o o o o o o o o o o o o o o o
Cannotbecombined N NA:Notapplicable
Combinationpossiblewithknownlimitationsinmissiondurationordepth L
Couldpotentiallybecombined o
Coulddefinitelybecombined Y
Combinationsalreadyintegrated Y
Notknownifcombinationispossible
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PAMsensors AAMsensors
Systemname
A-Tag
AUSO
MS-mini
Black
C-PO
D
C-PO
D-F
Cornell/
AutoB
uoys
Decim
us
DMON
SDA14
SDA41
6
Seiche
realtim
etran
mission
system
SM2/SM
3
Soun
dTrap
4cha
nnel
Soun
dTrapHF
WISPR
Aqu
adop
p
ADP
AZFP
DT-XSU
B
ES85
3
Gem
ini720
i
Gem
ini720
is
mod
ularVR2C
VMT
WBAT
WBTmini
C-Worker6 o o o o o o o o o o o o o o o o o o o o o o
C-Worker7 o o o o o o o o o o o o o o o o o o o o o o
C-WorkerHydro
o o o o o o o o o o o o o o o o o o o o o o
Delfim o o o o o o o o o o o o o o o o o o o o
Mariner o o o o o o o o o o o o o o o o o o o o
MeasuringDolphin
o o o o o o o o o o o o o o o o o o o o
ROAZI o o o o o o o o o o o o o o o o o o o o
ROAZII o o o o o o o o o o o o o o o L o o o o
RTSYSUSV o o o o o o o o o o o o o o o
ASV
unp
owered
AutoNaut2 o o o o o o o o o o o o o o L L L L L L L
AutoNaut3 o o o o o o o o o o o o o o L L L L L L L
AutoNaut5 o o o o o o o o o o o o o o L L L L L L L
AutoNaut7 o o o o o o o o o o o o o o L L L L L L L
WavegliderSV2
o o o o o Y o o o Y o o Y o L L L L L L L
WavegliderSV3
o o o o o o o o o o o o o o L L L L L L L
AUVglider
ALBAC o o N N N L o L L N N o o o Y Y o o Y Y N Y
Coastalglider o o N N N L o L L N N o o o Y Y o o Y Y N Y
Deepglider o o N N N N o L L N N o o o Y Y o o Y Y N Y
eFòlagaIII o o N N N N o L L N N o o o Y Y o o Y Y N Y
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PAMsensors AAMsensors
Systemname
A-Tag
AUSO
MS-mini
Black
C-PO
D
C-PO
D-F
Cornell/
AutoB
uoys
Decim
us
DMON
SDA14
SDA41
6
Seiche
realtim
etran
mission
system
SM2/SM
3
Soun
dTrap
4cha
nnel
Soun
dTrapHF
WISPR
Aqu
adop
p
ADP
AZFP
DT-XSU
B
ES85
3
Gem
ini720
i
Gem
ini720
is
mod
ularVR2C
VMT
WBAT
WBTmini
LiberdadeXwing/Zray
o o N N N N o L L N N o o o Y Y o o Y Y N Y
Petrel o o N N N N o L L N N o o o Y Y o o Y Y N Y
SeaBird o o N N N N o L L N N o o o N N N N N N N N
SeaExplorer o o N N N L o L L N N o o o Y Y o o Y Y N Y
Seaglider(ogive)
o o N N N N Y L L N N o Y Y Y Y o o Y Y N Y
SlocumG2glider
o o N N N L Y L L N N o Y o Y Y o o Y Y N Y
SlocumG2hybrid
o o N N N N o L L N N o o o Y Y o o Y Y N Y
SlocumG2thermal
o o N N N N o L L N N o o o Y Y o o Y Y N Y
Spray o o N N N N o L L N N o o o Y Y o o Y Y N Y
Sterneglider o o N N N N o L L N N o o o Y Y o o Y Y N Y
TONAI o o N N N N o L L N N o o o Y Y o o Y Y N Y
AUVprope
ller
A18-D o L L o o o L o N o L o o o o o L L L o
A9-M o o L o o o o o N o L o o o o o o o o o
Bluefin-12D o L L o o o o o N o L o o o o o L L L o
Bluefin-12S o o L o o o o o N o o o o o o o o o o o
Bluefin-21 o L L o o o L o N o L o o o o o L L L o
Bluefin-9 o o L o o o o o N o o o o o o o o o o o
Bluefin-9M o o L o o o o o N o o o o o o o o o o o
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PAMsensors AAMsensors
Systemname
A-Tag
AUSO
MS-mini
Black
C-PO
D
C-PO
D-F
Cornell/
AutoB
uoys
Decim
us
DMON
SDA14
SDA41
6
Seiche
realtim
etran
mission
system
SM2/SM
3
Soun
dTrap
4cha
nnel
Soun
dTrapHF
WISPR
Aqu
adop
p
ADP
AZFP
DT-XSU
B
ES85
3
Gem
ini720
i
Gem
ini720
is
mod
ularVR2C
VMT
WBAT
WBTmini
HUGIN1000(1000mversion)
o o L o o o o o o N o o L o o o o o o L o o
HUGIN1000(3000mversion)
o L L o o o L o o N o o L o o o o o L L L o
HUGIN3000 o L L o o o L o o N o o L o o o o o L L L o
HUGIN4500 o L L o o o L o o N o o L o o o o o L L L o
MUNIN o L L o o o o o o N o o L o o o o o L L L o
REMUS100 o o o o o o o o o N o o o o o o o o o o o o
REMUS3000 o L L o o o o o o N o o L o o o o o L L L o
REMUS600 o o L o o o o o o N o o L o o o o o o L o o
REMUS6000 o L L o o o L o o N o o L o o o o o L L L o
RTSYSAUV o o L o o o o o o N o o o o o o o o o
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11.5 Suppliercontacts
Table31.Listofsuppliercontactsoftheplatformsandsensorsincludedinthisreview.
Systemclass
Manufacturer Systemname(s) Street+No Town Postcode Country Phonenumber website
ASV
-po
wered
ASVC-Enduro,C-Worker4+6+7+Hydro,C-Cat2,C-Target3,C-Stat,ASV-6300
Unit12MurrillsEstate,SouthamptonRoad
Portchester PO169RD UK T+44(0)2392382573 asvglobal.com
InstitutoSuperiordeEngenhariadoPorto
ROAZIandIIRuaDr.AntónioBernardinodeAlmeida
Porto431,4200-072
Portugal T+351228340500 www.isep.ipp.pt
RTSYS SeawaysUSV RueMichelMariono 56850Caudan France T+33297898582 www.seaways.fr
RostockUniversity MeasuringDolphinAnwendungszentrumRegelungstechnik,Tannenweg22d,imRostockpark
Rostock 18059 Germany T+493814987727 www.uni-rostock.de
InstitutoSuperiorTécnico
DelfimTorreNorte,7thFloor.Av.RoviscoPais
Lisbon1.1049-001
Portugal T+351218418289 tecnico.ulisboa.pt
MaritimeRoboticsAS Mariner Brattørkaia11 Trondheim 7010 Norway T+4773401900 www.maritimerobotics.com
ASV
-un
powered
LiquidRobotics WavegliderSV2+3 1329MoffettParkDr Sunnyvale94089-1134
UnitedStates
T+14086364200 liquidr.com
MOST(AutonomousVessels)Ltd
AutoNaut2+3+5+7UnitA5TheBoatyard,ChichesterMarina
Chichester PO207EJ UK T+44(0)1243511421 www.autonautusv.com
AUV-glider
ALSEAMAR–ALCEN Seaexplorer 9Europarc 13590Meyreuil France T+33442484452 www.acsa-alcen.com
EcoleNationaleSuperioreD'IngenieursBrest
Sterneglider Nocontactdetails NocontactdetailsNocontactdetails
Nocontactdetails
Nocontactdetails Nocontactdetails
ExocetusDevelopmentLLC
Coastalglider 1444East9thAve Anchorage 99501 USA 9072278073 exocetus.com
Graaltech Folaga viJ.Ruffini9/r 16128Genova Italy T+390108597680 www.graaltech.com
KongsbergSeaglider(normal-alsoenquiredeep)
1921033rdAvernueWest SeattleWA98036-4707
USA T+14257121107 www.km.kongsberg.com
KyushuInstituteofTechnology
SeaBird 1-1SensuichoTobataWard,Kitakyushu
804-0015 Japan T+81938843000 www.kyutech.ac.jp
Nortek Aquadopp Vangkroken2 1351Rud Norway T+4767174500 www.nortek-as.com
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Systemclass
Manufacturer Systemname(s) Street+No Town Postcode Country Phonenumber website
SCRIPPSSprayGlider,LiberdadeXwing/Zray
9500GilmanDrive LaJolla92093-0230
USA (858)4614728 www.scripps.edu
TeledyneWebbSlocumglider(electric,thermalandhybrid)
49EdgertonDrive NorthFalmouth MA02556 USA T+15085482077 www.teledyne.com
TianjinUniversity Petrel Nocontactdetails NocontactdetailsNocontactdetails
China Nocontactdetails Nocontactdetails
TokaiUniversity ALBAC 2-28-4Tomigaya Shibuya 151-0063 Japan T+81334672211 www.u-tokai.ac.jp
OsakaPrefectureUniversity,TaijiWhaleMuseum,Cetus
TONAI:TwilightOcean-ZonalNaturalResourcesandAnimalInvestigator
Nocontactdetails NocontactdetailsNocontactdetails
Japan Nocontactdetails Nocontactdetails
AUV-prop
eller BluefinRobotics Bluefin9,9M,12D,12S,21 553SouthStreet Quincy 2169 USA T+1(617)7157000 www.bluefinrobotics.com
ECAGroup A9-M,A18-D Z.I.ToulonEst262,ruedesfrèresLumière
83130LaGarde
France T+33(0)494089000 www.ecagroup.com
KonsbergMaritime REMUS,HUGIN,MUNIN Kirkegårdsveien45 NO-3616Kongsberg NorwayT+4732285000
www.km.kongsberg.com
RTSYS AUVrobots RueMichelMariono 56850Caudan France T+33297898582 www.rtsys.eu
UASkite
FlyingRobotsSA SwanX1 RueThalberg,2 Geneva CH-1201Switzerland
www.flying-robots.com
UAS
l-t-a AllsoppHelikitesLtd DesertStar
Unit2,FordingbrigdeBusinessPark
Fordingbridge,Hampshire
SP61BD UK T+441425654967 www.allsopp.co.uk
MaritimeRoboticsAS OceanEye Brattørkaia11 Trondheim 7010 Norway T+4773401900 www.maritimerobotics.com
UAS-p
owered
-fixed
ArcturusUAV Jump20 P.O.Box3011 RohnertPark 94928 USA (707)2069372 arcturus-uav.com
C-Astrald.o.o. BRAMORC4EYE,gEO,rTK Gregorčičevaulica20 Ajdovščina 5270 Slovenia T+386(0)40121119 www.c-astral.com
Insitu ScanEagle 118EastColumbiaRiverWay Bingen,Washington 98605 USA T+15094938600 www.insitu.com
ThalesEspañagrp,S.A.U.
Fulmar SerranoGalvache,56 Madrid 28033 Spain T+34912737200 www.thalesgroup.com
TrimbeNavigationLimited
UX5 Buchtenstraat9/1 Gent 9051 Belgium T+3293350515 uas.trimble.com
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Systemclass
Manufacturer Systemname(s) Street+No Town Postcode Country Phonenumber website
Uavfactory PenguinB 50SouthBuckhoutStreet Irvington 10533 USA T+1(914)5913296 www.uavfactory.com
AAM
ASL AZFP #1-6703RajpurPlace Victoria,BC V8M1ZS Canada T+1-250-656-0177 www.aslenv.com
BiosonicsAutonomoussubmersibleechosounder
4027LearyWayNW Seattle 98107 U.S.A. 2069636369 biosonicsinc.com
Imagenex ES853 209-1875BroadwayStreet PortCoquitlam, V3C4Z1 Canada 604-944-8248 www.imagenex.com
Kongsberg/Simrad WBAT+WBTmini P.O.Box111Strandpromenaden50
3191 Norway 4733034026 www.simrad.com
Nortek Aquadopp Vangkroken2 1351Rud Norway T+4767174500 www.nortek-as.com
Sontek ADP 9940SummersRidgeRoad SanDiego CA USA T+1(858)5468327 www.sontek.com
Tritech Gemini720i&720isPeregrineRoad,WesthillBusinessPark
Westhill AB326JL UK T+44(0)1224744111 www.tritech.co.uk
Vemco modularVR2C+VMT 20AngusMortonDrive Bedford,NovaScotia B4B0L9 Canada T+1-9024501700 vemco.com
PAM
AquasoundInc. AUSOMS-miniBlackKyotoResearchCenter,46Shimoadachi-cho
YoshidaSakyo-ku 606-8501 Japan T+81-0757539670 aqua-sound.com
Chelonia CPOD+C-POD-F TheBarkhouse,NorthCliff Mousehole TR196PH UK T+44(0)1736732462 www.chelonia.co.uk
Cornell(PAM)/EOS(buoy)
Cornell/AutoBuoys/DOSITSCornellLabofOrnithology,159SapsuckerWoodsRd.
Ithaca 14850 USA 607-2546250 www.birds.cornell.edu
EmbeddedOceanSystems
WISPR Seattle USA (206)7660671 embeddedocean.com
JASCO AMARTheRoundel,StClair’sFarm,WickhamRoad,
Droxford SO323PW UK (0)1489878439 www.jasco.com
MarineMicroTechnology
A-Tag 4-12-1Takakura IrumaCity Japan 0429654127
OceanInstrumentsNZ SoundTrapHF+4channel 961SandspitRd Warkworth 982NewZealand
6499233601 www.OceanInstruments.co.nz
RTSYS PAM:RB-SDA14,BA-SDA14 RueMichelMariono 56850Caudan France 33297898582 www.rtsys.eu
SAInstrumentationLtd Decimus MillCourt,MillLane Tayport DD69EL UK T+44(0)1334845260 www.sa-instrumentation.com
SeicheMeasurementsLtd
Seicherealtimetranmissionsystem
BradworthyIndustrialEstate,LangdonRoad,Bradworthy
Holsworthy EX227SF UK T+44(0)1409404050 www.seiche.com
WHOI DMON 266WoodsHoleRoad WoodsHole02543-1050
USA T+15082892906 www.whoi.edu
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Systemclass
Manufacturer Systemname(s) Street+No Town Postcode Country Phonenumber website
WildlifeAcoustics SM2/SM3 3ClockTowerPlace,Suite210 Maynard01754-2549
USA T+1(978)3695225 www.wildlifeacoustics.com
Video
CloudcapTechnology TASE400HD+310 202WascoLoop,Suite103 HoodRiver 97031 USA (541)3872120 www.cloudcaptech.com
DSTOTUSU135+L205HIGHDEF
Åkerbogatan10 Linköping 582 Sweden T+4613211080 www.dst.com
Insitu EO900+DualImager 118EastColumbiaRiverWay Bingen,Washington 98605 USA T+15094938600 www.insitu.com
UAVvision CM202+100 10UrallaStreet PortMacquarie 2444 Australia T+61265811994 uavvision.com
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11.6 Surveyingwildlifepopulations
Thissectionprovidesfurtherdetailsonthestatisticalsideofdensityestimatesexplaininghowsurveydataare
analysed (section11.6.1)andwhichmethodsarecurrentlyavailable forestimating thedetectionprobability
(section11.6.2),whichservesforabetterunderstandingontherelevanceofretrievingappropriatesurveydata
fromacarefullyplannedandexecutedsurveydesign.
11.6.1 Estimatingabsolutepopulationdensityandabundance–anoverview
Statistical data analysismethods have been developed that correct observed data for undetected animals,
leadingtoabsoluteestimatesofanimalabundanceordensity(Borchersetal.,2002).Atthedataanalysisstage,
estimatorsareusedtoproducetherequireddensityorabundanceestimates.Atypicalestimatorforabsolute
densityis:
! = #$% (Eqn.1)
Where
!istheestimateddensity,
nisthenumberofencountersmadeduringthesurvey,
&istheestimatedaverageprobabilityofdetectinganencounterand
aisthetotalsurveyedarea.
It is rare that an entire study areaof interest canbe completely coveredby a survey (due to logistical and
financialconstraints)soitisoftenthecasethatthesurveyedareaisasubsampleofthestudyarea.Absolute
abundance in the study area can be estimatedusing Eqn. 2, if the survey has beendesigned such that the
surveyedareaisrepresentativeofthestudyarea,otherwiseresultingabundanceestimatesmaybebiased.
' = !×) (Eqn.2)
Where
'istheabsoluteabundanceinthestudyareaand
Aisthesizeofthestudyarea.
Theabilitytorelyonasurvey’sdesigntoinferthatestimatedanimaldensityisrepresentativeofdensityinthe
widerstudyarea,andthatabundanceinthestudyareacanbeestimatedusingEqn.2,isknownasdesign-based
inference (Borchers et al., 2002). An adequate survey design comprises multiple samplers such as survey
transects,whichhavebeenrandomlysampledfromallpossiblesamplersinthestudyarea(orstratum,ifthe
surveyisstratifiedgeographically)withequalprobability.Asystematicrandomdesignistypicallypreferableto
acompletelyrandomdesignbecauseitproducesmorepreciseestimates.Furthermore,thesamplersshouldbe
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randomlydistributedwithrespecttothestudyspecies’distribution(seeBucklandetal.,2001;2015formore
detailaboutsurveydesign). Ifasurveyhasnotbeendesigned,or therealisedsurveyhasdeviated fromthe
planneddesign, thenmodel-based inferencecanbeused insteadto inferdensityandabundanceacross the
studyarea.Thisapproachusesastatisticalmodelto linkestimateddensity inthesurveyedareawithspatial
covariates.Themodelcanthenbeusedtopredictthedensity/abundanceinunsurveyedpartsofthestudyarea
(e.g.CañadasandHammond,2006;Miller,2013).However,whilethisapproachrelaxestheneedforastrict
surveydesign,thereisstillarequirementthat,foranyselectedcovariate,therangeofmodelledvaluesmatches
therangeofvaluesinthestudyarea.Biasedresultsmayoccurifthespatialmodelisusedtogenerateabundance
predictionsusingcovariatevaluesthatarelowerorhigherthanthevaluesusedtobuildthemodel(i.e.model
extrapolation).Therefore,thesurveyedareamustberepresentativeofthewiderstudyarea.
Thedefinitionofanencountercanchangedependingonthespeciesofinterestandthesurveytype.However,
density estimators are flexible and can be altered accordingly. In some cases, it may be possible to count
individuals(visuallyoracoustically)butinothersurveysofhighlysocialorshoalinganimals,groupsofanimals
may form the observed encounters. When groups are counted, average group size can be included as an
additionalmultiplyingparameter,or“multiplier”,inthedensityestimatortoconvertestimatedgroupdensity
into estimated animal density. In the caseof shoaling fish and zooplankton,where group size estimation is
particularlydifficult,thereisalargeliteratureontheconversionofechosounderdatatoestimatesofdensityor
abundance(e.g.JohannessonandMitson,1983;Foote,2009).Alternatively,averagebiomassmaybeestimated
(e.g.Coxetal.,2011). Inpassiveacousticmonitoring,callscanoftenbereadilycountedandanaveragecall
productionrateisthenrequiredtoconvertestimatedcalldensitytoestimatedanimaldensity.Furthermore,
acousticdataareoftenautomaticallyprocessedandanadditionalmultiplierisrequiredtocorrectthenumber
ofobservationsforfalsedetections.Atypicaldensityestimatorforusewithpassiveacousticdatais,therefore:
! = # *+,$%- (Eqn.3)
Where
nisthenumberofacousticencounters,
fistheproportionoffalsedetectionsgeneratedbythedetectionroutineand
.representstheappropriatemultiplier(s)thatwillconverttheobjectdensitytoanimaldensity.
If, forexample, individualcallswerecounted inasurvey,thenrequiredmultiplierswouldbeanaveragecall
productionrate(anestimatedparameter)andtheamountoftimespentmonitoring(aconstant).
Theprobabilityofdetection typically correctsencounters forperceptionbias. This isbias causedbymissing
encountersthatwereavailablefordetectionbutwerenotdetectede.g.becauseofhighseastateduringvisual
surveysorhighbackgroundnoiseinacousticsurveys.Anotherformofbiasthatmustbeconsideredisavailability
bias.Thisisbiasthatarisesfrommissinganimalsthatwerepresentinthesurveyareabutwerenotavailableto
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bedetected.Invisualsurveysfromsurfacevesselsoraerialplatforms,animalsareonlyavailabletobeseenat
thesurfaceoratveryshallowdepthsandarethereforeonlytemporarilyavailableduringthesurvey.Therefore,
anadditionalmultiplierisrequiredinthedensityestimatortoaccountfortheproportionofanimalsthatwere
unavailabletobesurveyed.Similarly,inacousticsurveys,animalsarenotconstantlyvocal,soamultiplier,such
ascallproductionrate,isrequiredtoaccountforanimalsinthepopulationthatwerenotvocalisingatthetime
ofthesurvey.Furthermore,someanimalswillbepermanentlyunavailableinpassiveacousticsurveysifthey
neverproducethespecificvocalisation(s)thatarebeingmonitored.Thisproportionofthepopulationmustalso
beaccountedforinthedensityestimatorusinganappropriatemultiplier.Itisimportanttorecognisethatany
estimatedmultiplier (including theprobabilityofdetection)may showspatial and temporal variation.Using
multipliers from the literature or that have been estimated from datasets collected from another study
population (or even the same study population but at a different location and/or time) may result in the
applicationofthewrongmultiplierand,ultimately,biasedresults.
11.6.2 Methodstoestimatedetectionprobability
Wildlifesurveystypicallytakeplacealongplannedsurveytransectsorfromstaticmonitoringpoints.Insome
surveys,itmaybepossibletodetectallanimalswithinthetransectlinesorpoints.Suchsurveytypesareknown
asstriptransectsampling(usingtransectlines)orplotsampling(usingpoints).
Whenanimalsaresuspectedtobemissedwithinsurveyedareas,thereareseveralmethodsthatcanbeusedto
estimatetheprobabilityofdetection.Distancesampling,forexample, isacommonly-used,versatilemethod
thathasbeenwidelyappliedtomarinesurveys(Bucklandetal.,2001;2015).Distancesamplinghasbeenapplied
tovisualandacoustic(bothactiveandpassive)datatoestimatetheabundanceofmarinemammals,turtlesand
krillswarms.Distancesamplingdatahavebeencollectedfromavarietyofmarinesurveyingplatformsincluding
vessels and aircraft that follow survey lines (line transect sampling) and stationarymonitoring points (point
transectsampling).Detectionprobabilitiesestimatedusingdistancesamplingcanbeusedindensityestimators
suchasthoseinEqns1and3.
Mark-recaptureisanotherstandardabundanceestimationmethod(Borchersetal.,2002)buthasanalternative
estimation framework to distance sampling, involving different estimators to those in Eqns. 1 & 3. Mark
recapturehasbeenusedwithvisualdata toestimatemarinemammal (e.g.Cheneyetal.,2014), turtle (e.g.
Duttonetal.,2005)andfishabundances(e.g.Bradshawetal.,2007).Datafromanimal-borneactiveacoustic
tagscanalsobeanalysedusingofmark-recapture;taggedanimalsarere-identifiedastheyswimpastdeployed
acousticreceivers(e.g.Dudgeonetal.,2015).Adifficultyofmark-recapturemethodsisthat,whileabundance
canbeestimated,it isnon-trivialtodeterminethesizeofthesurveyedareaand,therefore,estimateanimal
density.However,spatially-explicitcapture-recapture(SECR),arelativelyrecentextensiontomark-recapture
methodology, allowsdensity, aswell as abundance, tobe inferred from the collecteddata.Despitebeinga
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recentlydevelopedmethod,SECRisconsideredtobea“standard”abundanceanddensityestimationapproach
duetoitslinkswithmark-recaptureandalsodistancesampling(Borchers,2012;Borchersetal.,2015).
SECRhasbeenusedwithbothvisual(Pirottaetal.,2014)andpassiveacoustic(Marquesetal.,2011;Martinet
al.,2012)datatoestimatetheabundanceanddensityofmarinemammals.Theabilitytoestimatedensityisa
majoradvantageofSECRovermark—recaptureand,therefore,SECRwillbediscussedinpreferencetomark-
recaptureintherestofthereport.
IfdistancesamplingorSECRcannotbeused,detectionprobabilitiescanbeestimatedusingalternative“non-
standard” methods. Many of the non-standard methods were developed specifically for use with passive
acousticdatatoestimatemarinemammaldensity.Thesemethodsoftenuseauxiliaryinformation.Forexample,
a“trial-based”methodimplementedbyMarquesetal.,(2009)usedpassiveacoustictagdata(specificallyDTag
data:Johnson&Tyack.,2003)tocrossreferencewhetherclicksproducedbythetaggedanimalwerereceived
ornotonfixedhydrophonesinthearea.Theprobabilityofdetectingabeakedwhaleonafixedhydrophone
couldthenbemodelledasafunctionofrange(Marquesetal.,2009).
The various methods require different information to be recorded about encounters. In general, distance
samplingrequiresthatthehorizontalandperpendicularrangeismeasuredfromthetransectlineorpointto
eachencounter.Rangeestimationmethodsusingpassiveacousticdatathatcontainambiguityaboutwhether
theencounterwasontheleft-orright-handsideofthetransectline,orthebearingfromapoint,areacceptable.
However,anestimateofhorizontalrangerequiresthedepthofacoustically-detectedanimalstobeestimated
(but also see Cox et al., 2011 and Harris, 2014 for alternative approaches to extending standard distance
samplingtoaccountforanimalsatdepth).
Spatially-explicitcapture-recapturewhenusedwithvisualdatarequiresthat individualsarere-identified, i.e.
“re-captured” across surveying occasions and that the location of each encountered animal is recorded. In
passiveacousticsurveys,thedatarequirementsforSECRaresomewhatdifferent.Hydrophonesareconsidered
tobe“traps”ofknownlocation,andthesameacousticencountercanbe“caught”onmultiplehydrophones.
The spatial pattern of captures of a given acoustic encounter provides information about the detection
probabilityoftheencounter.Receivedlevelandtime-of-arrivaloftheacousticencounterscanalsoimprovethe
precisionoftheresults(Stevensonetal.,2015).
Non-standardmethodscanbeimplementedwhenthereislessinformationavailableabouttheencounter(i.e.,
ranges,bearingsorrecapturescannotberecorded)butthemethodsrelyonmoreassumptionsandmodelling
tocompensateforthelackofempiricaldataandshouldnotbeconsideredinpreferencetostandardapproaches.
Depending on the particular method being used, measures of received levels and noise levels, as well as
estimates of transmission loss (estimated using sound propagation models) may be useful. These acoustic
measurescanbecombinedinasimulationframeworkthatcanbeusedtoestimatedetectionprobability(e.g.
Kuseletal.,2011).Finally,likestandardmethods,anyadditionalmultipliersthathavebeenidentifiedasrelevant
foragivensurvey,e.g.callproductionrateorgroupsize,willalsoberequired.
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Eachdensityestimationmethodhasassociatedassumptionsofvaryingseverity.Violationofkeyassumptions
canleadtobiasinestimateddetectionprobabilitiesand,ultimately,abundanceordensityestimates.Thereare
fourmainassumptionsindistancesampling(Bucklandetal.,2015).Theseare:(1)thattransectlinesorpoints
havebeenplacedrandomlywithrespecttothedistributionofthestudyspecies(thisisimportantnotonlyfor
thedesign-basedabundanceinference,butalsoforthedetectionprobabilityestimation),(2)anyencounteron
(i.e.,atzerodistancefrom)thetransectlineoratthecentreofthemonitoringpointisdetectedwithcertainty,
(3)thatencounteredanimalsaredetectedattheirinitiallocationi.e.,thereisnoanimalmovementand(4)that
measureddistancesareaccurate.ThemainassumptionofSECRarethatcaptures(whetherindividualanimals
oracousticencounters)canbeaccuratelyre-identifiedacrosscaptureoccasions.WhenusingSECRwithpassive
acousticdata,itiscurrentlyassumedthat(1)allcallsareemittedatthesamesourceleveland(2)thatthereis
noeffectofsignaldirectionalityondetectionprobability(Stevensonetal.,2015).Non-standardmethodsvary
intheirassumptionsbutasharedimportantassumptionisthatanyauxiliarydatausedintheanalysisisrelevant
tothestudypopulationatthetimeandplaceofthestudy.
The final aspect of abundance and density methodology to discuss is variance estimation. Estimating the
uncertainty in a density or abundance estimate is a crucial part of an analysis. It is important to generate
estimatesthatareaspreciseaspossiblesothattheycanbeusefulinmanagementandmitigationdecisions.The
deltamethodisaconvenientwayinwhichtheuncertaintyassociatedwitheachparameterinagivendensity
estimatorcanbecombinedtoestimateanoverallvarianceforthedensityorabundanceestimate(Seber,1982).
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12 Glossary of Terms, Acronyms and Abbreviations Term Description
AAM ActiveAcousticMonitoring
ADCP AcousticDopplerCurrentProfiler
AMG AircraftManagementGuidelines
AMOC AlternativeMethodOfCompliance
ASV AutonomousSurfaceVehicles
ATC AirTrafficControl
AUV AutonomousUnderwaterVehicles
BAS BritishAntarcticSurvey
BLOS BeyondVisibleLineOfSight
bps,kbps,Mbps Bitspersecond,Kilobitspersecond,Megabitspersecond.N.B.8bitsarerequiredto
formoneByteofdata.Mostcommunicationssystemsquotebandwidthinbps.
COTS CommercialOff-The-Shelf
CREEM CentreforResearchintoEcologicalandEnvironmentalModelling
CT Condutivity-Temperatureprobe
DS DepthSensor
DTAG DigitalAcousticRecordingTag
DVL DopplerVelocityLogger
E&P ExplorationAndProduction
ERMA EnergyRatioMappingAlgorithm
EVLOS ExtendVisibleLineOfSight
FAA FederalAviationAdministration
FLAC FreeLosslessAudioCodec
FLS Forward-LookingSonar
GENIoS Glider-EnvironmentNetworkInformationSystem
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Term Description
GPS GlobalPositioningSystem(simpleordifferential)
HSE HealthAndSafetyExecutive
IMU InertialMeasurementUnit
INS InertialNavigationSystem
IR Infrared
Iridium Iridiumsatellitecommunications
ISAS InterferometricSyntheticApertureSonar
LBL LongBaseLine
LFDCS LowFrequencyDetectionAndClassificationSystem
LIDAR LightImaging,Detection,AndRanging
MBES MultiBeamEchoSounder
MMO MarineMammalObserver
NCAA NationalCivilAviationAuthority
NORUT NorthernResearchInstitute
PAM PassiveAcousticMonitoring
PAR PhotosyntheticallyActiveRadiation
PSO ProtectedSpeciesObserver
RF RadioFrequencylink
RHIB Rigid-HulledInflatableBoat
RiNKO TypeofphosphorescentDOsensor
RPAS RemotelyPilotedAircraftSystems
SAS SyntheticApertureSonar
SBL ShortBaseLine
SBP Sub-BottomProfiler
SMRU SeaMammalResearchUnit
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Term Description
SSS SideScanSonar(singleordualfrequency)
SUNA SubmersibleUltravioletNitrateAnalyzer
SVTP Sound,Velocity,TemperatureandPressure
SVS SoundVelocitySensor
TS TurbiditySensor
UAS UnmannedAerialSystems
UNCLOS UnitedNationsConventionOnTheLawOfTheSea
USBL Ultra-ShortBaseLine
UTP UnderwaterTransponderPositioning
VLOS VisibleLineOfSight
WHOI WoodsHoleOceanographicInstitution
WiFi WiFilink
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