Path Planning and Obstacle Avoidance Strategies for

Autonomous Underwater Vehicles

Prof. T. AsokanIndian Institute of Technology Madras

asok@iitm.ac.in

Areas of Research and development

1. Autonomous Underwater Robots: Design, Modelling, and Control

2. Dynamic station keeping control of ROV3. Multipoint Potential Field method for

obstacle avoidance

4. AUV-Manipulator Systems

5. Surgical Robotics- Design and Development of Tele-surgical trainer robot (Filed 2 patents)

6. Tremor compensation in surgical robotics7. Aerial Robotics: Design of Novel quadrotor-

VOOPS (Filed 2 patents)8. Underwater mechatronic system for

selective deployment (Filed 1 patent)9. Task allocation in multirobot systems

Product Design and Development

1. Limb Immobilisation splint for trauma victims Patented; Licensed to and commercialised by M/s HLL Life care (Asokan, Rahul Ribeiro, Pulin, Darshan)(first product to be commercialized out of the Stanford-India Biodesign program)

2. Combinational Scissor-Grasper for laparoscopic surgery(Asokan, Chinmay Deodhar)Patented and Licensed to M/s Intuitive Surgicals,

USA

3. Wound cleaning Robot for hospital application

4. Non-Destructive Method to Identify Used Syringes (Patent filed in 2012; innovation award from M/s Intellectual ventures; JC Bose Patent award

2013)

5. A device for descaling the inner wall of a tank (Patent filed in 2008)

6. Fetal Growth Monitor for Rural Application

7. Proportional solenoid for space applications (ISRO)

Acknowledgements

Some of the works presented are from the published works of research students. The contributions by the following research scholars/ organisations are thankfully acknowledged:

Dr M Santhakumar

Dr T Periasamy

Dr S Saravanaklumar

NSTL Vizag

Contents

Introduction

Underwater Robots

Atonomous Underwater Vehicles

AUV Design

Design of Controllers

Path Planning

Obstacle Avoidance

Experimental Methods

Conclusions

UNDERWATER WORKING METHODS

3/17/2016 8

Why Underwater Robots ?

Motivation

US$300 B offshore industry Demand for pipeline survey and

inspection New installation of underwater pipeline Periodic checking required of installed

pipeline Task is performed under structured

conditions Extended duration of surveys

up to weeks for extensive pipelines boredom and fatigue

Major Players:

Institutes: University of Tokyo, MIT, Hawai University, WoodsholeOceanographic Institute, etc

Industry:

Alstom Schilling, Perry Tritech, TritechInternational etc..

Human Occupied Vehicle (HOV)

ALVINWoods hole Oceanographic Institute

3/17/2016 10

Unmanned Robotic Vehicles

OperationMode

Task Complexit

y

Tele-operation Supervisory Autonomous

ROV

HybridURV

AUV

Hybrid URV canswitch to direct tele-operation mode forcomplex tasks

Hybrid URV can switch to autonomous mode for simple tasks

Comparison of Task Complexity

Remotely Operated Vehicle (ROV)Tethered Supervised Vehicle:

The vehicle is connected to a mother ship by a cable through which communications, data transmissions and power supply are carried out.

Restricted operating range

Mother ship to follow the ROV

Pilot needs to be trained well

Operator fatigue affects mission results

Tether management is the main challenge

Operationally efficient compared to other class of vehicles

ROV Deployment and Applications

Diver Observation Platform Inspection Pipeline Inspection Surveys Drilling Support Construction Support Debris Removal Platform Cleaning

Sub-sea Installations

Telecommunications Support Object Location and Recovery

Autonomous Underwater Vehicle (AUV)

It is a robotic device that is driven through the water by apropulsion system, controlled and piloted by an onboardcomputer and maneuverable in three dimensions.

It needs to be Pre-programmed

Degree of human intervention will be a function of communication capability

AUV will require fool-proof navigation, control and guidance systemson board to meet the mission accuracy requirements

Transmission of data back to mother ship if on-board data storage with post mission retrieval does not meet the mission requirements

Autonomous Underwater Vehicle (AUV)

Geological Survey Earthquake detection and

monitoring. Tsunami detection. Volcano monitoring. Salinity monitoring. Seabed and deep sea

exploration. Oil and Gas Industry

Seabed survey. Pipeline monitoring. Intervention. Iceberg reconnaissance.

Marine Science Research Marine Biology Research Oceanography Studies

Shipwreck Reconnaissance Fisheries

Academic Research. Development. Teaching. Practical applications. Projects.

Defense Reconnaissance. Monitoring. Detection. Surveillance.

Customs Underwater power lines

Line laying. Line monitoring

Delivery Systems Telecommunications Repair and Rescue

Applications of AUVs

Main Building blocks of AUV Control System

Data loggingData logging

management

CommunicationsRF and Acoustic modem

NavigationRate gyro,

Compass,

Depth or Pressure sensor,

Doppler log,

Echo sounder,

GPS

Mission ControlOcean Sensors

Vehicle SafetyActuator Control

Thrusters, rudder and

stern planes

Vehicle

Guidance and

Control

Set

points

Reference

Trajectory

Vehicle

Dynamics

NavigationSystem

support data

Control, Guidance, and Navigation

Where am I going? How do I get there?

Control: To ensure that the various performance parameters are achieved in the system: eg: Speed, Acceleration, etc.

Guidance: To ensure that the commanded path is followed by the system

Navigation: To inform the MCS about the present status of the vehicle wrt the commanded position as well as react to the environmental changes

18

Mathematical Model of the AUV

where M = MRB+MA = Inertia matrixMRB = Rigid body Inertia MatrixMA = Added mass matrixD() = Damping matrix = Control inputs = Bf = FT + FCPFT = Propulsion forces and momentsFCP = Control plane forces and momentsC() = C()RB+ C()A = Coriolis and Centripetal Matrixg() = Restoring matrix (gravitational and buoyancy effects)

= Linear and angular velocities w.r.t. body (moving) frame = [u v w p q r]T

= Linear and angular displacements w.r.t. inertial (fixed) frame= [x y z ]T

The AUV kinematics can be expressed as

)g()D()C(M

)(J where, J() is the kinematic transformation (Jacobian) matrix.

f)(B)(g),(D),(C)(M

The AUV dynamic model with respect to the earth fixed frame of reference becomes

)))))((1 g(D(C(M

0

5

10

15

20

25

30

-25

-20

-15

-10

-5

0

5

-100

0

100

200

300

400

500

600

700

800

x in m

3D plot

y in m

z in m

Only one propulsion thruster activated, CB=[0 0 -7]mm,& CG=[0 0 0]mm and the vehicle net buoyancy effect is equal to+25kgf

0 100 200 300 400 500 600 700 800 900 1000-100

-80

-60

-40

-20

0

20

Time (t) in Sec

Angula

r D

ispla

cem

ent

in d

eg

Angular Displacement vs Time

Roll

Pitch

Yaw

21

Closed-loop (feedback) Control

Feedback

Control system

Environmental

Disturbances

AUV Dynamics AUV Kinematics

Thruster

Forces and

moments

JM-1

g(.)

C+D

Thruster

dynamics

d

d

Rudder &

stern plane

Forces and

moments

Control planes

dynamics

GNC

Sensor

System

Mission

control

d

Sensor

Noises

+

+

+

+

+_+

_

+

e

Vehicle Control System

23

Tracking Controller Design

Model reference control structure

Hybrid control structure

Tracking control structure

Trajectory tracking requires the design of control laws that guide the vehicle to

track an inertial trajectory, i.e., a 3D path on which a time law is specified.

Guidance System

Development of strategy and algorithm for obstacle avoidance and

path planning

Guidance Systems: Receive target related informationfrom the navigation and generate references for the vehicle

control system so as the vehicle can move through a set of

way points as per the given sequence.

Time variant trajectory tracking or time invariant path

following.

Guidance systems include waypoint guidance, obstacle

avoidance, minimum time navigation, fuel optimization and

weather routing.

Way-point guidance by Improved LOS method

Both next and the previous waypoints are

considered.

Auxiliary points are fixed on both sides of the

waypoints.

A distance threshold is fixed at both sides of the

waypoint

Vehicle calculates the heading correction only

between the threshold points and waypoints.

Heading correction is calculated when AUV is

moving towards a waypoint as well as starts

leaving the same waypoint.

Desired heading= reference heading +

correction heading.

3/17/2016 26

2D path following

WP1

WP3

WP2o

VP

r

Circle of acceptance

d

Methodology for 2D LOS

method

Starting Point: (50,50)

Way Point: (0,200),(300,350),(450,50),(300,200) and (100,150)

Goal Point: (100,0)

3/17/2016 27

Animation 1

Animation 2

../Videos/los4.avi../Videos/LOS5.AVI.avi

Figure : Calculation of heading correction for smooth turn

during course change at waypoints.

Methodology for algorithm development

crd

- desired heading,- actual reference heading- heading correction

d

r

c

mLwhereLThreshold

Lo

5.4,*10

,*2

),( iii yxWP

),( AiAii yxA

idA

idP

Aio),( BiBii yxB

idB

GP

),( )1()1(1 iii yxC

),( iii yxD

),( iii yxC

),( )1()1(1 iii yxD

),( 111 iii yxWP

SP

cr

r

rcVP

idP CidP

DidP

3/17/2016 29

30

Distance between vehicle position and auxiliary point Ai:

2

vAi

2

vAi

2

vAiAi )zz()yy()xx(d

o

CiAiAi

dPd

Normalized difference between the auxiliarypoint Ai and waypoint from the vehicle position:

Distance between vehicle position and current way-point

: 2vi

2

vi

2

viP )zz()yy()xx(d ci

Sign of orientation correction between Ci and WPi :

)(f).dP(f).(

)(f).dP(f).(

AiaCiACscc

AiaCiACscc

[Bakaric et al. (2004)]

Orientation correction between Ci and WPi:

Desired orientation can be calculated as

Map the spherical coordinate to 3D Cartesian coordinate

)sgn(

)sgn(

1ririsci

1ririsci

,ciridi

ciridi

No

Ye

s

No

Ye

s

Ye

s

Ye

sNo

No

DETERMINE: The reference heading

and path angle in spherical coordinatesDEFINE: Distance threshold points at

both sides of the current waypoint

FIND: The sign of correction heading

and path angle to be required for

smooth turn

COMPUTE: The correction heading

and path angle to be required for

smooth turn

CALCULATE: The desired heading

and path angle and map the spherical

coordinates to Cartesian coordinates

Is current vehicle

position =

waypoint1?

Start

Is number of

waypoints is

equal to zero?

DEFINE: Auxiliary points at both

sides of the current waypoint

DEFINE: Distance and Angle factors

by linear curve fitting

Is current vehicle

position = goal

position?

DEFINE: Aiming point distance,

reference angles and instantaneous angles

BRING: Vehicle position to coincide with

the tangential line to current waypoint

Is current vehicle

position= first

distance threshold

point?

Stop

OBTAIN: Vehicle and Goal position,

previous, current and next waypoints in

3D Cartesian coordinates

SELECT: The next waypoint

3/17/2016 32

0

50

100

150

0

20

40

60

80

0

2

4

Surge(x),[m]Sway(y),[m]

Heav

e(z

),[m

]VP

GP

WP

Basic LOS

C1

D1

A1

B1

Improved LOS

0

100

200

300

400

500

0

100

200

300

400

500

0

100

200

300

400

500

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

Basic LOS

Improved LOS

0

100

200

300

0

50

100

150

0

10

20

30

40

50

60

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

Basic LOS

Improved LOS

SP: (10,0,2), WP: (0,0,0) & (75,0,75) GP: (150,0,0)

SP: (10,0,2), WP: (0,0,0) & (75,75,0) GP: (150,0,0)

SP: (20,50,0), WP: (0,0,0),(75,75,75), (150,150,150), (250,250,250) & (300,300,300), GP:

(500,500,500)

Simulation results

SP: (150-20,0), WP: (150,-20,0),(0,0,50), (350,0,50), (200,50,50) (350,100,50) & (10,100,50), GP:

(150,120,0)

0

50

100

150

-5

0

50

20

40

60

80

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

Basic LOS

Improved LOS

0 20 40 60 80 100

0

5

10

15

20

25

30

35

40

45

50

Surge(x),[m]

Sw

ay(y

),[m

]

Basic LOS method

Smoothing spline for basic LOS

Bakaric method

Smoothing spline for bakaric method

Improved LOS

Smoothing spline for Improved d LOS method

Multipoint Potential Field Method for Obstacle Avoidance

Multipoint potential field method for Obstacle

avoidance in 3D space

factorscalingpositiveais

intpogoalandvehiclethearound

intpoqbetweencetandistheisqqdth

iiaa

2

aatt iid

2

1)q(U

planeverticalinangleturningMaximum

planehorizontalinangleturningMaximum

m

m

aq(b)

v

v

r

z y

x

Goal

1q

42q

vA

41q

49q

mhv

mhv

41ad

z

y

x

Goal

h

h

Z

YX Reference

Frame

Body Fixed Frame

(a)

[Ge and Cui

(2000)]

Attractive potential Uatt at qi points:

3/17/2016 SSK,IITM 35

r

r

R

zy

Goal

vA

41q

90

90

hhm

hhm

x

3

7

1

4

2

6

5

1

5

34153od

4113od

to

to

2

a

2

to

obs

ddif,0

ddif,dd

1

d

1

2

1

)q(U

j,i

j,ii

j,i

j,i

factorscalingpositiveais

thresholdluenceinfcetandistheisd,obstacletheon

intpopandintpoqbetweencetandistheis|pq|d

t

th

j

th

ijio

sensorofwidthbeamvertical

sensorofwidthbeamhorizontal

bm

bm

[Ge et al.(2000)]

Repulsive potential Uobs at qi due to the obstacle

point pj:

3/17/2016 36

sA

Calculating radius of

obstacle

Read Input: Sensor data

Generate multiple points on the obstacle

Calculate repulsive potential for the obstacles

Total repulsive potential Urep at qi due to

the obstacle:

Total potential Utot at each point around

the vehicle:

The above steps can be written in a single

equation as:

Minimum potential:

Command the vehicle to the minimum

potential position

Local minima problem

3/17/2016 37

)q(U)q(U

K

1j

obsrep j,ii

R

1m

repatttot )q(U)q(U)q(U m,iii

to

to

2

a

2

toK

1j

R

1m

atttot

ddif,0

ddif,dd

1

d

1

2

1

qU)q(U

m,j,i

m,j,ii

m,j,i

ii

)Umin(U tottotmin

rvCZ and,R Critical (local minima) zone in

horizontal plane is defined by

where,

,and,R vrCZ

Critical zone in vertical plane is defined by:

fauvobsCZ dRRR

Rcz- range of critical zone, Robs- radiusof obstacle, Rauv- radius of AUV, df-distance factor

Divide each zone into 3 sectors

Check for the presence of obstacle in the

sectors

Declare local minima, if exists

Activate strategy to avoid

Yes

No

Yes

No

Stop

Start

GIVEN: Starting and Goal

positions

DISCRETIZE: The region on a

hemisphere of radius r meter

around the vehicle into equiangular

points

DEFINE: Critical Zone (CZ) for

checking local minima

CALCULATE: Attraction potential

(Uatt)

CALCULATE: Repulsion potential

by discretizing obstacle into K2

points (Uobs) for each obstacle

FIND: The complete Repulsion

potential (Urep) for the obstacles

CALCULATE: Total potential

(Utot) for each point around AUV

DETERMINE: Minimum total

potential (Umintot)

COMMAND: Move the vehicle

towards the minimum potential

point (Umintot)

GET: Obstacles data from sensor

DECLARE: local minimum exists and

activate hovering thruster

Does Local

minima Exist?

Is current

position= goal

position

Simulation studies

Configuration: Flat-fish type

Design speed: 4 knots (max)

Buoyancy: Positive

3/17/2016 39

Both static and dynamic obstacles are assumed and they are in spherical shape of various

sizes. The vehicle is reduced in size to a single point, and the obstacles are enlarged by the

vehicles radius.

Two forward looking sonar sensors are considered to sense the obstacle. One sensor with a

range of 40m, horizontal & vertical beam widths of 1200 and 300 is fixed horizontally.

The other sensor with a range of 40m, horizontal & vertical beam widths of 300 and 1200

is fixed vertically.

The velocity of the obstacle is not more than the velocity of the vehicle.

The vehicle is underactuated and cannot move sideways and roll of the vehicle is

neglected.

Length (over all): 4.6m

Width (over all): 1.6m

Height: 0.7m

Propulsion thrusters: 2

Maneuvering thrusters: 3

Depth rating:100m

The positive scaling factors and are taken as 1 and 0.1. The distance influence threshold is

The forward speed (ud) of the vehicle is fixed to a constant value. This value is used to fix

the radial distance (dq ) which is used for defining the next one-step positions.

A sphere of acceptance with radial distance of 2m is considered at goal position.

Assumptions

)RR(2d auvobst

SP =(0,0,0)

GP=(90,90,90)

0

50

100

0

50

1000

20

40

60

80

100

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m] 0 20 40 60 80 100

0

50

100

Sw

ay(y

),[m

]

Surge(x),[m]

0 20 40 60 80 1000

50

100

Hea

ve(

z),[

m]

Sway(y),[m]

0 20 40 60 80 1000

50

100

Surge(x),[m]

Hea

ve(

z),[

m]

3/17/2016 40

0

50

100

0

50

1000

20

40

60

80

100

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

Vel=2m/s

Vel=1.5m/s

Vel=1m/s

050

1000

50

100

0

50

100

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

SP=(10,15,5)

GP=(83,84,78)

Local minimum near SP

0

50

100

0

50

100

0

50

100

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

0

50

100

0

50

100

0

50

100

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

Local minimum due to concave shape obstacle

Simulation results

SPPF MPPF

Distance between obstacle 1 and vehicle (m)

17.1675 21.9355

Distance between obstacle 2 and vehicle (m)

18.1688 22.5124

Significance of MPPF method

3/17/2016 41

0 20 40 60 80 1000

20

40

60

80

100

Surge(x),[m]

Sw

ay(y

),[m

]

SPPF method

MPPF method

0 10 20 30 40 50 60 70 80 90 10040

50

60

70

80

Surge(x),[m]

Sw

ay(y

),[m

]

SPPF

0 10 20 30 40 50 60 70 80 90 10040

50

60

70

80

Surge(x),[m]

Sw

ay(y

),[m

]

MPPF

0 20 40 60 80 100 -1000100

0

5

10

15

Sway(y),[m]Surge(x),[m]

SPPF

Rep

uls

ion

po

ten

tial

0 20 40 60 80 100 -1000100

0

5

10

15

Sway(y),[m]Surge(x),[m]

MPPF

Rep

uls

ion

po

ten

tial

SPPF MPPF

Path

Length

79 67

0 5 10 15 20 25 300

5

10

15

20

25

30

Surge(x),[m]

Sw

ay(y

),[m

]

D.Fu-guang method

Proposed method

Surge(x),[m]

Sw

ay(y

),[m

]

SPPF

5 10 15 20 25 30

5

10

15

20

25

30

Surge(x),[m]

Sw

ay(y

),[m

]

MPPF

5 10 15 20 25 30

5

10

15

20

25

30

0 10 20 30 0102030

0

2

4

Sway(y),[m]

SPPF

Surge(x),[m]

Rep

uls

ion

po

ten

tial

0 10 20 30 051015202530

0

2

4

Sway(y),[m]

MPPF

Surge(x),[m]

Rep

uls

ion

po

ten

tial

0

50

100

0

50

1000

50

100

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

0 20 40 60 80 1000

20

40

60

80

100

Surge(x),[m]

Sw

ay(y

),[m

]

SPPF method

MPPF method

Comparison of MPPF method with other methods

42

Surge(x),[m]

Sw

ay(y

),[m

]

SPPF

5 10 15 20 25 30

5

10

15

20

25

30

Surge(x),[m]

Sw

ay(y

),[m

]

MPPF

5 10 15 20 25 30

5

10

15

20

25

30

0 10 20 30 0102030

0

2

4

Sway(y),[m]

SPPF

Surge(x),[m]

Rep

uls

ion

po

ten

tial

0 10 20 30 051015202530

0

2

4

Sway(y),[m]

MPPF

Surge(x),[m]R

epu

lsio

n p

ote

nti

al

0 10 20 300

5

10

15

20

25

30

Surge(x),[m]

Sw

ay

(y),

[m]

0 5 10 15 20 25 300

5

10

15

20

25

30

Surge(x),[m]

Sw

ay(y

),[m

]

VFF method

MPPF method

-15 -10 -5 0 5 10 15-15

-10

-5

0

5

10

15

Surge(x),[m]

Sw

ay

(y),

[m]

SP

GP

MPPF

APF

NPF

-15 -10 -5 0 5 10 15-15

-10

-5

0

5

10

15

GP

SP

Surge(x),[m]

Sw

ay(y

),[m

]

CS boundary

MPPF method

NPF method

APF method

0 5 10 15 20 25 300

5

10

15

20

25

30

Surge(x),[m]

Sw

ay

(y),

[m]

VFF method

MPPF method

Method Local

minimum

Distance travelled to

reach goal position

APF Yes Couldnt reach

NPF No 37.9873m

MPPF No 33.3371m

Method Chance of

collision

Distance travelled to

reach goal position

APF Yes 22.36m

NPF No 37.9873m

MPPF No 33.3371m

020

4060

80100

120

-50

0

50

-50

0

50

Surge(x),[m]Sway(y),[m]

Hea

ve(

z),[

m]

SP

GP

Desired path

Actual path

obstacle path

In MPPF method the distances to the obstacles are increased by 27.8% compared to the conventional method, thus providing a safe path for AUV. Similarly it was observed that the path length is reduced by 17.9%

User Input

Obstacle avoidance and

way-point guidance

Control Plane

Trajectoryplanner

Sensor data

Controller

ThrusterPath

elements

Desired

states

Tracking

error

Speed

Angle

Actuators

Force/TorqueActual states

+-

The AUV dynamics can be expressed as:

where,M=Mass matrix, C()= Coriolis and Centripetal Matrix, D()= Damping matrixg() = Gravitational and buoyancy effects, = Control inputs

The above equation can be written as, )))g()D()C(((M1

)(g)(D)(CMBody

frame

Inertial

frame

x,

z,

y,

p,pK,

u,uX,

q,qM,

v,v,Y

w,w,Z r,r,N

AUV Frame assignment

3/17/2016 44

Robot Dynamic

Model

Integrating high level control with AUV dynamics

Simulation results

3/17/2016 450

50

100

150

200

2500

100

200

-100

0

100

Surge(x),[m]Sway(y),[m]

SP

WP

GP

Desired

Actual

0 50 100 1500

50

100

150

Surge(x),[m]S

way

(x),

[m]

SP

GP

WP

Desired path

Actual path

0 20 40 60 80 100 1200

100

200

Time(t),[s]

Su

rge(

x),

[m]

0 20 40 60 80 100 1200

50

100

Time(t),[s]

Sw

ay(y

),[m

]

0 20 40 60 80 100 120-50

0

50

Time(t),[s]

Yaw

(),

[deg

]

Desired

Actual

0 50 100 150

-50

0

50

-50

0

50

Sway(y),[m]

Surge,(x),[m]

Hea

ve(

z),[

m]

SP

GP

Desired Path

Actual Path

0

50

100

150

-100

-50

0

50

100-50

0

50

Surge(x),[m]Sway(x),[m]

Hea

ve(

z),[

m]

SP

GP

WP

Desired path

Actual path

Avoiding local minima Combined result

Obstacle

avoidance

3D way-point guidance

2D way-point guidance

Hardware-In-Loop (HIL) Experiments

dSPACE DS1104 R&D Controller

GUI

(ControlDesk)

Actuator Unit

(Thrusters and

Control planes)

Interface

Unit

(Connector

panel)

Sens

or

User

Input

Sensor

data

Measured pitch angle

Measured yaw angle

Measured

speed

Commanded pitch

angle

Commanded yaw

angle

Commanded

speed

Obstacle Avoidan

ce

Trajectory

Planner

AUV Controll

er

AUV Dynami

cs

Desired states

Path element

s

Commanded speed & angle

ForceSP & GP

dSPACE DS1104 R&D

controller

Actualstates Actuato

r Dynami

cs

Measured speed & angle

3/17/2016 46

Hardware-In-Loop (HIL) simulation is a technique that is used in the

development and test of complex real-time systems.

It replaces the emulated hardware under test or control logic in the

simulation model, with real hardware that interacts with the computer

models.

It provides the realism of the simulation and provides access to

hardware features currently not available in software-only simulation

models.HIL architecture

3/17/2016 47

Thruster

Rudder motorConnector panel

Driver boards

Stern motor

RTI model

HIL setup

ControlDesk Layout

3/17/2016SSK,IITM

48

HIL test using obstacle sensor

3DHILcase-1.avi3DHILcase-1.avieditedwosen.avieditedwosen.avi

3/17/2016SSK,IITM

49

HIL simulation results

0 20 40 60 80 100 120-20

0

20

Surge(x),[m]

Sw

ay(y

),[m

]

-10 -5 0 5 10 15 20-20

0

20

Sway(y),[m]

Hea

ve(

z),[

m]

Desired

Actual

0 20 40 60 80 100 120-20

0

20

Surge(x),[m]

Hea

ve(

z),[

m]

0 50 100 150-5

0

5

Time(t),[s]

xe,[

m]

0 50 100 150-5

0

5

Time(t),[s]

ye,[

m]

0 50 100 150-5

0

5

Time(t),[s]

z e,[

m]

0 50 100 150-20

0

20

Time(t),[s]

e,[

deg

]

0 50 100 150-20

0

20

Time(t),[s]

e,[

deg

]

Comparison between MIL and HIL

Error MIL HIL

xe (m) 0.6018 0.9573

ye (m) 0.3237 0.3528

ze (m) 0.3678 0.2187

Pitche (deg) 2.2558 2.4734

Yawe (deg) 1.4646 1.8525

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

-1

0

1x 10

4

Xcorr

ela

tion

Lags

Surge error

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

-1000

0

1000

Xcorr

ela

tion

Lags

Sway error

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

-1000

0

1000

Xcorr

ela

tion

Lags

Heave error

Error No. of samples

delayed

xe (m) 34

ye (m) 84

ze (m) 89

020 40 60 80 100

-50

0

50

-50

0

50

Sway(y),[m]Surge(x),[m]

Hea

ve(

z),[

m]

SP

GP

Desired Path

Actual Path

Video

../Videos/Case1A_HILdyn3Donline.avi

Summary

Underwater Robots are finding wide applications in Oil and

gas Industry, Oceanographic explorations and defence

areas.

AUVs are the promising robots for the future

Guidance, Navigation, Control, and power source are the

present challenges in AUV development

References1. Fossen, T., 1994. Guidance and Control of Underwater Vehicles. Wiley,

New York.2. www.rov.org3. D. Richard Blidberg, The Development of Autonomous Underwater

Vehicles (AUV); A Brief Summary, Autonomous Undersea SystemsInstitute technical report, Lee New Hampshire, USA.

http://www.rov.org/

Thank you