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Cal Poly PomonaCal Poly Pomona

Robot NavigationRobot Navigation

Salomón Oldak, Ph.D.Salomón Oldak, Ph.D.Electrical and Computer Electrical and Computer

EngineeringEngineering2/8/062/8/06

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DARPA Grand DARPA Grand ChallengeChallenge Congress mandate to increase the Congress mandate to increase the

use of ground unmanned vehicles. use of ground unmanned vehicles. Congressional Goal: 1/3 of armed Congressional Goal: 1/3 of armed

forces combat vehicles unmanned forces combat vehicles unmanned by 2015.by 2015.

Vehicles must be fully autonomousVehicles must be fully autonomous Nearly 250 miles on and off road in Nearly 250 miles on and off road in

10 hours.10 hours.

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First DARPA Challenge First DARPA Challenge 20042004 First Challenge took place on 3/14/04First Challenge took place on 3/14/04 Between Los Angeles and Las VegasBetween Los Angeles and Las Vegas $1 Million Prize$1 Million Prize 25 Teams Participated25 Teams Participated

– CalTech, University of Florida, University CalTech, University of Florida, University of Alaska, Virginia Tech and othersof Alaska, Virginia Tech and others

– Palos Verdes High School Palos Verdes High School

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Ghostrider(Blue Team, Ghostrider(Blue Team, Berkeley)Berkeley)

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Blue Team MovieBlue Team Movie

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ObstaclesObstacles

Paved RoadsPaved Roads OverpassesOverpasses Straight-Winding RoadsStraight-Winding Roads Sand, RockSand, Rock UnderpassesUnderpasses WaterWater Natural ObstructionNatural Obstruction

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1st Challenge Results1st Challenge Results

Failure:

No Team

Completed Task.

Max Distance 7.4Mi

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22ndnd DARPA Grand DARPA Grand Challenge (10/8/2005)Challenge (10/8/2005)

Success! 3 Robots Completed Success! 3 Robots Completed task!task!

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The Winner: StanleyThe Winner: Stanley

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Stanford University Stanford University (Sebastian Thrun)(Sebastian Thrun)

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Sensors UsedSensors Used

GPS AntennaGPS Antenna Laser Range Finder (Lidar) (30m)Laser Range Finder (Lidar) (30m) Video Camera (80m)Video Camera (80m) Odometry (Photo Sensor on Odometry (Photo Sensor on

Wheel)Wheel)

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AlgorithmAlgorithm

Problems: Vibration would trick Problems: Vibration would trick sensors to “imagine” ghost sensors to “imagine” ghost obstacles, The vehicle thought it’s obstacles, The vehicle thought it’s own shadow is an obstacle.own shadow is an obstacle.

Solution: Teach the car. Assess Solution: Teach the car. Assess weights to pixels as a human weights to pixels as a human driver operates the car.driver operates the car.

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FutureFuture

43000 people die in traffic 43000 people die in traffic accidents/year in the USaccidents/year in the US

Robot driven cars will reduce # of Robot driven cars will reduce # of fatalitiesfatalities

Accidents can be avoidedAccidents can be avoided Liability?Liability?

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Spirit and OpportunitySpirit and Opportunity

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NavigationNavigation

Rover’s are mostly teleoperatedRover’s are mostly teleoperated 2 stereo hazard avoidance 2 stereo hazard avoidance

cameras front and backcameras front and back 1 Stereo Navigation camera on 1 Stereo Navigation camera on

mastmast Rovers moves 0.5m at max speed Rovers moves 0.5m at max speed

of 34m/h = 0.02MPHof 34m/h = 0.02MPH

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Robot ParadigmsRobot Paradigms

Paradigm: Paradigm: ““A "view" of how things work in the A "view" of how things work in the

world.”world.” “…“…a set of rules and regulations…”a set of rules and regulations…” Paradigm Primitives:Paradigm Primitives:

SENSE, PLAN, ACTSENSE, PLAN, ACT

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ParadigmsParadigms

Hierarchical Hierarchical

1967-19901967-1990 ReactiveReactive

1988-19921988-1992 Hybrid Deliberative/ReactiveHybrid Deliberative/Reactive

1990-1990-

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Hierarchical ParadigmHierarchical Paradigm

The robot operates in a top-down fashion, The robot operates in a top-down fashion, heavy on planning. heavy on planning.

The robot senses the world, plans the next The robot senses the world, plans the next action, acts; at each step the robot action, acts; at each step the robot explicitly plans the next move. explicitly plans the next move.

All the sensing data tends to be gathered All the sensing data tends to be gathered into one global world model. into one global world model.

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Shakey (SRI)Shakey (SRI)

First AI Robot(1967-70)

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AssumptionsAssumptions

Close World: World Model Close World: World Model Contains everything the Robot Contains everything the Robot needs to knowneeds to know

Frame Problem: The real-world Frame Problem: The real-world situation is computationally situation is computationally feasible.feasible.

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ProblemProblem

Robots designed under Hierarchical Robots designed under Hierarchical Paradigm were VERY slow.Paradigm were VERY slow.

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Reactive ParadigmReactive Paradigm

Sense-act type of organization. Sense-act type of organization. The robot has multiple instances of Sense-Act The robot has multiple instances of Sense-Act

couplings. couplings. These couplings are concurrent processes, These couplings are concurrent processes,

called behaviours, which take the local sensing called behaviours, which take the local sensing data and compute the best action to take data and compute the best action to take independently of what the other processes are independently of what the other processes are doing. doing.

The robot will do a combination of behavioursThe robot will do a combination of behaviours. .

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Biological Foundation Biological Foundation of Reactive Paradigmof Reactive Paradigm Reactive Paradigm is based on Reactive Paradigm is based on

observations of ethologists (study observations of ethologists (study of animal behavior) and cognitive of animal behavior) and cognitive psychology (how humans think psychology (how humans think and represent knowledge)and represent knowledge)

Biology provides existence proofs.Biology provides existence proofs.

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Behavior Definition Behavior Definition (graphical)(graphical)

BEHAVIOR

SensoryInput

Patternof MotorActions

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Arctic Arctic TernsTerns

Arctic terns live in Arctic (black, white, gray Arctic terns live in Arctic (black, white, gray environment, some grass) but adults have a red spot environment, some grass) but adults have a red spot on beakon beak

When hungry, baby pecks at parent’s beak, who When hungry, baby pecks at parent’s beak, who regurgitates food for baby to eatregurgitates food for baby to eat

How does it know its parent?How does it know its parent?– It doesn’t, it just goes for the largest red spot in its field It doesn’t, it just goes for the largest red spot in its field

of view (e.g., ethology grad student with construction of view (e.g., ethology grad student with construction paper)paper)

– Only red thing should be an adult ternOnly red thing should be an adult tern

– Closer = large redCloser = large red

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Types of BehaviorsTypes of Behaviors

ReflexiveReflexive – stimulus-response, often abbreviated S-R (like knee stimulus-response, often abbreviated S-R (like knee

tapped). “Hardwired”tapped). “Hardwired” ReactiveReactive

– learned or “muscle memory”. (Riding a bike, skiing, learned or “muscle memory”. (Riding a bike, skiing, etc.)etc.)

ConsciousConscious – deliberately stringing together (Building a Robot)deliberately stringing together (Building a Robot)

WARNING Overloaded terms:Roboticists often use “reactive behavior” to mean purely reflexive,

And refer to reactive behaviors as “skills”

WARNING Overloaded terms:Roboticists often use “reactive behavior” to mean purely reflexive,

And refer to reactive behaviors as “skills”

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Example: Cockroach Example: Cockroach HideHide light goes on, the cockroach turns and runslight goes on, the cockroach turns and runs

when it gets to a wall, it follows itwhen it gets to a wall, it follows it

when it finds a hiding place (thigmotrophic), when it finds a hiding place (thigmotrophic), goes in and faces outwardgoes in and faces outward

waits until not scared, then comes outwaits until not scared, then comes out

even if the lights are turned back off earliereven if the lights are turned back off earlier

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Behaviors are Behaviors are ConcurrentConcurrent

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What happens when What happens when there’s a conflict from there’s a conflict from concurrent behaviors?concurrent behaviors?

EquilbriumEquilbrium– Feeding squirrels-feed, Feeding squirrels-feed,

flee: hesitate in-flee: hesitate in-betweenbetween

DominanceDominance– Sleepy, hungry: either Sleepy, hungry: either

sleep or eatsleep or eat CancellationCancellation

– Sticklebacks defend, Sticklebacks defend, attack: build a nest attack: build a nest

?

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Reactive RobotsReactive Robots

Most apps are programmed with this paradigmMost apps are programmed with this paradigm Biologically based:Biologically based:

– Behaviors (independent processes), released by perceptual or Behaviors (independent processes), released by perceptual or internal events (state)internal events (state)

– No world models or long term memoryNo world models or long term memory– Highly modular, genericHighly modular, generic– Overall behavior Overall behavior emergesemerges

SENSE ACT

RELEASERbehavior

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Example : My Real Example : My Real BabyBaby Behaviors?Behaviors? Touch-> AwakeTouch-> Awake Upside down & Awake-> CryUpside down & Awake-> Cry Awake & Hungry -> CryAwake & Hungry -> Cry Awake & Lonely -> CryAwake & Lonely -> Cry

Note can get crying from multiple behaviorsNote can get crying from multiple behaviors Note internal state (countdown timer on Note internal state (countdown timer on

Lonely)Lonely)

www.irobot.com

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Subsumption Architecture:Subsumption Architecture:Rodney BrooksRodney Brooks

From http://www.spe.sony.com/classics/fastcheap/index.html

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RunawayRunaway

HALT

COLLIDE

PS MS

RUN AWAYPS MS

runaway 0

wander 1 follow-corridor 2

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Example Perception: Polar Example Perception: Polar PlotPlot

Plot is Plot is ego-centricego-centric Plot is distributed (available to whatever wants to use Plot is distributed (available to whatever wants to use

it)it) Although it is a representation in the sense of being a Although it is a representation in the sense of being a

data structure, there is no memory (contains latest data structure, there is no memory (contains latest information) and no reasoning (2-3 means a “wall”)information) and no reasoning (2-3 means a “wall”)

if sensing is ego-centric, canoften eliminate need for memory, representation

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Potential Fields Potential Fields (Example: Navigation)(Example: Navigation)

7

6

5

4

3

2

1

0

0 1 2 3 4 5 6 7 8 9101112131415

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0

0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2

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Wavefront AlgorithmWavefront Algorithm

7

6

5

4

3

2

1

0

0 1 2 3 4 5 6 7 8 910

11

12

13

14

15

18 17 16 15 14 13 12 11 10 9 9 9 9 9 9 9

17 17 16 15 14 13 12 11 10 9 8 8 8 8 8 8

17 16 16 15 14 13 12 11 10 9 8 7 7 7 7 7

17 16 15 15 1 1 1 1 1 1 1 1 6 6 6 6

17 16 15 14 1 1 1 1 1 1 1 1 5 5 5 5

17 16 15 14 13 12 11 10 9 8 7 6 5 4 4 4

17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 3

17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2

(0,7) -> (1,7) -> (2,7) -> (3,7) -> (4,7) -> (5,7) -> (6,7) -> (7,7) -> (8,7) -> (9,7) -> (10,7) -> (10,6) -> (11,6) -> (11,5) -> (12,5) -> (12,4) -> (12,3) -> (13,3) -> (13,2) -> (14,2) -> (14,1) -> (15,1) -> (15,0)

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Hybrid Hybrid Deliberate/Reactive Deliberate/Reactive ParadigmParadigm The robot first plans (deliberates) how to best The robot first plans (deliberates) how to best

decompose a task into subtasks (also called “mission decompose a task into subtasks (also called “mission planning”) and then what are the suitable behaviours planning”) and then what are the suitable behaviours to accomplish each subtask. to accomplish each subtask.

Then the behaviours starts executing as per the Then the behaviours starts executing as per the Reactive Paradigm. Reactive Paradigm.

Sensing organization is also a mixture of Hierarchical Sensing organization is also a mixture of Hierarchical and Reactive styles; sensor data gets routed to each and Reactive styles; sensor data gets routed to each behaviour the needs that sensor, but is also available behaviour the needs that sensor, but is also available to the planner for construction of a task-oriented to the planner for construction of a task-oriented global world model. global world model.

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Deliberation v.s. Deliberation v.s. PlanningPlanning Besides “planning” robot has to Besides “planning” robot has to

perform other tasks such as: map perform other tasks such as: map making, performance monitoring, making, performance monitoring, learning, etc.learning, etc.

All these tasks together with All these tasks together with planning are known as: planning are known as: DELIBERATIONDELIBERATION

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Sensing OrganizationSensing Organization

BEHAVIOR

BEHAVIOR

BEHAVIOR

SENSOR 1

SENSOR 2

ACTUATORS

WORLD MAP/KNOWLEDGE REP

SENSOR 3

virtual sensor

Deliberative functions*Can “eavesdrop”*Can have their ownSensors*Have output which Looks like a sensorOutput to a behavior(virtual sensor)

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Hybrid BehaviorsHybrid Behaviors

Behaviors are extended toBehaviors are extended to ReflexiveReflexive InnateInnate LearnedLearned

(Just like in ethology)(Just like in ethology)

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Architectures: Architectures: Common FunctionalityCommon Functionality

Mission planner Mission planner

CartographerCartographer

Sequencer agentSequencer agent

Behavioral managerBehavioral manager

Performance monitor/problem solving Performance monitor/problem solving agent (fairly rare)agent (fairly rare)

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Several Hybrid Several Hybrid Approaches Have Been Approaches Have Been DevelopedDeveloped

AuRA (Arkin 1986)AuRA (Arkin 1986) Atlantis (Gat 1991)Atlantis (Gat 1991) Sensor-Fusion Effects (SFX) (Murphy 1996)Sensor-Fusion Effects (SFX) (Murphy 1996) 3-Tiered (3T) (JPL1990s)3-Tiered (3T) (JPL1990s) Saphira (Konolige 1998)Saphira (Konolige 1998) Tack Control Architecture (Simmons 1997)Tack Control Architecture (Simmons 1997) Planner-Reactor (Lyons and Hendriks 1992)Planner-Reactor (Lyons and Hendriks 1992) Procedural Reasoning System (PRS) (Georgeff and Lansky 1987)Procedural Reasoning System (PRS) (Georgeff and Lansky 1987) SSS (Connell 1992)SSS (Connell 1992) Multi-Valued Logic (Saffiotti 1995)Multi-Valued Logic (Saffiotti 1995) SOMASS Hybrid Assembly System (Malcom and Smithers 1990)SOMASS Hybrid Assembly System (Malcom and Smithers 1990) Agent Architecture (Hayes-Roth 1993)Agent Architecture (Hayes-Roth 1993) Etc., Etc.Etc., Etc.

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Georgia Tech Georgia Tech TMR(Tactical Mobile TMR(Tactical Mobile Robot) RobotsRobot) Robots

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NAVIGATIONNAVIGATION

Topological Navigation – Topological Navigation – Qualitative NavigationQualitative Navigation

Metric Navigation – Quantitative Metric Navigation – Quantitative NavigationNavigation

Navigation Algorithm usually is Navigation Algorithm usually is part of Deliberative part of Hybrid part of Deliberative part of Hybrid Architecture.Architecture.

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Qualitative Navigation Qualitative Navigation uses Landmarksuses Landmarks

floor plan

relational graph

Relational Methods

Nodes: landmarks, gateways,goal locations

Edges: navigable path

Gateway is an opportunityto change path heading

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Quantitative Quantitative NavigationNavigation

Want to get from one point to Want to get from one point to another with an optimization another with an optimization criteria:criteria:

Minimize TimeMinimize Time Minimize EnergyMinimize Energy Minimize DistanceMinimize Distance Etc.Etc.

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Space Representation: Space Representation: Voronoi Graphs Voronoi Graphs Imagine a fire starting at the boundaries, creating a Imagine a fire starting at the boundaries, creating a

line where they intersect, intersections of lines are line where they intersect, intersections of lines are nodesnodes

Result is a relational graphResult is a relational graph

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Space Representation: Space Representation: Rectangular GraphRectangular Graph

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AA** AlgorithmAlgorithm

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DiscussionDiscussion

Questions ?

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ReferencesReferences

Murphy R.R.; “Introduction to AI Murphy R.R.; “Introduction to AI Robotics, MIT Press, 2000Robotics, MIT Press, 2000

http://www.policyalmanac.org/gahttp://www.policyalmanac.org/games/aStarTutorial.htmmes/aStarTutorial.htm (Accessed 2/6/06) (Accessed 2/6/06)

http://robots.stanford.edu/http://robots.stanford.edu/ (Accessed (2/6/06)(Accessed (2/6/06)

http://www.ghostriderrobot.com/ihttp://www.ghostriderrobot.com/index.php?id=robotndex.php?id=robot (Accessed 2/6/06) (Accessed 2/6/06)