NUS CS5247

Real-Time Combinatorial Tracking of a Target Moving

Unpredictably Among Obstacles

Hector H. Gonzales-Banos, Cheng-Yu Hector H. Gonzales-Banos, Cheng-Yu Lee and Jean-Claude LatombeLee and Jean-Claude Latombe

Presented By: Amit Jain

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Outline

Introduction

Problem Statement

Approach

Evaluation

Future Work

Conclusion

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Introduction

Consider a scenario involving... Team of autonomous robots Performing independent tasks At a remote location

Requirements for ideal debugger: Dynamic Environment Unmapped Territory

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Problem Statement

Maximize Escape Time Unknown Environment Nondeterministically uncertain target

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Approach

Acts = {}

For each EscapePath ep ∈ SEP Acts = Acts ∪ maximizeEscapeTime(ep)

BestAction = combine(Acts)

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Approach: maximizeEscapeTimeBasic Approach

Maximize escape time

Pros Directly addresses the problem

Cons Computationally expensive to calculate

Alternatives/Proxies Shortest Distance to Escape as proxy Risk Function as proxy

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Approach: SDE

Maximize Shortest Distance to EscapePros

Correct in holonomic case Related in non-holonomic case

Cons Non-linear Relationship

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Approach: Risk Function

Minimize risk

Pros: Polynomial relationship with SDE Considers pursuer's position

risk c r o2 1h

m 2

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Approach

Acts = {}

For each EscapePath ep ∈ SEP Acts = Acts ∪ maximizeEscapeTime(ep)

BestAction = combine(Acts)

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Approach: Combining Actions

Naive Approach: Average over actions

Pros:

Easy to Implement

Intuitive

Cons:

Over Representation of Escape Paths

Alternative Approach: Average over EPTs

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Evaluation

Influence of escape paths on risk

Transient response of target tracker

Tracking over long time

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Future Work

Extending to 3-D

Learning the map

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Conclusion

Reactive target follower

Novelties: Linear time calculation of escape path

New proxy for escape time

Avoids localization issues

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