NUS CS5247 Real-Time Combinatorial Tracking of a Target Moving Unpredictably Among Obstacles Hector H. Gonzales-Banos, Cheng-Yu Lee and Jean-Claude Latombe Presented By: Amit Jain

NUS CS52472 Outline Introduction Problem Statement Approach Evaluation Future Work Conclusion

NUS CS52473 Introduction Consider a scenario involving... Team of autonomous robots Performing independent tasks At a remote location Requirements for ideal debugger: Dynamic Environment Unmapped Territory

NUS CS52474 Problem Statement Maximize Escape Time Unknown Environment Nondeterministically uncertain target

NUS CS52475 Approach Acts = {} For each EscapePath ep SEP Acts = Acts maximizeEscapeTime(ep) BestAction = combine(Acts)

NUS CS52476 Approach: maximizeEscapeTime Basic 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

NUS CS52477 Approach: SDE Maximize Shortest Distance to Escape Pros Correct in holonomic case Related in non-holonomic case Cons Non-linear Relationship

NUS CS52478 Approach: Risk Function Minimize risk Pros: Polynomial relationship with SDE Considers pursuer's position

NUS CS52479 Approach Acts = {} For each EscapePath ep SEP Acts = Acts maximizeEscapeTime(ep) BestAction = combine(Acts)

NUS CS Approach: Combining Actions Naive Approach: Average over actions Pros: Easy to Implement Intuitive Cons: Over Representation of Escape Paths Alternative Approach: Average over EPTs

NUS CS Evaluation Influence of escape paths on risk Transient response of target tracker Tracking over long time

NUS CS Future Work Extending to 3-D Learning the map

NUS CS Conclusion Reactive target follower Novelties: Linear time calculation of escape path New proxy for escape time Avoids localization issues

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