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Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games

  • Sertac Karaman
  • Emilio Frazzoli
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 68)

Abstract

Pursuit-evasion games have been used for modeling various forms of conflict arising between two agents modeled as dynamical systems. Although analytical solutions of some simple pursuit-evasion games are known, most interesting instances can only be solved using numerical methods requiring significant offline computation. In this paper, a novel incremental sampling-based algorithm is presented to compute optimal open-loop solutions for the evader, assuming worst-case behavior for the pursuer. It is shown that the algorithm has probabilistic completeness and soundness guarantees. As opposed to many other numerical methods tailored to solve pursuit-evasion games, incremental sampling-based algorithms offer anytime properties, which allow their real-time implementations in online settings.

Keywords

Motion Planning Differential Game Goal Region Asymptotic Optimality Motion Planning Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sertac Karaman
    • 1
  • Emilio Frazzoli
    • 2
  1. 1.Laboratory for Information and Decision Systems, Department of Electrical Engineering and Computer ScienceMassachusetts Institute of Technology 
  2. 2.Laboratory for Information and Decision Systems, Department of Aeronautics and AstronauticsMassachusetts Institute of Technology 

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