Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games
Author(s)Karaman, Sertac; Frazzoli, Emilio
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Algorithmic Foundations of Robotics IX
Karaman, Sertac, and Emilio Frazzoli. Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games. Springer-Verlag, 2011.
Author's final manuscript