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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for a class of pursuit-evasion games. In Springer Tracts in Advanced Robotics (Vol. 68, pp. 71–87). https://doi.org/10.1007/978-3-642-17452-0_5
Mendeley helps you to discover research relevant for your work.