Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games
Name
Frazzoli_Incremental sampling-based.pdf
Size
2.04 MB
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Adobe PDF
Checksum (MD5)
75a65a1f63cf767ad78aecda5c155ae3
Author(s)
Karaman, Sertac
Frazzoli, Emilio
Date Issued
2011
Journal
Algorithmic Foundations of Robotics IX
Publisher
Springer-Verlag
Citation
Karaman, Sertac, and Emilio Frazzoli. Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games. Springer-Verlag, 2011.
Version
Author's final manuscript
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.
MIT Department
Massachusetts 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
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/978-3-642-17452-0_5
