Asymptotically Optimal Sampling-based Planners

Kostas E. Bekris; Rahul Shome

doi:10.1007/978-3-642-41610-1_172-1

Asymptotically Optimal Sampling-based Planners

Robotics 2022-01-07 v3

Authors: Kostas E. Bekris , Rahul Shome

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Abstract

An asymptotically optimal sampling-based planner employs sampling to solve robot motion planning problems and returns paths with a cost that converges to the optimal solution cost, as the number of samples approaches infinity. This comprehensive article covers the theoretical characteristics of asymptotic optimality of motion planning algorithms, and traces its origins, analysis models, practical performance, extensions, and applications.

Keywords

motion planning path planning randomized algorithm

Cite

@article{arxiv.1911.04044,
  title  = {Asymptotically Optimal Sampling-based Planners},
  author = {Kostas E. Bekris and Rahul Shome},
  journal= {arXiv preprint arXiv:1911.04044},
  year   = {2022}
}

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