Gaussian sampling for probabilistic roadmap planners

Abstract

Probabilistic roadmap planners (PRMs) have become a popular technique for motion
\nplanning that has shown great potential. A critical aspect of a PRM is the probabilistic
\nstrategy used to sample the free configuration space. In this paper we present a new,
\nsimple sampling strategy, which we call the Gaussian sampler, that gives a much better
\ncoverage of the difficult parts of the free configuration space compared with the standard
\nuniform sampler. This results in much smaller roadmaps that can be computed faster.
\nThe approach uses only elementary operations which makes it suitable for many different
\nplanning problems. Experiments indicate that the technique is indeed very efficient in
\npractice.

Cite this paper

@article{boor2001gaussian,
  title = {{Gaussian sampling for probabilistic roadmap planners}},
  author = {Boor, V. and Overmars, M.H. and van der Stappen, A. Frank},
  journal = {Utrecht University Repository (Utrecht University)},
  year = {2001}
}

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