Gaussian sampling for probabilistic roadmap planners
Year: 2001
Type: article
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.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2108886409