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ICRA
2008
IEEE

Adaptive workspace biasing for sampling-based planners

9 years 4 months ago
Adaptive workspace biasing for sampling-based planners
Abstract— The widespread success of sampling-based planning algorithms stems from their ability to rapidly discover the connectivity of a configuration space. Past research has found that non-uniform sampling in the configuration space can significantly outperform uniform sampling; one important strategy is to bias the sampling distribution based on features present in the underlying workspace. In this paper, we unite several previous approaches to workspace biasing into a general framework for automatically discovering useful sampling distributions. We present a novel algorithm, based on the REINFORCE family of stochastic policy gradient algorithms, which automatically discovers a locally-optimal weighting of workspace features to produce a distribution which performs well for a given class of sampling-based motion planning queries. We present as well a novel set of workspace features that our adaptive algorithm can leverage for improved configuration space sampling. Experimenta...
Matthew Zucker, James Kuffner, James A. Bagnell
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICRA
Authors Matthew Zucker, James Kuffner, James A. Bagnell
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