Adaptive Workspace Biasing for Sampling Based Planners
journal contributionposted on 01.01.2008 by Matt Zucker, James Kuffner, J. Andrew Bagnell
Any type of content formally published in an academic journal, usually following a peer-review process.
The widespread success of sampling-based plan- ning 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 gen- eral framework for automatically discovering useful sampling distributions. We present a novel algorithm, based on the RE I NF ORCE 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. Experimental results show our algorithm to be effective across a variety of robotic platforms and high- dimensional conﬁguration spaces.