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Adaptive Workspace Biasing for Sampling Based Planners

Matthew Zucker, James Kuffner and J. Andrew (Drew) Bagnell
Conference Paper, Proceedings of Proc. IEEE Int'l Conf. on Robotics and Automation, May, 2008

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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. Experimental results show our algorithm to be effective across a variety of robotic platforms and high-dimensional configuration spaces.

author = {Matthew Zucker and James Kuffner and J. Andrew (Drew) Bagnell},
title = {Adaptive Workspace Biasing for Sampling Based Planners},
booktitle = {Proceedings of Proc. IEEE Int'l Conf. on Robotics and Automation},
year = {2008},
month = {May},
keywords = {Motion and Path Planning, Learning and Adaptive Systems, Nonholonomic Motion Planning},
} 2017-09-13T10:41:46-04:00