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Abstract

Cindy (Hsin-Yi) Yeh, Shawna Thomas, David Eppstein, Nancy M. Amato, "UOBPRM: A Uniformly Distributed Obstacle-Based PRM," In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 2655-2662, Vilamoura, Algarve, Portugal, Oct 2012.
Proceedings(ps, pdf, ppt, abstract)

This paper presents a new sampling method for motion planning that can generate configurations more uniformly distributed on C-obstacle surfaces than prior approaches. Here, roadmap nodes are generated from the intersections between C-obstacles and a set of uniformly distributed fixed-length segments in C-space. The results show that this new sampling method yields samples that are more uniformly distributed than previous obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling. UOBPRM is shown to have nodes more uniformly distributed near C-obstacle surfaces and also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages.