UOBPRM: A uniformly distributed obstacle-based PRM

Hsin-Yi Yeh, Shawna Thomas, David Eppstein, Nancy M. Amato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

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. © 2012 IEEE.
Original languageEnglish (US)
Title of host publication2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2655-2662
Number of pages8
ISBN (Print)9781467317368
DOIs
StatePublished - Oct 2012
Externally publishedYes

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