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

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.

Original languageEnglish (US)
Title of host publication2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
Pages2655-2662
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes
Event25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve, Portugal
Duration: Oct 7 2012Oct 12 2012

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
CountryPortugal
CityVilamoura, Algarve
Period10/7/1210/12/12

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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