UMAPRM: Uniformly sampling the medial axis

Hsin-Yi Cindy Yeh, Jory Denny, Aaron Lindsey, Shawna Thomas, Nancy M. Amato

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

10 Scopus citations

Abstract

© 2014 IEEE. Maintaining clearance, or distance from obstacles, is a vital component of successful motion planning algorithms. Maintaining high clearance often creates safer paths for robots. Contemporary sampling-based planning algorithms That utilize The medial axis, or The set of all points equidistant To Two or more obstacles, produce higher clearance paths. However, They are biased heavily Toward certain portions of The medial axis, sometimes ignoring parts critical To planning, e.g., specific Types of narrow passages. We introduce Uniform Medial Axis Probabilistic RoadMap (UMAPRM), a novel planning variant That generates samples uniformly on The medial axis of The free portion of Cspace. We Theoretically analyze The distribution generated by UMAPRM and show its uniformity. Our results show That UMAPRM's distribution of samples along The medial axis is not only uniform but also preferable To other medial axis samplers in certain planning problems. We demonstrate That UMAPRM has negligible computational overhead over other sampling Techniques and can solve problems The others could not, e.g., a bug Trap. Finally, we demonstrate UMAPRM successfully generates higher clearance paths in The examples.
Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5798-5803
Number of pages6
ISBN (Print)9781479936854
DOIs
StatePublished - May 2014
Externally publishedYes

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