MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the space

Steven A. Wilmarth, Nancy M. Amato, Peter F. Stiller

Research output: Contribution to journalConference article

Abstract

Probabilistic roadmap planning methods have been shown to perform well in a number of practical situations, but their performance degrades when paths are required to pass through narrow passages in the free space. We propose a new method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. We give algorithms that perform this retraction while avoiding explicit computation of the medial axis, and we show that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it. Theoretical and experimental results are given to show that this improves performance on problems requiring traversal of narrow passages.

Original languageEnglish (US)
Pages (from-to)1024-1031
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume2
StatePublished - Jan 1 1999
Externally publishedYes
EventProceedings of the 1999 IEEE International Conference on Robotics and Automation, ICRA99 - Detroit, MI, USA
Duration: May 10 1999May 15 1999

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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