MARRT: Medial Axis biased rapidly-exploring random trees

Jory Denny, Evan Greco, Shawna Thomas, Nancy M. Amato

Research output: Contribution to journalConference articlepeer-review

Abstract

Motion planning is a difficult and widely studied problem in robotics. Current research aims not only to find feasible paths, but to ensure paths have certain properties, e.g., shortest or safest paths. This is difficult for current state-of-the-art sampling-based techniques as they typically focus on simply finding any path. Despite this difficulty, sampling-based techniques have shown great success in planning for a wide range of applications. Among such planners, Rapidly-Exploring Random Trees (RRTs) search the planning space by biasing exploration toward unexplored regions. This paper introduces a novel RRT variant, Medial Axis RRT (MARRT), which biases tree exploration to the medial axis of free space by pushing all configurations from expansion steps towards the medial axis. We prove that this biasing increases the tree's clearance from obstacles. Improving obstacle clearance is useful where path safety is important, e.g., path planning for robots performing tasks in close proximity to the elderly. Finally, we experimentally analyze MARRT, emphasizing its ability to effectively map difficult passages while increasing obstacle clearance, and compare it to contemporary RRT techniques.

Original languageEnglish (US)
Article number6906594
Pages (from-to)90-97
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
DOIs
StatePublished - Sep 22 2014
Externally publishedYes
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

ASJC Scopus subject areas

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

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