Dynamic-domain RRTs: Efficient exploration by controlling the sampling domain

Anna Yershova, Léonard Jaillet, Thierry Siméon, Steven M. LaValle

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

Abstract

Sampling-based planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidly-exploring Random Trees (RRTs) have generated highly successful single-query planners. Even though RRTs work well on many problems, they have weaknesses which cause them to explore slowly when the sampling domain is not well adapted to the problem. In this paper we characterize these issues and propose a general framework for minimizing their effect. We develop and implement a simple new planner which shows significant improvement over existing RRT-based planners. In the worst cases, the performance appears to be only slightly worse in comparison to the original RRT, and for many problems it performs orders of magnitude better.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE International Conference on Robotics and Automation
Pages3856-3861
Number of pages6
DOIs
StatePublished - 2005
Event2005 IEEE International Conference on Robotics and Automation - Barcelona, Spain
Duration: Apr 18 2005Apr 22 2005

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2005
ISSN (Print)1050-4729

Other

Other2005 IEEE International Conference on Robotics and Automation
Country/TerritorySpain
CityBarcelona
Period4/18/054/22/05

Keywords

  • Motion planning
  • RRTs
  • Voronoi bias

ASJC Scopus subject areas

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

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