Biasing samplers to improve motion planning performance

Shawna Thomas, Marco Morales, Xinyu Tang, Nancy M. Amato

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

Abstract

With the success of randomized sampling-based motion planners such as Probabilistic Roadmap Methods, much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that outperforms all other techniques for all motion planning problems. Instead, each proposed technique has different strengths and weaknesses. However, little work has been done to combine these techniques to create new distributions. In this paper, we propose to bias one sampling distribution with another such that the resulting distribution out-performs either of its parent distributions. We present a general framework for biasing samplers that is easily extendable to new distributions and can handle an arbitrary number of parent distributions by chaining them together. Our experimental results show that by combining distributions, we can out-perform existing planners. Our results also indicate that not one single distribution combination performs the best in all problems, and we identify which perform better for the specific application domains studied.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Robotics and Automation, ICRA'07
Pages1625-1630
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Robotics and Automation, ICRA'07 - Rome, Italy
Duration: Apr 10 2007Apr 14 2007

Publication series

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

Conference

Conference2007 IEEE International Conference on Robotics and Automation, ICRA'07
Country/TerritoryItaly
CityRome
Period4/10/074/14/07

ASJC Scopus subject areas

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

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