Reducing metric sensitivity in randomized trajectory design

Peng Cheng, Steven M Lavalle

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

Abstract

This paper addresses trajectory design for generic problems that involve: 1) complicated global constraints that include nonconvex obstacles, 2) nonlinear equations of motion that involve substantial drift due to momentum, 3) a high-dimensional state space. Our approach to these challenging problems is to develop randomized planning algorithms based on Rapidly-exploring Random Trees (RRTs). RRTs use metric-induced heuristics to conduct a greedy exploration of the state space; however, performance substantially degrades when the chosen metric does not adequately reflect the true cost-to-go. In this paper, we present a version of the RRT that refines its exploration strategy in the presence of a poor metric. Experiments on problems in vehicle dynamics and spacecraft navigation indicate substantial performance improvement over existing techniques.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Pages43-48
Number of pages6
Volume1
StatePublished - 2001
Event2001 IEEE/RSJ International Conference on Intelligent Robots and Systems - Maui, HI, United States
Duration: Oct 29 2001Nov 3 2001

Other

Other2001 IEEE/RSJ International Conference on Intelligent Robots and Systems
Country/TerritoryUnited States
CityMaui, HI
Period10/29/0111/3/01

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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