A drift-diffusion model for robotic obstacle avoidance

Paul Reverdy, B. Deniz Ilhan, Daniel E. Koditschek

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

8 Scopus citations

Abstract

We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Péclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform.

Original languageEnglish (US)
Title of host publicationIROS Hamburg 2015 - Conference Digest
Subtitle of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6113-6120
Number of pages8
ISBN (Electronic)9781479999941
DOIs
StatePublished - Dec 11 2015
Externally publishedYes
EventIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
Duration: Sep 28 2015Oct 2 2015

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2015-December
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
Country/TerritoryGermany
CityHamburg
Period9/28/1510/2/15

Keywords

  • Boundary conditions
  • Collision avoidance
  • Mathematical model
  • Navigation
  • Robot kinematics
  • Stochastic processes

ASJC Scopus subject areas

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

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