Navigation with uncertainty: Reaching a goal in a high collision risk region

Philippe Burlina, Daniel DeMenthon, Larry S. Davis

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

Abstract

The authors describe a computational framework in which a probabilistic method for noisy sensor-based robotic navigation in dynamic environments can be devised. The aim of the method is to generate an optimal trajectory by considering an optimality criteria the probability of not colliding with the obstacles and the probability of accessing an operational position with respect to a moving target object. A formal framework in which the probability of collision associated with an elementary robot displacement can be calculated is discussed. Estimates on the obstacle kinematic parameters and measures of confidence on these estimates are used to produce the probability of collision associated with any robot displacement. The probability of collision is derived in two steps: a stochastic model is defined in the kinematic state space of the obstacles and collision events are given a simple geometric characterization in this state space.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherPubl by IEEE
Pages2440-2445
Number of pages6
ISBN (Print)0818627204
StatePublished - Dec 1 1992
EventProceedings of the 1992 IEEE International Conference on Robotics and Automation - Nice, Fr
Duration: May 12 1992May 14 1992

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume3

Other

OtherProceedings of the 1992 IEEE International Conference on Robotics and Automation
CityNice, Fr
Period5/12/925/14/92

ASJC Scopus subject areas

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

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