Framework for motion planning in stochastic environments: modeling and analysis

Steven M Lavalle, Rajeev Sharma

Research output: Contribution to journalConference articlepeer-review


We present a framework for analyzing and determining motion plans for a robot that operates in an environment that changes over time in an uncertain manner. We first classify sources of uncertainty in motion planning into four categories, and argue that the framework addressed in this paper characterizes an important, yet little-explored category. We treat the changing environment in a flexible manner by combining traditional configuration space concepts with a Markov process that models the environment. For this context, we then propose the use of a motion strategy, which provides a motion command for the robot for each contingency that it could be confronted with. We allow the specification of a desired performance criterion, such as time or distance, and the goal is to determine a motion strategy that is optimal with respect to that criterion. A motion planning problem in this framework is formulated as the design of a stochastic optimal controller. Applications and computational issues are discussed in a companion paper [12].

Original languageEnglish (US)
Pages (from-to)3057-3062
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Jan 1 1995
EventProceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3) - Nagoya, Jpn
Duration: May 21 1995May 27 1995

ASJC Scopus subject areas

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

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