A framework for motion planning in stochastic environments: modeling and analysis

Abstract

Presents a framework for analyzing and determining motion plans for a robot that operates in an environment that changes over time in an uncertain manner. The authors 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. The authors 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, the authors 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. The authors 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.