Robot Motion Planning in a Changing

In this paper we present a framework for analyzing and determining robot motion plans for situations in which the robot is aaected by an environment that prob-abilistically changes over time. In general, motion planning under uncertainty has recently received substantial interest, and in particular a changing-environment has been recognized as an important aspect of motion planing under uncertainty. We model the environment as a nite-state Markov process, and the robot executes a motion strategy that is conditioned on its current position and the state of the environment. Optimality of a robot strategy is evaluated in terms of a performance functional that depends on the environment, robot actions , and a precise encoding of relevant preferences. By using a simple, yet powerful computation technique that is based on dynamic programming, we can numerically compute optimal robot strategies for a wide class of problems, surpassing previous results in this context that were obtained analytically. Several computed motion planning examples are presented.