On the theory of user-guided planning

Abstract

Sampling-based techniques are often employed to solve various complex motion planning problems —the problem of computing a valid path under various robot and/or obstacle constraints. As these methods are random in nature, the probability of their success is directly related to the expansiveness, or openness, of the underlying planning space. However, little is known theoretically in qualifying the conditions under which user (human)-guided approaches improve the efficiency of sampling-based planners. In this paper, we classify and create simplistic models of common user-guided approaches, and we extend the concept of expansiveness to analyze these models to understand both when and how much user-guidance aids sampling-based planners.