Vision-Motion Planning of a Mobile Robot Considering Vision Uncertainty and Planning Cost

Abstract

This paper proposes a planning method for a vision-guided mobile robot under vision uncertainty and limited computational resources. The method considers the following two tradeoffs: (1) granularity in approximating a probabilistic distribution vs. plan quality, and (2) search depth vs. plan quality. The first tradeoff is managed by predicting the plan quality for a granularity using a learned relationship between them, and by adaptively selecting the best granularity. The second trade-off is managed by formulating the planning process as a search in the space of feasible plans, and by appropriately limiting the search considering the merit of each step of the search. Simulation results and experiments using a real robot show the feasibility of the method. 1 Introduction There has been an increasing interest in autonomous mobile robot which recognizes an environment with vision and moves without guidance of human operators. A key to realize such a robot is the ability to generate a p...