1998 Volume 13 Issue 4 Pages 588-596
This paper proposes a planning method for a vision-guided mobile robot under limited computational resources and vision uncertainty. The method considers the following two trade-offs : (1) granularity in approximating a probabilistic distribution vs. plan quality, and (2) search depth vs. plan quality. The first trade-off 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 show the feasibility of the method.
