Vision and motion planning for a mobile robot under uncertainty

This article describes a framework for vision and motion planning for a mobile robot. The robot's task is to reach the destination in the minimum time while detecting possible routes by vision. Since visual recognition is computationally expensive and the recognition result includes uncertainty, a trade-off must be considered between the rest of visual recognition and the effect of information to be obtained by recognition. Using a probabilistic model of the uncertainty of the recognition result, vision-motion planning is formulated as a recurrence formula. With this formulation, the optimal sequence of observation points is recursively determined. A generated plan is globally optimal, because the planner minimizes the total cost. An efficient solution strategy is also described that employs a pruning method based on the lower bound of the total cost calculated by assuming perfect sensor information. Stimulation results and experiments with an actual mobile robot demonstrate the feasibility of our approach.