Vision and Motion Planning for a Mobile Robot under Uncertainty

This paper describes a framework for vision and motion planning for a mobile robot. The task of the robot is to reach the destination in the minimum time while it detects possible routes by vision. Since visual recognition is computationally expensive and the recognition result includes uncertainty, a trade-o must be considered between the cost of visual recognition and the e ect 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 e cient solution strategy is also described which employs a pruning method based on the lower bound of the total cost calculated by assuming perfect sensor information. Simulation results and experiments with an actual mobile robot demonstrate the feasibility of our approach.