A new framework of a hierarchical vision-motion planning for a mobile robot under uncertainty is proposed. An optimal plan is generated by twolevel planning: global route selection and local path planning. In global route selection, a sequence of observation points to acquire sufficient information to reach a destination is determined. Both the cost and the uncertainty of vision are considered ...

Planning by incomplete stochastic search ooers a promising alternative to classical, complete planning methods. The success of this approach is documented by recent performance results obtained by transforming planning tasks into propositional sat-issability problems and using existing eecient local search methods to solve them. On the other hand, we argue that these results can still be improv...

We present a simple approach for planning the motion of nonholonomic robots among obstacles. Existing methods lead to open-loop solutions which are either obtained in two stages, approximating a previously built holonomic path, or computationally intensive, being based on configuration space discretization. Our nonholonomic planner employs a direct projection strategy to modify on-line the outp...

OF THE DISSERTATION Local Planning for Continuous Markov Decision Processes