Planning Optimal Motions for Anthropomorphic Systems

This thesis deals with the development and study of algorithms for planning optimal motions for anthropomorphic systems, which are underactuated and highly redundant systems, such as humanoid robots and digital actors. Randomized motion planners and optimal control methods are proposed and discussed. A first contribution concerns the use of an efficient graph search algorithm in order to optimize walk trajectories that were previously obtained for a bounding-box representation of the system using randomized motion planners. The second contribution develops the use of constrained randomized motion planners in order to plan in a generic way whole-body motions that involve both walking and manipulation. Finally we develop an algorithmic approach which combines constrained randomized motion planners and optimal control methods; this approach allows the generation of dynamic, fast and collision-free motions for anthropomorphic systems in the presence of obstacles.