A control-based approach to task-constrained motion planning

We consider the problem of planning collision-free motions for general (i.e., possibly nonholonomic) redundant robots subject to task space constraints. Previous approaches to the solution are based on the idea of sampling and inverting the task constraint to build a roadmap of task-constrained configurations which are then connected by simple local paths; hence, task tracking is not enforced during the motion between samples. Here, we present a control-based randomized approach relying on a motion generation scheme that guarantees continued satisfaction of such constraint. The resulting planner allows to achieve accurate execution of the desired task without increasing the size of the roadmap. Numerical results on a fixed-base manipulator and a free-fying mobile manipulator are presented to illustrate the performance improvement obtained with the proposed technique.