Dynamically Feasible Task-Constrained Motion Planning with Moving Obstacles

We present a randomized algorithm for planning dynamically feasible motions of robots subject to geometric task constraints in the presence of moving obstacles. The proposed method builds upon our previous results on task-constrained motion planning with moving obstacles. With respect to our previous formulation, the inclusion of bounds on the available actuator torques leads to the adoption of an acceleration-level motion generation scheme. Therefore, the new planner must operate in a task-constrained state space extended with time. The generated trajectories are collision-free, obey velocity and torque bounds, and satisfy the task constraint with arbitrary accuracy. The effectiveness of the proposed approach is shown by results on various scenarios involving a 7-dof manipulator.