Sampling-based motion planning with dynamic intermediate state objectives: Application to throwing

Dynamic manipulations require attaining high velocities at specified configurations, all the while obeying geometric and dynamic constraints. This paper presents a motion planner that constructs a trajectory that passes at an intermediate state through a dynamic objective region, which is comprised of a certain lower dimensional submanifold in the configuration/velocity state space, and then returns to rest. Planning speed and reliability are greatly improved by finding good intermediate states first, because the choice of intermediate state couples the ramp-up and ramp-down subproblems, and moreover very few (often less than 1%) intermediate states yield feasible solution trajectories. Simulation experiments demonstrate that our method quickly generates trajectories for a 6-DOF industrial manipulator throwing a small object.