Task space motion planning using reactive control

In this paper we present an approach to reduce the effort for planning robot motions by shifting the planning problem to a high-level representation. We combine classical sampling-based random tree planning with a reactive controller connecting sampling points with nontrivial trajectories, utilizing redundant DOFs to locally avoid obstacles. While the reactive planner operates locally on a short time scale, the complementary sampling-based method is able to find globally feasible solutions due to its larger preview horizon. Additionally, planning is done in a low-dimensional task space instead of the high-dimensional joint space. Comparing the average planning time and number of tree extensions for several scenarios and planning methods, we demonstrate that this hybrid planning approach is capable of solving a large fraction of planning queries while saving considerable planning time.