Sample-Based Motion planning for Soft Robot Manipulators Under Task Constraints

Random sampling-based methods for motion planning of constrained robot manipulators has been widely studied in recent years. The main problem to deal with is the lack of an explicit parametrization of the non linear submanifold in the Configuration Space (CS), due to the constraints imposed by the system. Most of the proposed planning methods use projections to generate valid configurations of the system slowing the planning process. Recently, new robot mechanism includes compliance either in the structure or in the controllers. In this kind of robot most of the times the planned trajectories are not executed exactly by the robots due to uncertainties in the environment. Indeed, controller references are generated such that the constraint is violated to indirectly generate forces during interactions. In this paper we take advantage of the compliance of the system to relax the geometric constraint imposed by the task, mainly to avoid projections. The relaxed constraint is then used in a state-of-the-art sub-optimal random sampling based technique to generate any-time paths for constrained robot manipulators. As a consequence of relaxation, contact forces acting on the constraint change from configuration to configuration during the planned path. Those forces can be regulated using a proper controller that takes advantage of the geometric decoupling of the subspaces describing constrained rigid-body motions of the mechanism and the controllable forces.