Planning Among Movable Obstacles with Artificial Constraints

In this paper we present artificial constraints as a method for guiding heuristic search in the computationally challenging domain of motion planning among movable obstacles. The robot is permitted to manipulate unspecified obstacles in order to create space for a path. A plan is an ordered sequence of paths for robot motion and object manipulation. We show that under monotone assumptions, anticipating future manipulation paths results in constraints on both the choice of objects and their placements at earlier stages in the plan. We present an algorithm that uses this observation to incrementally reduce the search space and quickly find solutions to previously unsolved classes of movable obstacle problems. Our planner is developed for arbitrary robot geometry and kinematics. It is presented with an implementation for the domain of navigation among movable obstacles.