Local and Global Search-Based Planning for Object Rearrangement in Clutter

Abstract

We propose two algorithms based on local and global searches for a Task and Motion Planning (TAMP) problem, which considers a robotic manipulator to rearrange obstacles and grasp a target in clutter. In the problem, no collision-free path for a robotic manipulator is available unless some obstacles blocking a target are relocated. The two algorithms determine the sequence of obstacles to be removed for grasping a target without collisions. The local search algorithm determines the sequence quickly in an online manner but could be suboptimal in the number of removed obstacles. The global search algorithm based on tree search needs upfront computation but runs in polynomial time. In numerical simulation settings, we consider objects in various shapes, which could make reachable directions bounded. From the simulations the planning time of local search algorithm is faster than that of global search, whereas the global search algorithm removes the less number of obstacles than the local search. In addition, we show that the global search algorithm with a heuristic cost is faster than without the cost but the minimum number of removed obstacles is still obtained from the global search algorithm without the heuristic cost. Practical experiments show the applicability of our algorithms in real environments.