Tree Search-based Task and Motion Planning with Prehensile and Non-prehensile Manipulation for Obstacle Rearrangement in Clutter

Abstract

We propose a tree search-based planning algorithm for a robot manipulator to rearrange objects and grasp a target in a dense space. We consider environments where tasks cannot be completed with prehensile planning only. As assuming that a manipulator is only allowed to grasp from the top, we aim to minimize the number of rearrangement actions and the total execution time, which affects the efficiency of manipulation. The proposed search algorithm determines the optimal sequence of object rearrangement with prehensile and non-prehensile grasping until grasping a target. For non-prehensile grasping, a heuristic function is employed to model frictions and contacts between objects and a table. Experimental results in a realistic simulated environment show that the proposed algorithm can reduce the number of rearranged obstacles up to 27% and the total execution time up to 15% with 14 objects compared to the previous work.