Real-time Planning Robotic Palletizing Tasks using Reusable Roadmaps

Abstract

This paper focuses on robotic motion planning for performing the palletizing or de-palletizing tasks. In such tasks, a robot usually iterates similar pick-and-place for several times. Considering such feature of the tasks, we propose two motion planning approaches named reusable Probabilistic Roadmap Method (PRM) and reusable Rapidly-exploring Random Tree Star (RRT*) where both methods utilize the previously constructed roadmaps in the conventional PRM and RRT*, respectively. We experimentally confirm that both methods significantly save the calculation time needed for motion planning compared to the conventional planning methods.