Minimizing Task Completion Time of Prioritized Motion Planning in Multi-Robot Systems

Prioritized motion planning is an effective approach to decouple the motion coordination in multi-robot systems, in which priority assignment plays an important role on its performance. Previous approaches apply either a randomized search or a simple rule to prioritize robot motions, which are often computationally expensive and sometimes ineffective. In this paper, we present an effective approach to schedule robots movement with the aim of minimizing their task completion time. This approach first plans a time-optimal motion for each robot individually, then roughly prioritizes robots in the descending order of their motion-time, i.e., a robot is prioritized the highest if it has the largest motion-time, and vice versa. Then, we propose a heuristic to fine-tune robot priorities under the principle that the tuned priority shortens the task completion time. We also develop an asynchronous decentralized algorithm to accelerate the priority-tuning process. Compared to previous priority assignment approaches, our approach needs less computation time to generate a shorter task completion time in a multi-robot system.