Learning to Coordinate for a Worker-Station Multi-Robot System in Planar Coverage Tasks
Jingtao Tang, Yuan Gao, Tin Lun Lam · IEEE Robotics and Automation Letters · 2022
For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this letter, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources. We introduce aworker-stationMRS consisting of multipleworkerswith limited resources for actual work, and onestationwith enough resources for resource replenishment. We aim to solve the mCPP problem for theworker-stationMRS by formulating it as a fully cooperative multi-agent reinforcement learning problem. Then we propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning forworkersand rendezvous planning forstation. Our method manages to reduce the influence of random dynamic interferers on planning, while the robots can avoid collisions with them. We conduct simulation and real robot experiments, and the comparison results show that our method has competitive performance in solving the mCPP problem forworker-stationMRS in metric of task finish time.