Learning to Coordinate for a Worker-Station Multi-Robot System in Planar Coverage Tasks

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 a
worker-station
MRS consisting of multiple
workers
with limited resources for actual work, and one
station
with enough resources for resource replenishment. We aim to solve the mCPP problem for the
worker-station
MRS 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 for
workers
and rendezvous planning for
station
. 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 for
worker-station
MRS in metric of task finish time.