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

TL;DR

This paper presents a decentralized reinforcement learning approach for multi-robot coverage tasks in large-scale environments with dynamic obstacles, optimizing task completion time while managing limited resources and replenishment stations.

Contribution

It introduces a novel end-to-end decentralized planning method for worker-station multi-robot systems tackling dynamic coverage tasks with resource constraints.

Findings

The method effectively reduces the impact of dynamic interferers on planning.

Robots successfully avoid collisions with dynamic obstacles.

Simulation and real robot experiments demonstrate competitive task finish times.

Abstract

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 paper, 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.