Coverage Control in Multi-Robot Systems via Graph Neural Networks

TL;DR

This paper introduces a decentralized multi-robot coverage control method using Graph Neural Networks, enabling robots to communicate over multiple hops for improved coverage in regions with varying importance.

Contribution

It presents a novel GNN-based decentralized control policy that leverages multi-hop communication for enhanced coverage in multi-robot systems.

Findings

GNN-based controller outperforms classical local-only methods.

Multi-hop communication improves coverage quality.

Controller demonstrates scalability and transferability in simulations.

Abstract

This paper develops a decentralized approach to mobile sensor coverage by a multi-robot system. We consider a scenario where a team of robots with limited sensing range must position itself to effectively detect events of interest in a region characterized by areas of varying importance. Towards this end, we develop a decentralized control policy for the robots -- realized via a Graph Neural Network -- which uses inter-robot communication to leverage non-local information for control decisions. By explicitly sharing information between multi-hop neighbors, the decentralized controller achieves a higher quality of coverage when compared to classical approaches that do not communicate and leverage only local information available to each robot. Simulated experiments demonstrate the efficacy of multi-hop communication for multi-robot coverage and evaluate the scalability and transferability of the learning-based controllers.