Coverage Control in Multi-Robot Systems via Graph Neural Networks
This addresses coverage control for multi-robot systems in scenarios with varying importance, offering a decentralized solution that improves performance over local methods.
The paper tackled the problem of mobile sensor coverage in multi-robot systems by developing a decentralized control policy using Graph Neural Networks with multi-hop communication, resulting in higher coverage quality compared to classical non-communicating approaches.
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.