Is Limited Information Enough? An Approximate Multi-agent Coverage Control in Non-Convex Discrete Environments

Conventional distributed approaches to coverage control may suffer from lack of convergence and poor performance, due to the fact that agents have limited information, especially in non-convex discrete environments. To address this issue, we extend the approach of [Marden 2016] which demonstrates how a limited degree of inter-agent communication can be exploited to overcome such pitfalls in one-dimensional discrete environments. The focus of this paper is on extending such results to general dimensional settings. We show that the extension is convergent and keeps the approximation ratio of 2, meaning that any stable solution is guaranteed to have a performance within 50% of the optimal one. We also show that the computational complexity and communication complexity are both polynomial in the size of the problem. The experimental results exhibit that our algorithm outperforms several state-of-the-art algorithms, and also that the runtime is scalable as per theory.