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. The experimental results exhibit that our algorithm outperforms several state-of-the-art algorithms, and also that the runtime is scalable.

翻译：传统的分布式覆盖控制方法可能因智能体信息有限而缺乏收敛性且性能不佳，尤其是在非凸离散环境中。为解决此问题，我们扩展了[Marden 2016]的方法，该方法展示了如何利用有限程度的智能体间通信来克服一维离散环境中的此类难题。本文重点在于将这些结果推广至一般维度场景。我们证明了该扩展方法具有收敛性，且保持2的近似比，即任何稳定解的性能均可保证在最优解的50%以内。实验结果表明，我们的算法优于多种当前先进算法，且运行时间具有可扩展性。