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.

翻译：传统分布式覆盖控制方法因智能体信息有限，尤其在非凸离散环境中，可能面临收敛性不足和性能欠佳的问题。为解决这一难题，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缺陷。本文的重点是将这些结果推广到一般维度设置中。我们证明该扩展具有收敛性，并保持2的近似比，即任何稳定解的性能都保证在最优解的50%以内。我们还证明其计算复杂度和通信复杂度均为问题规模的多项式级。实验结果表明，我们的算法优于多个最新算法，且运行时间具有理论上的可扩展性。