This paper introduces a novel approach to solve the coverage optimization problem in multi-agent systems. The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function for optimizing the agents’ coverage and the cost function eventually aligns with the conventional Voronoi-based cost function. Theoretical analyses are conducted to assure the asymptotic convergence of agents toward an optimal configuration. A distinguishing feature of this approach lies in its departure from the reliance on geometric methods that are characteristic of Voronoi-based approaches; hence it can be implemented more simply. Remarkably, the technique is adaptive and applicable to various environments with both known and unknown information distributions. Lastly, the efficacy of the proposed method is demonstrated through simulations, and the obtained results are compared with those of Voronoi-based algorithms.

中文翻译：

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多智能体系统中的分布式最佳覆盖控制：已知和未知环境


本文介绍了一种解决多智能体系统中覆盖率优化问题的新方法。与传统的基于 Voronoi 的技术相比，所提出的技术通过使用排序函数有效地处理代理在没有信息的区域保持静止的问题，提供了一种成本更低的最佳解决方案。所提出的方法利用一种新的成本函数来优化代理的覆盖率，并且成本函数最终与传统的基于 Voronoi 的成本函数保持一致。进行理论分析以确保代理向最佳配置渐近收敛。这种方法的一个显着特点在于它摆脱了对基于 Voronoi 的方法所特有的几何方法的依赖;因此可以更简单地实现它。值得注意的是，该技术具有自适应性，适用于具有已知和未知信息分布的各种环境。最后，通过仿真证明了所提方法的有效性，并将所得结果与基于 Voronoi 的算法进行了比较。