多机器人通过特定排列精准定位三维信号源，抗干扰更强。
Voronoi-based Multi-Robot Formations for 3D Source Seeking via Cooperative Gradient Estimation
- 用球面重心Voronoi点分布机器人位置，保证覆盖与几何精度。
- 无需复杂计算，直接用解析式估计梯度，定位更准。
- 适合噪声环境下分布式信号源搜索，优于现有方法。
本文研究如何利用一群可收集信号强度噪声测量值并共享信息的移动机器人，定位三维信号场的源头。采用协同梯度估计策略，通过解析表达式计算信号场梯度，并引导机器人向源点移动。为确保梯度估计的准确与鲁棒性，机器人被布置在固定半径的球面上，其位置对应于球面上受限的重心Voronoi剖分生成点。我们证明，这种特定构型同时实现关键几何特性与高场覆盖度，并支持通过简单解析式进行梯度估计。最后通过仿真评估了该方法性能，涵盖无噪与有噪情形。对比分析表明，该方法在面对故障测量时具有更高鲁棒性，优于一种先进基准方案。
原文摘要 · Abstract (English)
In this paper, we tackle the problem of localizing the source of a three-dimensional signal field with a team of mobile robots able to collect noisy measurements of its strength and share information with each other. The adopted strategy is to cooperatively compute a closed-form estimation of the gradient of the signal field that is then employed to steer the multi-robot system toward the source location. In order to guarantee an accurate and robust gradient estimation, the robots are placed on the surface of a sphere of fixed radius. More specifically, their positions correspond to the generators of a constrained Centroidal Voronoi partition on the spherical surface. We show that, by keeping these specific formations, both crucial geometric properties and a high level of field coverage are simultaneously achieved and that they allow estimating the gradient via simple analytic expressions. We finally provide simulation results to evaluate the performance of the proposed approach, considering both noise-free and noisy measurements. In particular, a comparative analysis shows how its higher robustness against faulty measurements outperforms an alternative state-of-the-art solution.
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