Voronoi-based Multi-Robot Formations for 3D Source Seeking via Cooperative Gradient Estimation

TL;DR

This paper introduces a Voronoi-based formation control strategy for multi-robot systems to accurately and robustly locate 3D signal sources by cooperative gradient estimation, even in noisy measurement environments.

Contribution

The paper proposes a novel formation control method using constrained Centroidal Voronoi partitions on a sphere for improved gradient estimation in 3D source seeking.

Findings

Robust gradient estimation with Voronoi formations improves source localization accuracy.

Simulation results demonstrate superior performance under noisy conditions.

The approach outperforms existing methods in robustness and accuracy.

Abstract

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.