Multi-agents adaptive estimation and coverage control using Gaussian regression

TL;DR

This paper presents a method for multi-agent coverage control that simultaneously estimates an unknown sensory function using Gaussian regression, balancing coverage and estimation in real-time with proven convergence.

Contribution

It introduces a Bayesian Gaussian regression framework for online estimation of unknown sensory functions in multi-agent coverage tasks, integrating estimation with control.

Findings

Numerical experiments demonstrate the effectiveness of the proposed approach.

The algorithm achieves a good balance between coverage and estimation accuracy.

Convergence properties of the control and estimation process are discussed.

Abstract

We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function. In particular, centroidal Voronoi partitions have to be computed. The difficulty of the task is that the sensory function is unknown and has to be reconstructed on line from noisy measurements. Hence, estimation and coverage needs to be performed at the same time. We cast the problem in a Bayesian regression framework, where the sensory function is seen as a Gaussian random field. Then, we design a set of control inputs which try to well balance coverage and estimation, also discussing convergence properties of the algorithm. Numerical experiments show the effectivness of the new approach.