# Multi-agents adaptive estimation and coverage control using Gaussian regression

arXiv: https://arxiv.org/abs/1407.5807 · HTML: https://mlsanity.com/paper/1407.5807/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2014.

## Built on by (most cited first, 4 of 4)

- [Near-Optimal Multi-Agent Learning for Safe Coverage Control](https://mlsanity.com/paper/2210.06380/) (2022)
- [Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles](https://mlsanity.com/paper/2105.00246/) (2021)
- [Gaussian kernel expansion with basis functions uniformly bounded in L∞](https://mlsanity.com/paper/2410.01394/) (2024)
- [A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control](https://mlsanity.com/paper/2511.02398/) (2025)

## Abstract (arXiv)

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.