Multi-agents adaptive estimation and coverage control using Gaussian regression
This work addresses the challenge of optimal region coverage for multi-agent systems when the sensory function is unknown and noisy, representing an incremental improvement in adaptive control methods.
The paper tackled the problem of multi-agent coverage control with an unknown sensory function by simultaneously performing estimation and coverage using Gaussian regression, and demonstrated the effectiveness of the approach through numerical experiments.
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.