Distributed Coverage Control for Time-Varying Spatial Processes

TL;DR

This paper introduces a distributed control method for multi-robot systems to monitor and adaptively cover dynamic spatial phenomena using Gaussian Processes, balancing exploration and exploitation in real-time environments.

Contribution

It presents a novel fully distributed control strategy that handles time-varying spatial fields with efficient data management, advancing multi-robot environmental monitoring capabilities.

Findings

Effective coverage of dynamic spatial fields demonstrated in simulations.

Robustness to real-world data variations validated through experiments.

Scalable approach suitable for large multi-robot teams.

Abstract

Multi-robot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multi-robot team for optimal coverage in environments where the density distribution, describing areas of interest, is unknown and changes over time. We propose a fully distributed control strategy that uses Gaussian Processes (GPs) to model the spatial field and balance the trade-off between learning the field and optimally covering it. Unlike existing approaches, we address a more realistic scenario by handling time-varying spatial fields, where the exploration-exploitation trade-off is dynamically adjusted over time. Each robot operates locally, using only its own collected data and the information shared by the neighboring robots. To address the computational limits of GPs, the algorithm efficiently manages the volume of data by selecting only the most relevant samples for the process estimation. The performance of the proposed algorithm is evaluated through several simulations and experiments, incorporating real-world data phenomena to validate its effectiveness.