Distributed Time-Varying Coverage Control via Singular Perturbations

TL;DR

This paper introduces a distributed, dynamic coverage control algorithm for robot groups that leverages singular perturbation theory and two-time scale separation to achieve optimal deployment in time-varying environments, with proven convergence.

Contribution

It develops a novel distributed coverage control method based on singular perturbation theory, enabling robots to adapt to changing density functions with proven asymptotic convergence.

Findings

Algorithm achieves centralized performance with small perturbation parameters.

Three discrete-time versions with 1-hop communication are effective.

Simulations demonstrate the algorithm's adaptability and convergence.

Abstract

This paper presents a novel dynamic coverage control algorithm allowing a group of robots to track an optimal-deployment configuration for arbitrary time-varying density functions. Building on singular perturbation theory, the proposed design employs a two-time scale separation approach, with a fast time scale corresponding to communication and a slow time scale corresponding to agent motion. The resulting algorithm is distributed over the 2-hop Delaunay graph and, for small enough values of the perturbation parameter, achieves the same performance as its centralized counterpart. We also introduce three discrete-time versions that rely only on 1-hop communication at the cost of having to use delayed information and formally establish their asymptotic convergence properties. Our technical approach combines computational geometry, singular perturbation theory, generating functions, and linear iterations with delayed updates. Various simulations illustrate the performance of the proposed algorithms.