Distributed and Proximity-Constrained C-Means for Discrete Coverage Control

TL;DR

This paper introduces a distributed coverage control algorithm for mobile agents that uses proximity-constrained C-Means clustering to efficiently cover points of interest while respecting agents' sensing limitations.

Contribution

It extends traditional C-Means clustering with proximity constraints for distributed coverage control in multi-agent networks.

Findings

Effective coverage of dispersed PoIs achieved

Agents prioritize PoIs based on importance ranking

Framework applicable to disaster relief and patrolling

Abstract

In this paper we present a novel distributed coverage control framework for a network of mobile agents, in charge of covering a finite set of points of interest (PoI), such as people in danger, geographically dispersed equipment or environmental landmarks. The proposed algorithm is inspired by C-Means, an unsupervised learning algorithm originally proposed for non-exclusive clustering and for identification of cluster centroids from a set of observations. To cope with the agents' limited sensing range and avoid infeasible coverage solutions, traditional C-Means needs to be enhanced with proximity constraints, ensuring that each agent takes into account only neighboring PoIs. The proposed coverage control framework provides useful information concerning the ranking or importance of the different PoIs to the agents, which can be exploited in further application-dependent data fusion processes, patrolling, or disaster relief applications.