Distributed and Proximity-Constrained C-Means for Discrete Coverage Control
This addresses coverage control for mobile agents in applications like disaster relief, though it is incremental as it builds on existing C-Means with added constraints.
The paper tackles the problem of distributed coverage control for mobile agents covering points of interest by proposing a novel framework based on C-Means with proximity constraints to handle limited sensing ranges, resulting in a method that provides ranking information for PoIs applicable to disaster relief and other domains.
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.