  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.
