Distributed and proximity-constrained C-means for discrete coverage control

Adriano Fagiolini, Andrea Gasparri, Gabriele Oliva, Christoforos N. Hadjicostis

Risultato della ricerca: Conference contribution

1 Citazioni (Scopus)


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.
Lingua originaleEnglish
Titolo della pubblicazione ospite2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Numero di pagine6
Stato di pubblicazionePublished - 2017

All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.2200.2209???
  • ???subjectarea.asjc.2600.2606???


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