This paper addresses the crucial challenge of maintaining the directed graph topology in multi-robot systems, particularly when operating under limited field-of-view constraints and with a lack of communication among robots. Traditional methods for multi-robot coordination rely heavily on inter-robot communication, which may not always be feasible, particularly in constrained or hostile environments. Our work presents a novel distributed control algorithm that leverages Control Barrier Functions (CBFs) to maintain the graph topology of a multi-robot system based solely on local, onboard sensor data. This approach is particularly beneficial in situations where external communication channels are disrupted or unavailable. The key contributions of this research are threefold: First, we design a novel control algorithm that efficiently maintains the graph topology in multi-robot systems using CBFs, which operate on neighbor detection data. Second, we perform an experimental evaluation of the algorithm, demonstrating its efficacy in controlling the flight of a team of drones using only local robot data. Third, we apply our methodology to a distributed coverage control scenario, showing that our approach can effectively manage a multi-robot system using only local information.

Directed Graph Topology Preservation in Multi-Robot Systems With Limited Field of View Using Control Barrier Functions / Bertoncelli, F.; Radhakrishnan, V.; Catellani, M.; Loianno, G.; Sabattini, L.. - In: IEEE ACCESS. - ISSN 2169-3536. - 12:(2024), pp. 9682-9690. [10.1109/ACCESS.2024.3352131]

Directed Graph Topology Preservation in Multi-Robot Systems With Limited Field of View Using Control Barrier Functions

Bertoncelli F.
;
Catellani M.;Sabattini L.
2024

Abstract

This paper addresses the crucial challenge of maintaining the directed graph topology in multi-robot systems, particularly when operating under limited field-of-view constraints and with a lack of communication among robots. Traditional methods for multi-robot coordination rely heavily on inter-robot communication, which may not always be feasible, particularly in constrained or hostile environments. Our work presents a novel distributed control algorithm that leverages Control Barrier Functions (CBFs) to maintain the graph topology of a multi-robot system based solely on local, onboard sensor data. This approach is particularly beneficial in situations where external communication channels are disrupted or unavailable. The key contributions of this research are threefold: First, we design a novel control algorithm that efficiently maintains the graph topology in multi-robot systems using CBFs, which operate on neighbor detection data. Second, we perform an experimental evaluation of the algorithm, demonstrating its efficacy in controlling the flight of a team of drones using only local robot data. Third, we apply our methodology to a distributed coverage control scenario, showing that our approach can effectively manage a multi-robot system using only local information.
2024
12
9682
9690
Directed Graph Topology Preservation in Multi-Robot Systems With Limited Field of View Using Control Barrier Functions / Bertoncelli, F.; Radhakrishnan, V.; Catellani, M.; Loianno, G.; Sabattini, L.. - In: IEEE ACCESS. - ISSN 2169-3536. - 12:(2024), pp. 9682-9690. [10.1109/ACCESS.2024.3352131]
Bertoncelli, F.; Radhakrishnan, V.; Catellani, M.; Loianno, G.; Sabattini, L.
File in questo prodotto:
File Dimensione Formato  
24Access.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 983.77 kB
Formato Adobe PDF
983.77 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1366431
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact