The manufacturing assembly lines of the future are foreseen to dismiss fully unmanned systems in favour of anthropocentric solutions. However, bringing in the human complexity leads to modeling and control questions that only data can answer. Moreover, many human-robot collaborative applications in flexible manufacturing involve manipulator cobots, whereas little attention is given to the role of mobile robots. This work outlines a data-driven framework, which is the core of a brand new project to be fully developed in the very next future, to let human-robot collaborative processes overcome the barriers to successful interaction, leveraging mobile and fixed-base robots.

Data-driven framework to improve collaborative human-robot flexible manufacturing applications / Sibona, Fiorella; Indri, Marina. - ELETTRONICO. - (2021). (Intervento presentato al convegno IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society tenutosi a Toronto, ON, Canada nel 13-16 Oct. 2021) [10.1109/IECON48115.2021.9589874].

Data-driven framework to improve collaborative human-robot flexible manufacturing applications

Sibona, Fiorella;Indri, Marina
2021

Abstract

The manufacturing assembly lines of the future are foreseen to dismiss fully unmanned systems in favour of anthropocentric solutions. However, bringing in the human complexity leads to modeling and control questions that only data can answer. Moreover, many human-robot collaborative applications in flexible manufacturing involve manipulator cobots, whereas little attention is given to the role of mobile robots. This work outlines a data-driven framework, which is the core of a brand new project to be fully developed in the very next future, to let human-robot collaborative processes overcome the barriers to successful interaction, leveraging mobile and fixed-base robots.
2021
978-1-6654-3554-3
File in questo prodotto:
File Dimensione Formato  
main_rev_final_authors_IECON21.pdf

accesso aperto

Descrizione: Versione accettata
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 1.3 MB
Formato Adobe PDF
1.3 MB Adobe PDF Visualizza/Apri
IECON2021.pdf

accesso riservato

Descrizione: Versione a stampa
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.97 MB
Formato Adobe PDF
1.97 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2939000