A growing presence of mobile agents is envisaged in the smart factories scenario of the next future. The safe motion of traditional Automated Guided Vehicles in human-shared workspaces can be achieved thanks to the support of a fleet of Autonomous Mobile Robots, acting as a net of meta-sensors, able to detect the human presence and share the information. This paper proposes a preliminary working implementation of one meta-sensor module, exploiting the synergistic use of different sensors through an overall affordable and accessible sensor data fusion algorithm. Experimental results in a laboratory environment confirm the validity of the approach.

Sensor data fusion for smart AMRs in human-shared industrial workspaces / Indri, Marina; Sibona, Fiorella; Cen Cheng, Pangcheng David. - ELETTRONICO. - (2019), pp. 738-743. ((Intervento presentato al convegno IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society tenutosi a Lisbon, Portugal nel 14-17 Oct. 2019 [10.1109/IECON.2019.8927622].

Sensor data fusion for smart AMRs in human-shared industrial workspaces

Indri, Marina;Sibona, Fiorella;Cen Cheng, Pangcheng David
2019

Abstract

A growing presence of mobile agents is envisaged in the smart factories scenario of the next future. The safe motion of traditional Automated Guided Vehicles in human-shared workspaces can be achieved thanks to the support of a fleet of Autonomous Mobile Robots, acting as a net of meta-sensors, able to detect the human presence and share the information. This paper proposes a preliminary working implementation of one meta-sensor module, exploiting the synergistic use of different sensors through an overall affordable and accessible sensor data fusion algorithm. Experimental results in a laboratory environment confirm the validity of the approach.
978-1-7281-4878-6
File in questo prodotto:
File Dimensione Formato  
IECON2019.pdf

non disponibili

Descrizione: Versione editoriale
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 3.51 MB
Formato Adobe PDF
3.51 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
main_rev_authors.pdf

accesso aperto

Descrizione: Versione autori accettata per la pubblicazione
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 3.36 MB
Formato Adobe PDF
3.36 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento 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: http://hdl.handle.net/11583/2785718