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Effect of Human-Robot Interaction on the Fleet Size of AIV Transporters in FMS

Effect of Human-Robot Interaction on the Fleet Size of AIV Transporters in FMS

Martin, Lancelot, González-Romo, Mario, Sahnoun, M'hammed, Bettayeb, Belgacem, He, Naihui and Gao, James ORCID: 0000-0001-5625-3654 (2021) Effect of Human-Robot Interaction on the Fleet Size of AIV Transporters in FMS. In: 2021 1st International Conference On Cyber Management And Engineering (CyMaEn). IEEE. (In Press)

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Abstract

The execution of material handling tasks using autonomous guided vehicles (AGVs) has proven a real success during the last decade. Nevertheless, the installation of AGVs is costly as it needs to modify the workshop’s configuration by defining dedicated movement zones. Recently, more flexible and collaborative mobile robots known as autonomous intelligent robots (AIV) can be used in manufacturing systems. This new generation of intelligent mobile robots does not need specific zones and can interact with unexpected or mobile obstacles such as human operators. This paper focuses on AIV fleet size definition in a variable and unexpected environment with humans while keeping AIV assigned transportation tasks on time. A simulation that model the complexity of the AIV travel time estimation under the mentioned circumstances and the improvement brought by IoT, Big Data and sensors by using them as the real-time data source is developed.

Item Type: Conference Proceedings
Title of Proceedings: 2021 1st International Conference On Cyber Management And Engineering (CyMaEn)
Uncontrolled Keywords: Industry 4.0, Industry 5.0, Fleet management optimization, Simulation, Human operator behavior, Scheduling
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 15 Jun 2021 15:22
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/32978

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