The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots' physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and preliminary results of the linear regression model is presented.

Prediction and estimation model of energy demand of the AMR with cobot for the designed path in automated logistics systems / Aliev, K.; Traini, E.; Asranov, M.; Awouda, A.; Chiabert, P.. - 99:(2021), pp. 116-121. [10.1016/j.procir.2021.03.036]

Prediction and estimation model of energy demand of the AMR with cobot for the designed path in automated logistics systems

Aliev K.;Traini E.;Asranov M.;Awouda A.;Chiabert P.
2021

Abstract

The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots' physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and preliminary results of the linear regression model is presented.
Procedia CIRP
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2924892