Human-robot collaborative applications are generally based on some kind of co-working of the human operator and the robot in the execution of a given task. A disruptive change in the collaborative modalities would be given by the capability of the robot to anticipate how it could be of help for the operator. In case of an Autonomous Mobile Robot (AMR), this would imply not only a safe navigation in presence of a human operator, but the automatic adaptation of its motion to the specific operation carried out by the operator. This paper investigates the possibility of achieving operation recognition by monitoring the human motion on a 2D map and classifying his/her path on the map, taken as an image data sample. Deep learning state-of-the-art libraries and architectures are exploited with the aim of making the robotic system aware of the ongoing process. The reported results, relative to a small training dataset, are nonetheless promising.

How to improve human-robot collaborative applications through operation recognition based on human 2D motion / Sibona, Fiorella; Cen Cheng, Pangcheng David; Indri, Marina. - ELETTRONICO. - (2022). ((Intervento presentato al convegno IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society tenutosi a Brussels (Belgium) nel 17-20 October 2022 [10.1109/IECON49645.2022.9969120].

How to improve human-robot collaborative applications through operation recognition based on human 2D motion

Sibona, Fiorella;Cen Cheng, Pangcheng David;Indri, Marina
2022

Abstract

Human-robot collaborative applications are generally based on some kind of co-working of the human operator and the robot in the execution of a given task. A disruptive change in the collaborative modalities would be given by the capability of the robot to anticipate how it could be of help for the operator. In case of an Autonomous Mobile Robot (AMR), this would imply not only a safe navigation in presence of a human operator, but the automatic adaptation of its motion to the specific operation carried out by the operator. This paper investigates the possibility of achieving operation recognition by monitoring the human motion on a 2D map and classifying his/her path on the map, taken as an image data sample. Deep learning state-of-the-art libraries and architectures are exploited with the aim of making the robotic system aware of the ongoing process. The reported results, relative to a small training dataset, are nonetheless promising.
978-1-6654-8025-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2970839