Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Mixed Perception Approach for Safe Human-Robot Collaboration in Industrial Automation

Version 1 : Received: 4 September 2020 / Approved: 5 September 2020 / Online: 5 September 2020 (05:29:53 CEST)
Version 2 : Received: 25 October 2020 / Approved: 26 October 2020 / Online: 26 October 2020 (11:31:02 CET)
Version 3 : Received: 4 November 2020 / Approved: 5 November 2020 / Online: 5 November 2020 (11:08:19 CET)

A peer-reviewed article of this Preprint also exists.

Mohammadi Amin, F.; Rezayati, M.; van de Venn, H.W.; Karimpour, H. A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation. Sensors 2020, 20, 6347. Mohammadi Amin, F.; Rezayati, M.; van de Venn, H.W.; Karimpour, H. A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation. Sensors 2020, 20, 6347.

Journal reference: Sensors 2020, 20, 6347
DOI: 10.3390/s20216347

Abstract

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings. This issue is addressed in this work by implementing a reliable system for real-time safe human-robot collaboration based upon the combination of human action and contact type detection systems. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and robot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, lead to the enhancement of human safety and an increase of the level of robot awareness about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Subject Areas

Safe physical Human-Robot Collaboration; collision detection; human action recognition; artificial intelligence; industrial automation

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