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Article

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

1
Institute of Mechatronics System, Zurich University of Applied Science, 8400 Winterthur, Switzerland
2
Mechanical Engineering Department, University of Isfahan, Isfahan 81746-73441, Iran
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6347; https://doi.org/10.3390/s20216347
Received: 4 September 2020 / Revised: 29 October 2020 / Accepted: 2 November 2020 / Published: 7 November 2020
(This article belongs to the Special Issue Human-Robot Collaborations in Industrial Automation)
Digital-enabled manufacturing systems require a 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; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception. 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 physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception 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. View Full-Text
Keywords: safe physical human–robot collaboration; collision detection; human action recognition; artificial intelligence; industrial automation safe physical human–robot collaboration; collision detection; human action recognition; artificial intelligence; industrial automation
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MDPI and ACS Style

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. https://doi.org/10.3390/s20216347

AMA Style

Mohammadi Amin F, Rezayati M, van de Venn HW, Karimpour H. A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation. Sensors. 2020; 20(21):6347. https://doi.org/10.3390/s20216347

Chicago/Turabian Style

Mohammadi Amin, Fatemeh; Rezayati, Maryam; van de Venn, Hans W.; Karimpour, Hossein. 2020. "A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation" Sensors 20, no. 21: 6347. https://doi.org/10.3390/s20216347

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