Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20791
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: A mixed-perception approach for safe human–robot collaboration in industrial automation
Authors: Mohammadi Amin, Fatemeh
Rezayati, Maryam
van de Venn, Hans Wernher
Karimpour, Hossein
et. al: No
DOI: 10.3390/s20216347
10.21256/zhaw-20791
Published in: Sensors
Volume(Issue): 20
Issue: 21
Pages: 6347
Issue Date: 2020
Publisher / Ed. Institution: MDPI
ISSN: 1424-8220
Language: English
Subjects: Safe physical human–robot collaboration; Collision detection; Human action recognition; Artificial intelligence; Industrial automation
Subject (DDC): 006: Special computer methods
621.3: Electrical, communications, control engineering
Abstract: 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/20791
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Mechatronic Systems (IMS)
Appears in collections:Publikationen School of Engineering

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