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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20791
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dc.contributor.authorMohammadi Amin, Fatemeh-
dc.contributor.authorRezayati, Maryam-
dc.contributor.authorvan de Venn, Hans Wernher-
dc.contributor.authorKarimpour, Hossein-
dc.date.accessioned2020-11-12T13:03:27Z-
dc.date.available2020-11-12T13:03:27Z-
dc.date.issued2020-
dc.identifier.issn1424-8220de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20791-
dc.description.abstractDigital-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.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofSensorsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectSafe physical human–robot collaborationde_CH
dc.subjectCollision detectionde_CH
dc.subjectHuman action recognitionde_CH
dc.subjectArtificial intelligencede_CH
dc.subjectIndustrial automationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleA mixed-perception approach for safe human–robot collaboration in industrial automationde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Mechatronische Systeme (IMS)de_CH
dc.identifier.doi10.3390/s20216347de_CH
dc.identifier.doi10.21256/zhaw-20791-
zhaw.funding.euNode_CH
zhaw.issue21de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start6347de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume20de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Mohammadi Amin, F., Rezayati, M., van de Venn, H. W., & Karimpour, H. (2020). A mixed-perception approach for safe human–robot collaboration in industrial automation. Sensors, 20(21), 6347. https://doi.org/10.3390/s20216347
Mohammadi Amin, F. et al. (2020) ‘A mixed-perception approach for safe human–robot collaboration in industrial automation’, Sensors, 20(21), p. 6347. Available at: https://doi.org/10.3390/s20216347.
F. Mohammadi Amin, M. Rezayati, H. W. van de Venn, and H. Karimpour, “A mixed-perception approach for safe human–robot collaboration in industrial automation,” Sensors, vol. 20, no. 21, p. 6347, 2020, doi: 10.3390/s20216347.
MOHAMMADI AMIN, Fatemeh, Maryam REZAYATI, Hans Wernher VAN DE VENN und Hossein KARIMPOUR, 2020. A mixed-perception approach for safe human–robot collaboration in industrial automation. Sensors. 2020. Bd. 20, Nr. 21, S. 6347. DOI 10.3390/s20216347
Mohammadi Amin, Fatemeh, Maryam Rezayati, Hans Wernher van de Venn, and Hossein Karimpour. 2020. “A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation.” Sensors 20 (21): 6347. https://doi.org/10.3390/s20216347.
Mohammadi Amin, Fatemeh, et al. “A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation.” Sensors, vol. 20, no. 21, 2020, p. 6347, https://doi.org/10.3390/s20216347.


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