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Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration

  • Human–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implementHuman–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human–machine differentiation to the speed and separation monitoring in human–robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.zeige mehrzeige weniger

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Metadaten
Dokumentart:Zeitschriftenartikel, wissenschaftlich
Review-Status:Begutachtet (reviewed)
Zitierlink: https://opus.hs-offenburg.de/5305
Bibliografische Angaben
Titel (Englisch):Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration
Verfasserangaben:Urban HimmelsbachStaff MemberORCiD, Thomas WendtStaff MemberORCiDGND, Nikolai HangstStaff MemberORCiDGND, Philipp GawronStaff MemberORCiDGND, Lukas StiglmeierStaff MemberORCiD
Erscheinungsjahr:2021
Verlagsort:Basel
Verlag:MDPI
Seitenanzahl:18
Erste Seite:1
Letzte Seite:18
Aufsatznummer:7144
Titel des übergeordneten Werkes (Englisch):Sensors
Herausgeber*in:Anne Schmitz
Jahrgang (Band):21
Heft (Ausgabe):21
ISSN:1424-8220
DOI:https://doi.org/10.3390/s21217144
URN:https://urn:nbn:de:bsz:ofb1-opus4-53052
Sprache:Englisch
Inhaltliche Informationen
Fakultäten / Einrichtungen:Fakultät Wirtschaft (W)
Forschung:WLRI - Work-Life Robotics Institute
Sammlungen der Hochschule Offenburg:Bibliografie
Formale Angaben
Open-Access-Status: Open Access 
Lizenz (Deutsch):License LogoUrheberrechtlich geschützt