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 monitori...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/21/7144 |
| _version_ | 1797511806446469120 |
|---|---|
| author | Urban B. Himmelsbach Thomas M. Wendt Nikolai Hangst Philipp Gawron Lukas Stiglmeier |
| author_facet | Urban B. Himmelsbach Thomas M. Wendt Nikolai Hangst Philipp Gawron Lukas Stiglmeier |
| author_sort | Urban B. Himmelsbach |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-03-10T05:53:15Z |
| format | Article |
| id | doaj.art-c43ab670b9fb4eafb536c1981a4c5f38 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:15Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c43ab670b9fb4eafb536c1981a4c5f382023-11-22T21:37:14ZengMDPI AGSensors1424-82202021-10-012121714410.3390/s21217144Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot CollaborationUrban B. Himmelsbach0Thomas M. Wendt1Nikolai Hangst2Philipp Gawron3Lukas Stiglmeier4Work-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, GermanyWork-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, GermanyWork-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, GermanyWork-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, GermanyWork-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, GermanyHuman–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.https://www.mdpi.com/1424-8220/21/21/7144human–robot collaborationspeed and separation monitoringhuman–machine differentiationthermal camerasprotective separation distance |
| spellingShingle | Urban B. Himmelsbach Thomas M. Wendt Nikolai Hangst Philipp Gawron Lukas Stiglmeier Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration Sensors human–robot collaboration speed and separation monitoring human–machine differentiation thermal cameras protective separation distance |
| title | Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration |
| title_full | Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration |
| title_fullStr | Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration |
| title_full_unstemmed | Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration |
| title_short | Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration |
| title_sort | human machine differentiation in speed and separation monitoring for improved efficiency in human robot collaboration |
| topic | human–robot collaboration speed and separation monitoring human–machine differentiation thermal cameras protective separation distance |
| url | https://www.mdpi.com/1424-8220/21/21/7144 |
| work_keys_str_mv | AT urbanbhimmelsbach humanmachinedifferentiationinspeedandseparationmonitoringforimprovedefficiencyinhumanrobotcollaboration AT thomasmwendt humanmachinedifferentiationinspeedandseparationmonitoringforimprovedefficiencyinhumanrobotcollaboration AT nikolaihangst humanmachinedifferentiationinspeedandseparationmonitoringforimprovedefficiencyinhumanrobotcollaboration AT philippgawron humanmachinedifferentiationinspeedandseparationmonitoringforimprovedefficiencyinhumanrobotcollaboration AT lukasstiglmeier humanmachinedifferentiationinspeedandseparationmonitoringforimprovedefficiencyinhumanrobotcollaboration |