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Depth camera based collision avoidance via active robot control
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-8906-630X
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, 100 44 Stockholm, Sweden. (Produktion och automatiseringsteknik, Production and Automation Engineering)
2014 (English)In: Journal of manufacturing systems, ISSN 0278-6125, Vol. 33, no 4, 711-718 p.Article in journal (Refereed) Published
Abstract [en]

A new type of depth cameras can improve the effectiveness of safety monitoring in human–robot collaborative environment. Especially on today's manufacturing shop floors, safe human–robot collaboration is of paramount importance for enhanced work efficiency, flexibility, and overall productivity. Within this context, this paper presents a depth camera based approach for cost-effective real-time safety monitoring of a human–robot collaborative assembly cell. The approach is further demonstrated in adaptive robot control. Stationary and known objects are first removed from the scene for efficient detection of obstacles in a monitored area. The collision detection is processed between a virtual model driven by real sensors, and 3D point cloud data of obstacles to allow different safety scenarios. The results show that this approach can be applied to real-time work cell monitoring.

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 33, no 4, 711-718 p.
Keyword [en]
Depth camera, Monitoring, Collision avoidance, Human–robot collaboration
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-9376DOI: 10.1016/j.jmsy.2014.04.004ISI: 000348883200024ScopusID: 2-s2.0-84919463262OAI: oai:DiVA.org:his-9376DiVA: diva2:722564
Note

Available online 21 May 2014

Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2015-08-25Bibliographically approved

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Schmidt, BernardWang, Lihui
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