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AI supported method to improve the work organization in human-robot-collaboration targeting on semi-autonomous group work

  • The production environment experiences copious challenges, but likewise discovers many new potential opportunities. To meet the new requirements, caused by the developments towards mass-customization, human-robot-cooperation (HRC) was identified as a key piece of technology and is becoming more and more important. HRC combines the strengths of robots, such as reliability, endurance and repeatability, with the strengths of humans, for instance flexibility and decision-making skills. Notwithstanding the high potential of HRC applications, the technology has not achieved a breakthrough in production so far. Studies have shown that one of the biggest obstacles for implementing HRC is the allocation of tasks. Another key technology that offers various opportunities to improve the production environment is Artificial Intelligence (AI). Therefore, this paper describes an AI supported method to improve the work organization in HRC in regards to the task-allocation. The aim of this method is to build a dynamic, semi-autonomous group work environment which keeps not just employee motivation at a high level, but also the product quality due to a decreased failure rate. The AI helps to detect the perfect condition in which the employee delivers the best performance and also supports at identifying the time when the worker leaves this optimal state. As soon as the employee reaches this trigger event, the allocation of the tasks adapts based on the identified stress. This adaptation aims to return the employee to the state of the optimal performance. In order to realize such a dynamic allocation, this method describes the creation of a pool with various interaction scenarios, as well as the AI supported recognition of the defined trigger event.

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Metadaten
Author of HS ReutlingenHummel, Vera; Euchner, Marc
URN:urn:nbn:de:bsz:rt2-opus4-33225
DOI:https://doi.org/10.2139/ssrn.3858372
Erschienen in:Proceedings of the Conference on Learning Factories (CLF) 2021, 1-2 July 2021, online
Publisher:Elsevier
Place of publication:Rochester, NY
Document Type:Conference proceeding
Language:English
Publication year:2021
Tag:AI; HRC; artifical intelligence; human-robot-collaboration; motivation level; task allocation; work organziation
Page Number:6
DDC classes:650 Management
Open access?:Ja
Licence (German):License Logo  Open Access