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Concept of a Self-Learning Workplace Cell for Worker Assistance While Collaboration with a Robot Within the Self-Adapting-Production-Planning-System

Ender, J, Wagner, JC, Kunert, G, Guo, FB, Larek, R and Pawletta, T (2019) Concept of a Self-Learning Workplace Cell for Worker Assistance While Collaboration with a Robot Within the Self-Adapting-Production-Planning-System. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska,, 9 (4). pp. 4-9. ISSN 2083-0157


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For some time, the focus of past research on industrial workplace designs has been the optimization of processes from the technological point of view. Since human workers have to work within this environment the design process must regard Human Factor needs. The operators are under additional stress due to the range of high dynamic processes and due to the integration of robots and autonomous operating machines. There have been few studies on how Human Factors influence the design of workplaces for Human-Robot Collaboration (HRC). Furthermore, a comprehensive, systematic and human-centred design solution for industrial workplaces particularly considering Human Factor needs within HRC is widely uncertain and a specific application with reference to production workplaces is missing. The research findings described in this paper aim the optimization of workplaces for manual production and maintenance processes with respect to the workers within HRC. In order to increase the acceptance of integration of human-robot teams, the concept of the Assisting-Industrial-Workplace-System (AIWS) was developed. As a flexible hybrid cell for HRC integrated into a Self-Adapting-Production-Planning-System (SAPPS) assists the worker while interaction.

Item Type: Article
Uncontrolled Keywords: human-robot collaboration; human factors; post-optimised reinforcement learning algorithm; self-adapting-production-planning-system
Subjects: T Technology > T Technology (General)
Divisions: Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher: Lublin University of Publishing House
Date Deposited: 08 Jan 2020 10:10
Last Modified: 04 Sep 2021 08:13
DOI or ID number: 10.35784/iapgos.36
URI: https://researchonline.ljmu.ac.uk/id/eprint/11969
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