Komenda, T., Schmidbauer, C., Kames, D., & Schlund, S. (2021). Learning to Share - Teaching the Impact of Flexible Task Allocation in Human-cobot Teams. In Proceedings of the Conference on Learning Factories (CLF) 2021. 11th Conference on Learning Factories (CLF 2021), Graz, Austria. https://doi.org/10.2139/ssrn.3869551
Collaborative robots (cobots) are vital for agile concepts of industrial manufacturing and regarded as flexible and low-cost automation solutions. Therefore, learning factory concepts increasingly include handling with cobot technology, their industrial applications and safety concepts. In addition to these fields of knowledge, it is widely considered that work organization at the manufacturing shop floor must be rethought to use the full potential of cobot flexibility. One of the approaches is the flexible task allocation in human-cobot teams. Based on the idea of self-organization, human and cobot tasks are adaptively assessed and optimized. The implementation of this concept enables productivity gains and at the same time reduces negative aspects of automation such as reduced readiness to take action and monotonous work. As this approach requires technological and organizational preconditions as well as prior knowledge of the industrial engineering domain, the paper presents a hands-on educational concept for flexible task allocation in human-cobot teams. Upon an analysis of required competencies and an identification of deficits in existing teaching approaches - based on evaluation criteria such as innovation content, human-cobot team suitability, industrial relevance and reusability - a teaching concept in addition to three hands-on demonstrators was developed. The demonstrators were implemented at the TU Wien Pilot Factory Industry 4.0 allowing for representative task allocation patterns applied in industrial settings. Besides professional educational purposes, the demonstrators can be used for exhibitions as they allow hands-on experiences for the public and therefore contribute to a more realistic perception of the potential, the challenges, and the impact of robots in the manufacturing domain. Thus, working with and evaluating the demonstrators hands-on, not only students and professionals, but also children, teenagers and the general public are able to explore the potential benefits, consequences and necessary preconditions within potential use cases.
