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In recent years, the numer of hybrid work systems using human robot collaboration (HRC) increased in industrial production environments - enhancing productivity while reducing work-related burden. Despite growing availability of HRC-suitable manipulation and safety technology, tools and techniques facilitating the design, planning and implementation process are still lacking. System engineers who strive to implement technically feasible, ergonomically meaningful and economically beneficial HRC application need to make design and technology decisions in various subject areas, whereas the design alternatives per morphological analysis is applied to establish a description model that can serve as both a supporting design guideline for future HRC application of value-adding, industrial quality as well as a tool to characterize and compare existing applications. It focuses on HRC within assembly processes, and illustrates the complexity of HRC applications in a comprehensible manner through its multi-dimensional structure. The morphology has been validated through its application on various existing industrial HRC applications, research demonstrators and interviews of experts from academia.
While academia and industry see large potential for human-robot collaboration (HRC), only a small number of realized HRC application is currently found in industry. To gather more data about current hindrances to wider implementation of collaborative robots, a study among 15 robot manufactureres and 14 system integrators of collaborative robot technology has been conducted through a predesigned questionnaire procedure. Additionally, five industrial users of human-robot collaboration have been interviewed on the main challenges they experienced during the initial implementation process. The quantitative data has been analyzed using the Wilcoxon-Signed-Rank-Test. Accoring to the study participants, the main challenges within the implementation currently are the identification of HRC-suitable processes, the application of relevant safety norms (such as ISO 10218, ISO/TS 15066) and the application-individual risk assessment.
After the initiator of the ESB Logistics Learning Factory, Prof. Vera Hummel had made experience in developing and implementing a concept for a Learning Factory for Advanced Industrial Engineering (aIE) at the University of Stuttgart, Institute IFF between 2005 and 2008, she was appointed as a full professor at ESB Business School, a faculty of Reutlingen University in March 2010. Lacking a realistic, hands on learning and teaching environment of industrial scale for its industrial engineering students, first ideas for a Learning Factory that would strongly focus on all aspects of production logistics were drafted in 2012. Already back then, a strong integration of virtual and physical factory was desired: While the Learning Factory itself would be physical, the neighboring partners along the supply chain, such as suppliers or distribution warehouses, could be added in a fully virtual way. Considering implementation of the ESB Logistics Learning Factory a strategic initiative of the university, initial funding was provided by the faculty ESB Business School itself. Following its own creed, to provide future-oriented training for the region, also primarily local suppliers and manufacturers were selected as equipment providers to the new Learning Factory. During the initialization phase, 2014, a total of three researchers and nine students worked approximately four months to set up a first assembly line, storage racks, AGVs, or pick-by-light systems in conjunction with the underlying didactical concept. Since then, several hundred of students have participated in trainings and lectures held in the ESB Logistics Learning Factory, several research projects were carried out, and multiple high-level politicians and industry executives have been touring the shop floor. Also, more than EUR 2 million in research and infrastructure funds could be secured for expansion and upgrade — allowing the ESB Logistics Learning Factory today to represent many core aspects of an Industrie 4.0 production environment.