The development of a human factors tool for the successful implementation of industrial human-robot collaboration - Charalambous, George (Cranfield University, 2014)
Manufacturing organisations have placed significant attention to the potential of industrial human-robot collaboration (HRC) as a means for enhancing productivity and product quality. This concept has predominantly been seen from an engineering and safety aspect, while the human related issues tend to be disregarded. As the key human factors relevant to industrial HRC have not yet been fully investigated, the research presented in this thesis sought to develop a human factors tool to enable the successful implementation of industrial HRC. First, a theoretical framework was developed which collected the key organisational and individual level human factors by reviewing comparable contexts to HRC. The human factors at each level were investigated separately. To identify whether the organisational human factors outlined in the theoretical framework were enablers or barriers, an industrial exploratory case study was conducted where traditional manual work was being automated. The implications provided an initial roadmap of the key organisational human factors that need to be considered as well as the critical inter-relations between them. From the list of individual level human factors identified in the theoretical framework, the focus was given on exploring the development of trust between human workers and industrial robots. A psychometric scale that measures trust specifically in industrial HRC was developed. The scale offers the opportunity to system designers to identify the key system aspects that can be manipulated to optimise trust in industrial HRC. Finally, the results were gathered together to address the overall aim of the research. A human factors guidance tool was developed which provides practitioners propositions to enable successful implementation of industrial HRC.
Keywords: Organisation; workforce acceptance; trust; scale; scale development; qualitative analysis; quantitative analysis; factor analysis; reliability analysis