A structured methodology to support human–robot collaboration configuration choice

Human–robot collaboration (HRC) is a gradually consolidating paradigm of the modern industry which combines human and robot skills to make production more flexible. Since the effective implementation of HRC requires a careful analysis of different aspects, related both to robots and humans, there is a real need for a structured methodology to support it. A previous work proposed a multi-dimensional framework to analyze several HRC aspects of a collaborative task. However, identifying the configuration that better exploits the HRC potential is not always trivial, especially among multiple alternative solutions. In addition, the priority levels (weights) assigned to the individual sub-dimensions of the framework, which identify specific design strategies, do not appear explicitly. The goal of this paper is to address these gaps, expanding the previous methodology and proposing the introduction of a multiple-criteria decision analysis (MCDA) method (i.e., ELECTRE-II). The inclusion of a MCDA method allows designers to: (i) express importance weights for each sub-dimension of the framework, and (ii) generate a preference ranking through a structured comparison of alternative HRC configurations. The description is supported by a real industrial application in the automotive field, in which four alternative HRC configurations are analyzed by a team of experts providing a holistic analysis.