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The Industry 4.0 paradigm promises shorter development times, increasedergonomy, higher flexibility, and resource efficiency in manufacturingenvironments. Collaborative robots are an important tangible technology forimplementing such a paradigm. A major bottleneck to effectively deploycollaborative robots to manufacturing industries is developing task planningalgorithms that enable them to recognize and naturally adapt to varying andeven unpredictable human actions while simultaneously ensuring an overallefficiency in terms of production cycle time. In this context, an architectureencompassing task representation, task planning, sensing, and robot control hasbeen designed, developed and evaluated in a real industrial environment. Apick-and-place palletization task, which requires the collaboration betweenhumans and robots, is investigated. The architecture uses AND/OR graphs forrepresenting and reasoning upon human-robot collaboration models online.Furthermore, objective measures of the overall computational performance andsubjective measures of naturalness in human-robot collaboration have beenevaluated by performing experiments with production-line operators. The resultsof this user study demonstrate how human-robot collaboration models like theone we propose can leverage the flexibility and the comfort of operators in theworkplace. In this regard, an extensive comparison study among recent modelshas been carried out.

Deployment and Evaluation of a Flexible Human-Robot Collaboration Model Based on AND/OR Graphs in a Manufacturing Environment