Progettazione e gestione di modelli interdisciplinari per celle robotiche collaborative

This thesis investigates the design and management of interdisciplinary models for collaborative robot cells within the Industry 5.0 paradigm, which emphasizes human-centric approaches. The research addresses the challenges of integrating collaborative robots (cobots) into modern production systems by focusing on multi-objective task allocation, safety constraints, and dynamic rescheduling mechanisms. The primary objectives are to enhance productivity, ensure operator well-being, and maintain safety standards in human-robot collaborative environments. A comprehensive literature review explores the historical development of collaborative robotics, the influence of human factors, and the evolution of production systems. The study presents a multi-objective task allocation model that optimizes productivity, energy expenditure, and mental workload. It also introduces a real-time 3D collision avoidance strategy using markerless motion capture systems to ensure safety without compromising performance. Two extensive experimental campaigns involving 20 participants each were conducted to validate the proposed models. Participants performed tasks under static and dynamic task allocations, with and without rescheduling mechanisms. The results demonstrate that dynamic rescheduling significantly reduces cognitive workload and maintains productivity, highlighting the importance of adaptive task management in collaborative settings. This thesis contributes to the advancement of Industry 5.0 by proposing innovative solutions that bridge technological advancements and human-centric design. The findings emphasize the critical role of real-time feedback and adaptive mechanisms in optimizing collaborative robot systems, ultimately enhancing both human and robotic performance in modern manufacturing environments.