Ergonomic design of Human-Robot collaborative workstation in the Era of Industry 5.0

• A mathematical model to speed up the ergonomic design phase and resource allocation in collaborative workstations. • A database of real values of fatigue level and ergonomic postural risk scores for collaborative tasks monitored. • Experimental test cases for collaborative workstation design in laboratory settings. • Implications of workstation design on strategic, tactical, and operational decisions. The increasing adoption of collaborative robots to support job execution in manufacturing has catalyzed companies’ attention to safety and well-being issues. Sharing the human-centric perspective and harmonious human–machine collaboration concepts emphasized by Industry 5.0, the design phase of a collaborative workstation must integrate both psychological and physical risk evaluations to provide a safe and inclusive work environment suitable for a diversified workforce. Accelerating the pre-deployment phase to quickly reconfigure workstation design and assess its impact on workload balancing and task sequencing during the deployment of assembly lines still represents a challenging task considering the available software tools. This research proposes a new mathematical model to accelerate the design of ergonomic human-robot collaborative workstations based on task alternatives and the combined consideration of postural assessment and fatigue analyses for each of them to design an ergo-friendly collaborative environment. Surface electromyography analysis is jointly adopted with postural risk assessment measured with inertial measurement units and developed by a digital ergonomic platform to determine the optimal workplace configuration for tools, equipment, and resources to promote physical well-being while considering station productivity. Experimental tests are performed to investigate arm muscles and postural risk assessment for different configurations of workstation design and collaborative human-robot job progression. Experimental results demonstrate the feasibility, and the advantages of the proposed approach compared to existing simulation software to quickly generate and assess alternative scenarios and find a trade-off between ergo-quality levels and system performance. The final discussion offers valuable information for decision-makers and practitioners to facilitate the integration of human factors throughout the early stages of ergo-friendly workspace design, while effectively managing the complexity generated by resource allocation and collaborative robots.