ProMentoHR
Assessing Cognitive Workload in Human-Robot Collaboration and Cooperative Assembly Systems
Collaborative robotics is a vital technology in Industry 4.0, particularly in the rapidly expanding market of collaborative assembly. This area of industrial human-robot interaction (HRI) not only focuses on mechanical safety but increasingly recognizes the importance of cognitive ergonomics and human factors, which are critical for operator safety, well-being, and performance. Addressing these aspects requires a human-centered approach to developing Collaborative Assembly Systems (CASs), highlighting the need for interdisciplinary research.
The project PromentorHR aims to enhance human factors in industrial HRI by developing and validating a new cognitive workload (CWL) assessment method, building on the Cognitive Load Assessment for Manufacturing model (CLAM) proposed by Thorvald et al. in 2017. The updated method, named “ProMentoHR,” seeks to minimize subjective biases in CAS design and specifically address how design elements affect operator CWL.
Additionally, the project will develop a practical tool based on the enhanced CLAM methodology, intended for use by assembly system experts who may not specialize in human factors. This tool will undergo experimental validation through a case study in a realistic setting, ensuring it effectively integrates cognitive requirements into human-centered CASs.
ProMentoHR aims to improve operators’ working conditions, safety, and well-being, while also enhancing production performance. The project will utilize both qualitative and quantitative methods and maintain a user-centered design approach. Feedback and recommendations will be collected through surveys and interviews with experts in manufacturing systems, robotics, and human factors. This input will inform the development of the methodology and the integration of cognitive ergonomics into CAS features and HRI patterns.
Ultimately, the project focuses on advancing theoretical understanding and practical applications of how operators’ CWL can be managed in manufacturing settings, aiming to improve worker conditions and optimize the effectiveness of collaborative robotics in industrial environments.
