Deploying collaborative robots in manufacturing presents diverse challenges. Rapid adaptability to the environment while ensuring user safety and engagement is paramount. Existing human-aware task sequencing solutions often lack explicit risk modeling and management. International standards emphasize severity, exposure, and avoidance as critical risk factors. To enhance intelligent risk awareness control, we propose integrating multiple risk factors into task sequencing models. This forms the basis for a cutting-edge planning framework-backed risk-aware task sequencing system. Our approach’s evaluation across various scenarios showcases its efficacy and adaptability to diverse risk levels. Experimental results show a positive equilibrium between productivity and safety, achieving both high throughput and low operator risk.
Risk-Aware Task Sequencing for Human-Robot Collaboration
Cesta A.;Orlandini A.
;Umbrico A.
2024
Abstract
Deploying collaborative robots in manufacturing presents diverse challenges. Rapid adaptability to the environment while ensuring user safety and engagement is paramount. Existing human-aware task sequencing solutions often lack explicit risk modeling and management. International standards emphasize severity, exposure, and avoidance as critical risk factors. To enhance intelligent risk awareness control, we propose integrating multiple risk factors into task sequencing models. This forms the basis for a cutting-edge planning framework-backed risk-aware task sequencing system. Our approach’s evaluation across various scenarios showcases its efficacy and adaptability to diverse risk levels. Experimental results show a positive equilibrium between productivity and safety, achieving both high throughput and low operator risk.| File | Dimensione | Formato | |
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ESAIM23 CR FINAL.pdf
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Descrizione: Bonini, A., Cesta, A., Cialdea Mayer, M., Orlandini, A., Umbrico, A. (2024). Risk-Aware Task Sequencing for Human-Robot Collaboration. In: Wagner, A., Alexopoulos, K., Makris, S. (eds) Advances in Artificial Intelligence in Manufacturing. ESAIM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://biblioproxy.cnr.it:2481/10.1007/978-3-031-57496-2_15
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