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.


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    Titel :

    Risk-Aware Task Sequencing for Human-Robot Collaboration


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Wagner, Achim (Herausgeber:in) / Alexopoulos, Kosmas (Herausgeber:in) / Makris, Sotiris (Herausgeber:in) / Bonini, Alex (Autor:in) / Cesta, Amedeo (Autor:in) / Cialdea Mayer, Marta (Autor:in) / Orlandini, Andrea (Autor:in) / Umbrico, Alessandro (Autor:in)

    Kongress:

    European Symposium on Artificial Intelligence in Manufacturing ; 2023 ; Kaiserslautern, Germany September 19, 2023 - September 19, 2023



    Erscheinungsdatum :

    27.06.2024


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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