Distribution centers are facilities designed to store and manage different products to be redistributed to another location or directly to customers. Although these centers have a high degree of automation, tasks such as picking are hardly automatable and are one of the most labor-intensive and monotonous tasks in material handling operations. Picking, i.e. the process of collecting items to create a package for shipment, is the main source of errors and lack of efficiency. Although the fully automation of the picking task is highly desirable, multiple factors such as the high variability of parts or industrial requirements (safety, robustness...) limit the fully automation of the task. RSAII developed a hybrid order picking approach, in which robots and humans share the same workspace, combining high automation, flexibility and safety. The objectives defined in the project addressed key technical and industrial challenges and three experiments were performed with increasing complexity: (1) Free-style: Mono-reference picking with high availability, (2) Showcase: Safe multi-reference integrated with current pick to light solutions, (3) Field test: Collaborative solution for Unitary Picking in order preparation area of a Distribution Center. As a result of the Field test, it has been developed a working prototype that works in a realistic environment and represents the foundation for real implementation of the system at ULMA customers’ premises.


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    Titel :

    RSAII: Flexible Robotized Unitary Picking in Collaborative Environments for Order Preparation in Distribution Centers


    Weitere Titelangaben:

    Spr.Tracts in Adv.Robotics


    Beteiligte:
    Caccavale, Fabrizio (Herausgeber:in) / Ott, Christian (Herausgeber:in) / Winkler, Bernd (Herausgeber:in) / Taylor, Zachary (Herausgeber:in) / Susperregi, Loreto (Autor:in) / Fernandez, Ane (Autor:in) / Molina, Jorge (Autor:in) / Iriondo, Ander (Autor:in) / Sierra, Basilio (Autor:in) / Lazkano, Elena (Autor:in)


    Erscheinungsdatum :

    2020-02-07


    Format / Umfang :

    23 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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