Master's thesis in Mechatronics (MAS500) ; The industry standards and capability are constantly advancing and pushing forward to increase data collection, efficiency, profit, and quality as well as decrease downtime, injuries, and hazards as much as possible. In recent years, robot systems have received more attention in the context of a large number of industrial applications, such as automotive manufacturing, additive manufacturing, assembly, quality inspection, and co-packing. The collaboration between multiple robots and human operators is considered to be the most prominent strategy in Industry 4.0 and future Industry 5.0, sharing the same space and collaborating on tasks according to their complementary capabilities. With the use of robots and their abilities could efficiency, profit, safety, and quality be further increased, potentially revolutionizing the industry and production. This project was supported in part by DEEPCOBOT Project. DEEPCOBOT, Collective Efficient Deep Learning and Networked Control for Multiple Collaborative Robot Systems, are a research project funded by IKTPLUSS under Grant 306640/O70 from the Research Council of Norway. The project will investigate the design of a new generation of decentralized data-driven Deep Learning based controllers for multiple coexisting collaborative robots, which interact both between them-selves and with human operators in order to collectively learn from each other’s experiences and perform cooperatively different complex tasks in large-scale industrial environments. This is motivated by the increasing demand of automation in industry, especially the demand of a safer and more efficient collaboration between multiple robots and human operators to integrate the best of human abilities and robotic automation.


    Access

    Download


    Export, share and cite



    Title :

    Model-free object grasping : Model-free object grasping with a learning-free approach


    Contributors:

    Publication date :

    2021-01-01


    Remarks:

    73


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    Object Grasping Device and Manufacturing Method for Object Grasping Device

    KOOK DONG HOON | European Patent Office | 2018

    Free access


    Stereo-vision-guided object grasping

    Nguyen,M.C. / Graefe,V. / Univ.d.Bundeswehr Muenchen,DE | Automotive engineering | 1999


    Stereo-Vision-Guided Object Grasping

    Nguyen, M.-C. / Graefe, V. / International Symposium on Automotive Technology and Automation | British Library Conference Proceedings | 1999


    Unknown Object Grasping for Assistive Robotics

    Miller, Elle / Durner, Maximilian / Humt, Matthias et al. | ArXiv | 2024

    Free access