This paper presents an algorithm for the forward kinematics and online self-calibration of cable-driven parallel robots. Covariance-based metrics known as the position dilution of precision (PDOP) and orientation dilution of precision (ODOP) are introduced as a means to quantify the quality of data collected with regards to self-calibration. These metrics enable systematic pruning of the data used for self-calibration and an assessment of when sufficiently rich data has been collected to perform self-calibration. The proposed algorithm is demonstrated through inverse-kinematics- and dynamics-based numerical simulations.


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    Titel :

    Forward Kinematics and Online Self-calibration of Cable-Driven Parallel Robots with Covariance-Based Data Quality Assessment


    Weitere Titelangaben:

    Mechan. Machine Science


    Beteiligte:
    Caro, Stéphane (Herausgeber:in) / Pott, Andreas (Herausgeber:in) / Bruckmann, Tobias (Herausgeber:in) / Caverly, Ryan J. (Autor:in) / Bunker, Keegan (Autor:in) / Patel, Samir (Autor:in) / Nguyen, Vinh L. (Autor:in)

    Kongress:

    International Conference on Cable-Driven Parallel Robots ; 2023 ; Nantes, France June 25, 2023 - June 28, 2023


    Erschienen in:

    Cable-Driven Parallel Robots ; Kapitel : 30 ; 369-380


    Erscheinungsdatum :

    03.06.2023


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch