In this paper, we propose a graph-based kinetostatic state estimation refinement method for cable-driven parallel robots (CDPRs), addressing the challenges posed by cable sag in large-scale systems. CDPRs offer significant advantages, such as lower inertia, cost-effectiveness, and large workspaces; however, cable sag introduces inaccuracies that hinder precise control and pose estimation. Our method leverages a unified graph-based optimization framework to model and solve the coupled kinetostatic equations of CDPRs. To eliminate the need for force sensors, a dedicated force distribution optimizer estimates cable forces, which are used to refine the robot’s pose in subsequent iterations. Finite element (FE) simulations using RecurDyn validate the proposed approach, demonstrating its ability to achieve significant improvements in position and orientation accuracy while maintaining computational efficiency suitable for real-time applications.


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    Titel :

    Graph-Based Kinetostatic State Estimation in Cable-Driven Parallel Robots


    Weitere Titelangaben:

    Mechan. Machine Science


    Beteiligte:
    Lau, Darwin (Herausgeber:in) / Pott, Andreas (Herausgeber:in) / Bruckmann, Tobias (Herausgeber:in) / Dindarloo, Mohammadreza (Autor:in) / Hassani, Ali (Autor:in) / Sharifi, Ali (Autor:in) / Khalilpour, S. Ahmad (Autor:in) / Cardou, Philippe (Autor:in) / Taghirad, Hamid D. (Autor:in)

    Kongress:

    International Conference on Cable-Driven Parallel Robots ; 2025 ; Hong Kong July 08, 2025 - July 11, 2025


    Erschienen in:

    Cable-Driven Parallel Robots ; Kapitel : 2 ; 16-27


    Erscheinungsdatum :

    19.06.2025


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch