Tension–path co-optimization of cable-driven manipulators based on a digital twin framework

Cable-driven manipulators, characterized by their hyper-redundant degrees of freedom and exceptional flexibility, present significant potential for performing complex tasks. However, traditional approaches face challenges in concurrently addressing path planning and tension distribution, often leading to reduced control accuracy and compromised system stability. This study introduces a Digital Twin-based framework for tension–path co-optimization. A kinematic model of the manipulator and a cable tension model are developed in a virtual environment. By integrating physical modeling with data-driven techniques, the framework enables accurate simulation of the manipulator's motion and cable tension distribution. A gradient descent optimization method is employed to simultaneously optimize tension distribution and the motion path. To ensure high-precision closed-loop control between the virtual and physical spaces, a self-compensating optical fiber angle sensor feedback mechanism is implemented, effectively minimizing joint angle errors. The proposed methodology is comprehensively validated through Digital Twin simulations and experimental testing, with a focus on tension prediction accuracy, path optimization efficacy, and control precision. The results demonstrate that the proposed approach outperforms traditional models in terms of tension prediction accuracy, path optimization, and joint angle error reduction, exhibiting superior precision and stability under various stiffness settings.