Fault-tolerant path planning for cable-driven manipulators based on a diffusion strategy

Cable-driven manipulators often perform tasks in radiation, toxic, high-temperature and high-pressure environments. In such conditions, cable wear or failure cannot be addressed on-site. To address operational safety issues arising from cable abrasion in these scenarios, this study proposes a physics-informed diffusion policy framework for fault-tolerant path planning of cable-driven manipulators. Given the hyper-redundant degrees of freedom in cable-driven manipulators, the system can redistribute loads and continue functioning despite cable damage. First, a virtual-work-based cable tension estimation network embedding physical constraints is developed to achieve high-accuracy tension prediction corresponding to joint postures. Subsequently, tension guidance is integrated into the denoising diffusion model, leveraging collision-free reference trajectories as priors, thus iteratively generating fault-tolerant trajectories in high-dimensional joint spaces that satisfy both low-tension and obstacle avoidance constraints. Multidimensional experiments evaluating tension prediction accuracy, path feasibility, and algorithm scalability demonstrate that the proposed method effectively reduces average tension in damaged cables while ensuring path safety, achieving a planning time of only 9.7 s. Furthermore, the method maintains stable convergence and low jitter even with up to three damaged cables. Experimental results validate the algorithm's effectiveness in fault-tolerant path planning for cable-driven manipulators, presenting a promising new paradigm for continuous and reliable operations in extreme environments.