Practical Finite-Time Motion Planning for Spacecraft-Mounted Soft Manipulators under Dynamic Obstacles

Soft robots, owing to their inherent compliance and adaptability, are particularly well suited for on-orbit applications. However, motion planning for such robots in space environments poses considerable challenges, e.g., navigating through cluttered orbital debris and accounting for microgravity-induced dynamic behaviors during obstacle avoidance. This article presents a motion planning framework tailored for spacecraft-mounted soft manipulators (SMSMs). A generalized practical finite-time stable optimization algorithm is proposed to enable efficient and robust planning. By leveraging strict convexification and discretization, the method achieves rapid convergence while accommodating the complex dynamics and morphological characteristics of SMSMs. To further enhance autonomy, a generative adversarial network integrated with long short-term memory is developed to learn intricate state-control mappings. This deep learning architecture captures temporal dependencies to improve the quality of control policies and reduce reliance on manual intervention or replanning. The effectiveness of all proposed methods is validated through simulations.