An efficient hybrid motion planning framework for redundant space manipulators in constrained environments

Space robotic manipulators are crucial for on-orbit servicing tasks like satellite maintenance and debris removal. However, the complex environment poses major challenges, especially obstacle avoidance and self-collision risks under joint constraints. To address these challenges, this paper proposes a hybrid motion planning framework for 7-degree-of-freedom (7-DOF) Space Station Remote Manipulator System (SSRMS) type manipulators to complete the grasping tasks in constrained orbital environments. First, the extended manipulability is introduced to evaluate inverse kinematics (IK) solutions by integrating manipulability ellipsoid analysis, joint limit proximity, and obstacle distance. Subsequently, the proposed Wrapping-based Informed RRT∗-Connect (WIRRT∗-Connect) algorithm efficiently generates optimal collision-free paths in high-dimensional configuration space. Finally, a minimum-jerk trajectory generation method ensures smooth and dynamically feasible motion while accounting for system constraints. The effectiveness of the proposed framework is validated through simulations in ADAMS under two representative on-orbit scenarios. Compared with the conventional sample-based motion planning methods discussed in this paper, the proposed approach demonstrates significant improvements in computational efficiency and path optimality, while ensuring dynamically feasible trajectories.