Reinforcement Learning-Based Pose Coordination Planning Capture Strategy for Space Non-Cooperative Targets

During the process of capturing non-cooperative targets in space, space robots have strict constraints on the position and orientation of the end-effector. Traditional methods typically focus only on the position control of the end-effector, making it difficult to simultaneously satisfy the precise requirements for both the capture position and posture, which can lead to failed or unstable grasping actions. To address this issue, this paper proposes a reinforcement learning-based capture strategy learning method combined with posture planning. First, the structural models and dynamic models of the capture mechanism are constructed. Then, an end-to-end decision control model based on the Optimistic Actor-Critic (OAC) algorithm and integrated with a capture posture planning module is designed. This allows the strategy learning process to reasonably plan the posture of the end-effector to adapt to the complex constraints of the target capture task. Finally, a simulation test environment is established on the Mujoco platform, and training and validation are conducted. The simulation results demonstrate that the model can effectively approach and capture multiple targets with different postures, verifying the effectiveness of the proposed method.