一种面向空间非合作目标的强化学习多臂协同俘获策略研究

In order to solve the problem of target adaptability and complexity of capture action planning in space non-cooperative target clearing tasks，an envelope capture strategy based on reinforcement learning combined with multi-arm group coordination mechanism is proposed. Firstly，the physical model and kinematic model of the multi-arm trap mechanism are constructed，and then the reinforcement learning controller is designed by using the soft-actor-critic（SAC）algorithm and introducing pretraining （PT） method. Then the reward function is designed based on the“multi-arm grouping cooperation”reward mechanism to train the optimal capture action. In order to verify the high efficiency of capture strategy for single target operation and high adaptability for multi-target operation，simulation experiments are carried out on various targets respectively. Simulation results show that the proposed capture strategy can capture targets of various configurations efficiently and adaptively.