航天器反应式碎片规避动作规划方法

提出一种航天器反应式碎片规避动作规划方法，首先以扰动流体动态系统(IFDS)算法作为动作规划的基础算法，通过其中的总和扰动矩阵对航天器的轨道速度矢量进行修正，实现轨道机动规避；然后，建立基于双延迟深度确定性策略梯度(TD3)深度强化学习算法的反应式动作规划方法，通过TD3在线优化IFDS规划参数，实现对碎片群的“状态-动作”最优、快速规避决策。在此基础上，将优先级经验回放和渐进式学习策略引入该方法中，提升训练效率。最后，仿真结果表明，所提方法可使航天器安全规避多发、突发、动态且形状各异的空间碎片群，且具有较好的实时性。

Spacecraft Reactive Collision Avoidance Action Planning Method for Space Debris

A method of reactive debris avoidance action planning for spacecraft is proposed. Firstly, the interfered fluid dynamical system (IFDS) is taken as the fundamental action planning algorithm, and the orbital velocity vector is modified by the total interfered matrix in the IFDS to achieve the orbital maneuver for collision avoidance. Then, a reactive action planning method based on a deep reinforcement learning algorithm called the twin delayed deep deterministic policy gradient (TD3) is established. By introducing the TD3 to optimize the IFDS planning parameters online, the “state action” response that balances optimality and rapidity for space debris clusters can be realized. On this basis, the strategies of the prioritized experience replay and progressive learning are introduced into the method to improve the training efficiency. Finally, simulation results show that the proposed method enables the spacecraft to achieve the safe collision avoidance for concurrent, sudden, dynamic and multi shape space debris clusters, and has a good real time performance.