This paper focuses on the use of meta-reinforcement learning for the autonomous guidance of a spacecraft during the terminal phase of an impact mission toward a binary asteroid system. The control policy is replaced by a convolutional-recurrent neural network, which is used to map optical observations collected by the onboard camera to the control thrust and thrusting times. The network is trained by a proximal policy optimization algorithm, a family of reinforcement learning methods. The final phase of NASA’s Double Asteroid Redirection Test (DART) mission is used as a test case. The objective is to maneuver the spacecraft to impact the smaller object, Dimorphos, in the Didymos binary system. The spacecraft dynamics are described using the bi-elliptic restricted four-body problem with solar radiation pressure. The initial conditions are randomly scattered according to the actual specifications of the DART mission. A random error on the orbital position of Dimorphos is also considered to reflect uncertainty on the binary system’s characteristics and dynamics. The control system aims at minimizing the error on the final spacecraft position. Numerical results show that the guidance system can correctly drive the spacecraft toward the final impact point in more than 98% of the 500 test scenarios.

本文重点关注在对双星小行星系统的撞击任务的最后阶段，使用元强化学习对航天器进行自主引导。控制策略被卷积循环神经网络所取代，该网络用于将机载相机收集的光学观察结果映射到控制推力和推力时间。该网络由一种近端策略优化算法（强化学习方法家族）进行训练。NASA 的双小行星重定向测试 (DART) 任务的最后阶段被用作测试案例。目标是操纵航天器撞击Didymos双星系统中较小的物体Dimorphos。航天器动力学是使用具有太阳辐射压力的双椭圆受限四体问题来描述的。初始条件根据 DART 任务的实际规格随机分布。Dimorphos 轨道位置的随机误差也被认为反映了双星系统特性和动力学的不确定性。控制系统旨在最大限度地减少最终航天器位置的误差。数值结果表明，在 500 次测试场景中，超过 98% 的情况下，制导系统都能正确地将航天器推向最终撞击点。