用强化学习训练自动航天器，主动避障并救援危急卫星
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making
- 用强化学习训练自动服务舱，自主判断碰撞风险
- 能实现对接危急卫星并执行最优规避机动
- 适合关注太空安全与智能航天系统的研究人员
本研究开发了一种基于人工智能的自主在轨服务（OOS）任务，用于辅助航天器规避碰撞。提出一种通过强化学习（RL）训练的自主‘服务舱’，可自主检测目标卫星与空间碎片间的潜在碰撞，与受威胁卫星交会并对接，执行最优规避机动（CAM）。该RL模型融合碰撞风险评估、卫星参数和碎片数据，生成最优的在轨交会与防撞操作矩阵。采用交叉熵算法高效求解最优决策策略。初步结果验证了自主机器人式在轨服务在碰撞规避中的可行性，聚焦于单个服务舱对单个受威胁卫星的场景。然而，将航天器交会与最优规避机动融合仍面临显著复杂性。通过案例研究讨论了该框架成功实施的设计挑战与关键参数。
原文摘要 · Abstract (English)
This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs). We propose an autonomous `servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM. The RL model integrates collision risk estimates, satellite specifications, and debris data to generate an optimal maneuver matrix for OOS rendezvous and collision prevention. We employ the Cross-Entropy algorithm to find optimal decision policies efficiently. Initial results demonstrate the feasibility of autonomous robotic OOS for collision avoidance services, focusing on one servicer spacecraft to one endangered satellite scenario. However, merging spacecraft rendezvous and optimal CAM presents significant complexities. We discuss design challenges and critical parameters for the successful implementation of the framework presented through a case study.
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