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.

本研究解决航天器在轨服务（OOS）中的碰撞规避问题，提出了一种基于强化学习的自主服务器，能够自动检测潜在碰撞并执行最优碰撞规避机动（CAM）。研究结果表明，该方法具备实现碰撞规避服务的可行性，尽管在航天器会合与最优机动之间存在显著复杂性。

基于自主决策的轨道服务用于航天器碰撞规避