卫星避碰现在靠人工和规则，越来越难。这篇用PPO强化学习，在模拟里成功率97.5%，比传统方法高出一大截，代码还开源了。
研究者提出用近端策略优化（PPO）训练强化学习智能体，实现卫星自主避碰。在1000次确定性GEO场景中，智能体避障成功率达97.5%，远超基于规则基线（20.7%）和脉冲delta-v规划器（27.5%）。训练使用开源高保真天体动力学模拟器，包含日月三体摄动、燃料依赖推力及可配置碎片场。采用课程学习和塑形奖励，兼顾生存、投影距离和delta-v节省。框架已公开，代码见purl.org/sat-trajectory-avoidance。
Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance
Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO). However, these events have been growing in frequency as orbital congestion worsens with the launch of megaconstellations. Consequently, conjunction alerts and collision risks are becoming increasingly common. Current practices, which are commonly manual or rule-based, have difficulty scaling to these worsening dynamic environments. To address this intensifying situation, we propose a reinforcement-learning policy for autonomous collision avoidance, trained via Proximal Policy Optimization (PPO) along with an open-source, high-fidelity astrodynamics simulator for training and evaluation. In 1,000 deterministic GEO episodes, our agent achieves a 97.5% collision avoidance success rate, outperforming traditional controllers such as a rule-based baseline (20.7% success) and an impulsive delta-v planner baseline (27.5% success). To achieve these results, we designed a simulator to train and evaluate our agent, using real-world and simulated debris. We simulate Newtonian two-body dynamics using Sun/Moon third-body perturbations, fuel-dependent thrust, and configurable debris fields. The agent is trained with curriculum learning and shaped rewards oriented toward encouraging survival, adequate projected miss distance, and delta-v conservation. Finally, our evaluation consisted of a fully deterministic pipeline, including shared seeds, per-episode logs, and telemetry exports. Our work is a publicly available framework at https://purl.org/sat-trajectory-avoidance