构建高保真卫星多智能体环境，提升强化学习在轨自主决策能力
OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning
- 基于工业级动力学库构建真实轨道环境，支持多智能体协同
- 与星链星座对比误差仅0.16%，验证了动力学精度可靠性
- 适合研究太空碰撞规避与自主轨道控制的科研人员
卫星与轨道碎片数量激增导致太空拥堵问题日益严重，威胁卫星安全与可持续性。碰撞规避、轨道保持及机动等任务需应对动态不确定性与多智能体交互。强化学习（RL）在该领域展现出潜力，可实现自适应、自主的太空操作策略；但现有多数框架依赖从零构建的简化仿真环境，需大量时间实现和验证轨道动力学，难以充分反映真实复杂性。为此，我们提出OrbitZoo，一个基于高保真工业标准库的多智能体强化学习环境，支持真实数据生成与多种场景如碰撞规避与协同机动，确保精确可靠的轨道动力学建模。该环境经真实星链星座验证，与实际数据对比的平均绝对百分比误差（MAPE）仅为0.16%。此精度保障了高保真仿真的可靠性，助力实现独立自主的卫星运行。
原文摘要 · Abstract (English)
The increasing number of satellites and orbital debris has made space congestion a critical issue, threatening satellite safety and sustainability. Challenges such as collision avoidance, station-keeping, and orbital maneuvering require advanced techniques to handle dynamic uncertainties and multi-agent interactions. Reinforcement learning (RL) has shown promise in this domain, enabling adaptive, autonomous policies for space operations; however, many existing RL frameworks rely on custom-built environments developed from scratch, which often use simplified models and require significant time to implement and validate the orbital dynamics, limiting their ability to fully capture real-world complexities. To address this, we introduce OrbitZoo, a versatile multi-agent RL environment built on a high-fidelity industry standard library, that enables realistic data generation, supports scenarios like collision avoidance and cooperative maneuvers, and ensures robust and accurate orbital dynamics. The environment is validated against a real satellite constellation, Starlink, achieving a Mean Absolute Percentage Error (MAPE) of 0.16% compared to real-world data. This validation ensures reliability for generating high-fidelity simulations and enabling autonomous and independent satellite operations.
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