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.

翻译：随着卫星和轨道碎片数量的不断增加，空间拥堵已成为一个关键问题，威胁着卫星的安全与可持续性。碰撞规避、位置保持和轨道机动等挑战需要先进技术来处理动态不确定性和多智能体交互。强化学习（RL）在该领域已显示出潜力，能够为空间操作提供自适应、自主的决策策略；然而，许多现有的RL框架依赖于从头开始构建的定制化环境，这些环境通常使用简化模型，并且需要大量时间来实施和验证轨道动力学，限制了其充分捕捉现实世界复杂性的能力。为解决这一问题，我们引入了轨道动物园（OrbitZoo），这是一个基于高保真工业标准库构建的通用多智能体RL环境，能够实现真实数据生成，支持碰撞规避和协同机动等场景，并确保稳健且精确的轨道动力学。该环境已通过真实卫星星座——星链（Starlink）——进行验证，与实际数据相比，平均绝对百分比误差（MAPE）达到0.16%。这一验证确保了生成高保真模拟以及实现自主独立卫星操作的可靠性。