首个在轨运行的智能体系统，实现航天器自主热控
ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy
- 用轻量LLM智能体与强化学习控制器异步协同，适配航天硬件
- 在轨实验中热控违规减少，任务时长和CPU利用率提升
- 适合需自主决策的深空探测与低轨卫星任务
本文提出ASTREA，首个在飞行成熟硬件（技术成熟度TRL 9）上实现的自主航天器操作智能体系统，已在国际空间站进行在轨运行。以热控为典型场景，将资源受限的大语言模型（LLM）智能体与强化学习控制器结合，采用专为航天平台设计的异步架构。地面实验表明，LLM引导的监督显著提升热稳定性并减少违规，验证了语义推理与自适应控制在硬件约束下的可行性。在轨测试初期因推理延迟与低地球轨道（LEO）快速热循环不匹配而受阻，通过同步轨道周期后，系统性能超越基线，违规减少、任务时长延长、CPU利用率提升。这些发现展示了未来自主航天器中可扩展智能体监督架构的潜力。
原文摘要 · Abstract (English)
This paper presents ASTREA, the first agentic system executed on flight-heritage hardware (TRL 9) for autonomous spacecraft operations, with on-orbit operation aboard the International Space Station (ISS). Using thermal control as a representative use case, we integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms. Ground experiments show that LLM-guided supervision improves thermal stability and reduces violations, confirming the feasibility of combining semantic reasoning with adaptive control under hardware constraints. On-orbit validation aboard the ISS initially faced challenges due to inference latency misaligned with the rapid thermal cycles of Low Earth Orbit (LEO) satellites. Synchronization with the orbit length successfully surpassed the baseline with reduced violations, extended episode durations, and improved CPU utilization. These findings demonstrate the potential for scalable agentic supervision architectures in future autonomous spacecraft.
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