This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions including optical and Synthetic Aperture Radar (SAR) satellites. In autonomous operation mode, satellites are equipped with intelligent capabilities enabling real-time decision-making based on the latest conditions, while requiring minimal interaction with ground operators. Traditional scheduling approaches typically rely on mathematical models to represent satellite mission and resource management. Then, this problem is solved by using optimization algorithms. However, such solutions become less effective when the underlying models are not available, over complex, and inaccurate due to dynamic changes and uncertainties inherent in the space mission environment. A promising alternative is to reformulate the problem as a sequential decision-making process and apply model-free reinforcement learning techniques to enable adaptive and real-time resource management. To this end, we propose a novel transformer-based architecture tailored for heterogeneous satellite cluster autonomous EO Mission with relational observations-actions tokenization and differential attention mechanism. Our experimental results demonstrate significant performance improvements compared to the available baselines. Moreover, the proposed architecture exhibits strong adaptability and transferability with respect to varying numbers of satellite clusters.

翻译：本文针对执行光学与合成孔径雷达（SAR）卫星等对地观测任务中异构卫星集群的自主资源管理问题。在自主运行模式下，卫星配备智能能力，能够基于最新条件实施实时决策，同时最大限度减少与地面操作员的交互。传统调度方法通常依赖数学模型表征卫星任务与资源管理，进而采用优化算法求解。然而，当底层模型不可用、过度复杂或由于空间任务环境固有的动态变化与不确定性而存在误差时，此类解决方案的有效性会降低。一个有前景的替代方案是将该问题重构为序列决策过程，并应用无模型强化学习技术实现自适应实时资源管理。为此，我们提出一种新颖的基于Transformer的架构，该架构专为异构卫星集群自主对地观测任务设计，包含关系型观测-动作标记化与差分注意力机制。实验结果表明，与现有基线相比，该方法在性能上取得显著提升。此外，所提架构在不同卫星集群数量下展现出强大的适应性与可迁移性。