用深度强化学习优化卫星拍照，省电又提质量。
An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem
- 双决策机制：选拍哪些目标、定最佳拍摄时间
- 图像不合格率降60%以上，姿态调整耗能减少78%
- 适合航天调度、资源受限系统优化场景
敏捷地球观测卫星调度问题（AEOSSP）旨在卫星轨道上选择一组观测目标并满足时间、能量和内存约束。决定何时何地观测本身就很复杂，若再考虑云遮挡、大气扰动和图像分辨率等影响成像质量的因素，则挑战更大。本文提出一种基于深度强化学习（DRL）的解决方案，针对具有时变收益的AEOSSP，整合上述三类因素以优化能量与内存使用。该方法采用双重决策机制：先确定目标序列，再为每个目标选定最优观测时机。实验表明，所提算法将不符合质量要求的图像捕获量减少超过60%，从而减少姿态机动带来的能量浪费达78%，同时保持优异的观测性能。
原文摘要 · Abstract (English)
The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) entails finding the subset of observation targets to be scheduled along the satellite's orbit while meeting operational constraints of time, energy and memory. The problem of deciding what and when to observe is inherently complex, and becomes even more challenging when considering several issues that compromise the quality of the captured images, such as cloud occlusion, atmospheric turbulence, and image resolution. This paper presents a Deep Reinforcement Learning (DRL) approach for addressing the AEOSSP with time-dependent profits, integrating these three factors to optimize the use of energy and memory resources. The proposed method involves a dual decision-making process: selecting the sequence of targets and determining the optimal observation time for each. Our results demonstrate that the proposed algorithm reduces the capture of images that fail to meet quality requirements by > 60% and consequently decreases energy waste from attitude maneuvers by up to 78%, all while maintaining strong observation performance.
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