基于学习的规划以提高地球观测卫星的科学回报
Learning-Based Planning for Improving Science Return of Earth Observation Satellites
AI总结：
本文提出基于强化学习和模仿学习的两种方法，用于优化地球观测卫星的数据采集策略，提升科学回报。
摘要：
地球观测卫星是收集关于我们星球的科学信息的强大工具，然而它们有局限性：它们不能轻易偏离轨道轨迹，其传感器有有限的视野范围，指向和操作这些传感器会消耗大量航天器的资源。这些卫星优化所收集的数据并只包括最重要或有信息量的测量是至关重要的。动态目标定位是一种新兴的概念，其中卫星资源和前瞻性仪器的数据用于智能重新配置和指向主要仪器。模拟研究已显示，动态目标定位比传统采样策略能收集更多的科学信息。在本工作中，我们提出了两种不同的基于学习的方法来实现动态目标定位，分别使用强化学习和模仿学习。这些学习方法建立在动态规划解决方案的基础上，以规划一系列采样地点的序列。我们评估了我们的方法与现有启发式方法在动态目标定位中的表现，显示了使用学习进行此应用的好处。模仿学习在平均上比最好的启发式方法好10.0%，而强化学习平均好13.7%。我们还展示了这两种学习方法可以使用少量数据有效地进行训练。
英文摘要：
Earth observing satellites are powerful tools for collecting scientific information about our planet, however they have limitations: they cannot easily deviate from their orbital trajectories, their sensors have a limited field of view, and pointing and operating these sensors can take a large amount of the spacecraft's resources. It is important for these satellites to optimize the data they collect and include only the most important or informative measurements. Dynamic targeting is an emerging concept in which satellite resources and data from a lookahead instrument are used to intelligently reconfigure and point a primary instrument. Simulation studies have shown that dynamic targeting increases the amount of scientific information gathered versus conventional sampling strategies. In this work, we present two different learning-based approaches to dynamic targeting, using reinforcement and imitation learning, respectively. These learning methods build on a dynamic programming solution to plan a sequence of sampling locations. We evaluate our approaches against existing heuristic methods for dynamic targeting, showing the benefits of using learning for this application. Imitation learning performs on average 10.0\% better than the best heuristic method, while reinforcement learning performs on average 13.7\% better. We also show that both learning methods can be trained effectively with small amounts of data.
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