用强化学习动态调整卫星编队，故障后快速重任务分配
Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications
- 采用DQN与PPO等强化学习算法实现编队重构与任务重分配
- 故障后任务完成率提升，响应时间显著缩短，平均奖励更高
- 适用于高可靠需求的航天任务，如应急通信与监测
由于用户需求增长、运营成本降低和技术进步，卫星编队应用发展迅速。然而，现有研究在编队重构与任务重分配方面存在显著空白，这正是本文关注的重点。我们系统评估了卫星故障对编队性能及任务需求的影响，提出针对GPS卫星编队的建模方法，以分析性能动态与任务分布策略，尤其在关键任务期间发生故障的场景。此外，引入Q-learning、Policy Gradient、Deep Q-Network（DQN）和Proximal Policy Optimization（PPO）等强化学习技术，应对故障后的重构与重任务挑战。结果表明，DQN与PPO在平均奖励、任务完成率和响应时间上均表现优异。
原文摘要 · Abstract (English)
The development of satellite constellation applications is rapidly advancing due to increasing user demands, reduced operational costs, and technological advancements. However, a significant gap in the existing literature concerns reconfiguration and retasking issues within satellite constellations, which is the primary focus of our research. In this work, we critically assess the impact of satellite failures on constellation performance and the associated task requirements. To facilitate this analysis, we introduce a system modeling approach for GPS satellite constellations, enabling an investigation into performance dynamics and task distribution strategies, particularly in scenarios where satellite failures occur during mission-critical operations. Additionally, we introduce reinforcement learning (RL) techniques, specifically Q-learning, Policy Gradient, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), for managing satellite constellations, addressing the challenges posed by reconfiguration and retasking following satellite failures. Our results demonstrate that DQN and PPO achieve effective outcomes in terms of average rewards, task completion rates, and response times.
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