用深度强化学习实现卫星近距离协同操作，提升自主性与鲁棒性。
Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments
- 基于深度强化学习训练多星协同控制策略。
- 在仿真到真实四旋翼平台测试中验证算法稳定性。
- 关注模型对扰动和噪声的敏感性，适合航天自主控制研究者。
随着太空环境日益拥挤和对抗加剧，安全高效的卫星操作变得愈发困难。因此，发展自主卫星能力受到广泛关注，机器学习技术因其在复杂决策中的潜力而备受瞩目。然而，许多方法存在“黑箱”问题，难以理解模型输入输出关系，尤其是对环境扰动、传感器噪声和控制干预的敏感性。本文探索深度强化学习（DRL）在多智能体卫星巡检任务中的应用。利用局部协同卫星智能网络（LINCS）实验室，在从仿真到真实四旋翼无人机硬件的不同环境中测试控制算法性能，重点关注其在训练环境外部署时的行为表现及性能退化情况。
原文摘要 · Abstract (English)
With the increasingly congested and contested space environment, safe and effective satellite operation has become increasingly challenging. As a result, there is growing interest in autonomous satellite capabilities, with common machine learning techniques gaining attention for their potential to address complex decision-making in the space domain. However, the "black-box" nature of many of these methods results in difficulty understanding the model's input/output relationship and more specifically its sensitivity to environmental disturbances, sensor noise, and control intervention. This paper explores the use of Deep Reinforcement Learning (DRL) for satellite control in multi-agent inspection tasks. The Local Intelligent Network of Collaborative Satellites (LINCS) Lab is used to test the performance of these control algorithms across different environments, from simulations to real-world quadrotor UAV hardware, with a particular focus on understanding their behavior and potential degradation in performance when deployed beyond the training environment.
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