通过深度强化学习和地面测试环境实现近距离卫星操作
Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments
AI总结：
本文研究利用深度强化学习实现多智能体卫星检查任务中的自主控制，并通过LINCS实验室从仿真到真实四旋翼硬件测试算法性能，关注其在训练环境外部署时的行为与性能退化。
摘要：
随着太空环境日益拥挤和竞争激烈，安全有效的卫星操作变得越来越具有挑战性。因此，人们对自主卫星能力越来越感兴趣，常见的机器学习技术因其在太空领域解决复杂决策问题的潜力而受到关注。然而，这些方法中许多具有“黑箱”特性，导致难以理解模型的输入/输出关系，尤其是其对环境扰动、传感器噪声和控制干预的敏感性。本文探讨了在多智能体检查任务中使用深度强化学习（DRL）进行卫星控制。本地协作卫星智能网络（LINCS）实验室用于测试这些控制算法在不同环境中的性能，从模拟到真实世界的四旋翼无人机硬件，特别关注理解它们在训练环境之外部署时的行为及性能可能退化的情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
