自主巡检机制评估：主动与被动卫星巡检
Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection
AI总结：
本研究通过比较仿真评估卫星巡检中被动与主动策略的权衡，发现被动策略在特定条件下可与主动策略表现相当。
摘要：
本文针对卫星巡检问题展开研究，在该问题中，一个或多个卫星（巡检器）因潜在故障或异常而被指派对空间目标（RSO）进行成像或检查。巡检策略通常被简化为具有预定义航路点的离散动作空间，这有助于经典优化和基于机器学习的方法的可处理性。然而，这种离散化在特定场景下可能导致次优的制导。本研究提出了一项比较仿真，以探索多智能体任务中被动策略与主动策略之间的权衡。考虑的关键因素包括空间目标的动态模式、状态不确定性、未建模的进入准则以及巡检器的运动类型。评估侧重于燃料利用率和表面覆盖率。基于蒙特卡洛的被动策略评估器和用于训练主动巡检策略的强化学习框架，本研究探讨了在何种条件下被动策略（如自然运动环绕（NMC））可能表现得与主动策略（如基于强化学习的航路点转移）相当。
英文摘要：
This paper addresses the problem of satellite inspection, where one or more satellites (inspectors) are tasked with imaging or inspecting a resident space object (RSO) due to potential malfunctions or anomalies. Inspection strategies are often reduced to a discretized action space with predefined waypoints, facilitating tractability in both classical optimization and machine learning based approaches. However, this discretization can lead to suboptimal guidance in certain scenarios. This study presents a comparative simulation to explore the tradeoffs of passive versus active strategies in multi-agent missions. Key factors considered include RSO dynamic mode, state uncertainty, unmodeled entrance criteria, and inspector motion types. The evaluation is conducted with a focus on fuel utilization and surface coverage. Building on a Monte-Carlo based evaluator of passive strategies and a reinforcement learning framework for training active inspection policies, this study investigates conditions under which passive strategies, such as Natural Motion Circumnavigation (NMC), may perform comparably to active strategies like Reinforcement Learning based waypoint transfers.
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。
