对比主动与被动卫星巡检策略，发现特定条件下被动方案可媲美主动策略。
Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection
- 用强化学习训练主动巡检策略，对比预设路径的被动策略。
- 在燃料消耗和表面覆盖上，被动策略在部分场景表现相当。
- 适合关注太空任务效率与自主决策的航天工程师参考。
本文研究卫星巡检问题，即一个或多个巡检卫星对疑似故障的空间目标（RSO）进行成像或检查。传统策略常将动作空间离散化为预设航点，便于经典优化与机器学习方法处理，但可能导致某些场景下的引导不优。本研究通过仿真对比多智能体任务中被动与主动策略的权衡，关键因素包括RSO动态模式、状态不确定性、未建模进入条件及巡检器运动类型。评估聚焦燃料消耗与表面覆盖效果。基于蒙特卡洛的被动策略评估器与强化学习框架相结合，研究发现，在特定条件下，如自然运动绕行（NMC）等被动策略可达到与基于强化学习的航点转移主动策略相当的性能。
原文摘要 · Abstract (English)
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.
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