用AI自动生成太空垃圾清理最优路线，还能自动应对高风险目标。
AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions
- 基于深度强化学习训练航天器自主规划清理顺序。
- 能有效处理高碰撞风险碎片，提升任务安全性。
- 适合需要自主决策的深空任务与轨道维护系统。
低地球轨道（LEO）中空间碎片的激增对太空可持续性与航天器安全构成重大威胁。主动碎片清除（ADR）作为一种有前景的解决方案，利用轨道转移飞行器（OTVs）实现碎片离轨，从而降低未来碰撞风险。然而，ADR任务极为复杂，需精准规划以确保经济可行性和技术有效性。此外，这些服务任务需具备高度自主能力，在不断变化的轨道条件和任务需求下进行实时规划。本文提出一种基于深度强化学习（DRL）的自主决策-规划模型，训练OTV生成最优碎片清除序列。结果表明，该框架能使智能体找到最优任务计划，并自主更新规划策略，以应对高碰撞风险碎片。
原文摘要 · Abstract (English)
The proliferation of debris in Low Earth Orbit (LEO) represents a significant threat to space sustainability and spacecraft safety. Active Debris Removal (ADR) has emerged as a promising approach to address this issue, utilising Orbital Transfer Vehicles (OTVs) to facilitate debris deorbiting, thereby reducing future collision risks. However, ADR missions are substantially complex, necessitating accurate planning to make the missions economically viable and technically effective. Moreover, these servicing missions require a high level of autonomous capability to plan under evolving orbital conditions and changing mission requirements. In this paper, an autonomous decision-planning model based on Deep Reinforcement Learning (DRL) is developed to train an OTV to plan optimal debris removal sequencing. It is shown that using the proposed framework, the agent can find optimal mission plans and learn to update the planning autonomously to include risk handling of debris with high collision risk.
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