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.

本研究解决了低地球轨道 debris 增加对航天安全构成的威胁，以及主动去除 debris (ADR) 任务规划复杂性的问题。论文提出了一种基于深度强化学习的自主决策规划模型，能够训练轨道转移飞行器 (OTV) 最优去除 sequencing，并能够自主适应动态轨道条件和任务要求。该模型的应用将显著提高 ADR 任务的经济性和技术有效性。

基于人工智能的风险意识调度用于主动去除太空 debris 任务