用马尔可夫决策过程实现卫星避撞的实时最优规划
Markov Decision Processes for Satellite Maneuver Planning and Collision Avoidance
- 将卫星避撞建模为马尔可夫决策过程，支持在线生成最优策略
- 相比传统规则方法，显著减少机动次数并节省燃料
- 适用于大规模低轨星座，适合需要实时决策的场景
本文提出一种去中心化、在线式的可扩展卫星机动规划方法。尽管去中心化的规则策略已实现高效扩展，但卫星机动的最优决策算法仍研究不足。随着商业卫星星座规模扩大，在线规划可通过实时轨迹预测提升状态认知，从而降低机动频率并节约燃料。为此，本文将卫星机动规划问题建模为马尔可夫决策过程（MDP），实现在线生成最优机动策略且计算成本低。该方法应用于低地球轨道避撞问题，针对主动航天器需规避非机动目标的情况。我们在模拟的低地球轨道环境中测试所生成策略，并与传统规则基避撞方法进行对比。
原文摘要 · Abstract (English)
This paper presents a decentralized, online planning approach for scalable maneuver planning for large constellations. While decentralized, rule-based strategies have facilitated efficient scaling, optimal decision-making algorithms for satellite maneuvers remain underexplored. As commercial satellite constellations grow, there are benefits of online maneuver planning, such as using real-time trajectory predictions to improve state knowledge, thereby reducing maneuver frequency and conserving fuel. We address this gap in the research by treating the satellite maneuver planning problem as a Markov decision process (MDP). This approach enables the generation of optimal maneuver policies online with low computational cost. This formulation is applied to the low Earth orbit collision avoidance problem, considering the problem of an active spacecraft deciding to maneuver to avoid a non-maneuverable object. We test the policies we generate in a simulated low Earth orbit environment, and compare the results to traditional rule-based collision avoidance techniques.
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