利用强化学习优化多碎片对接任务的使命规划
Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance
AI总结：
本研究提出基于强化学习的框架，用于优化多碎片清除任务的使命规划，通过自适应碰撞避让和高效任务规划提高任务效率和安全性。
摘要：
随着地球轨道环境日益拥挤，主动碎片清除（ADR）任务在确保安全操作的同时减少在轨碰撞风险面临重大挑战。本研究提出了一种基于强化学习（RL）的框架，以增强ADR任务中的自适应碰撞避让，特别是针对使用小卫星进行多碎片清除。小卫星因其灵活性、成本效益和机动性而被越来越多地采用，使其非常适合动态任务如ADR。在现有多碎片对接研究的基础上，该框架整合了补给策略、高效的任务规划和自适应碰撞避让，以优化航天器对接操作。所提出的方法采用掩码的近端策略优化（PPO）算法，使RL代理能够根据实时轨道条件动态调整机动。关键考虑因素包括燃料效率、避免活跃碰撞区以及动态轨道参数的优化。RL代理学习确定与多个碎片目标对接的高效序列，优化燃料使用和任务时间，同时纳入必要的补给停靠。基于铱星33碎片数据集的模拟ADR场景用于评估，涵盖了多样的轨道配置和碎片分布，以展示鲁棒性和适应性。结果表明，所提出的RL框架相比传统启发式方法减少了碰撞风险并提高了任务效率。本工作为规划复杂的多碎片ADR任务提供了可扩展的解决方案，并适用于其他多目标对接问题的自主航天任务规划。
英文摘要：
As the orbital environment around Earth becomes increasingly crowded with debris, active debris removal (ADR) missions face significant challenges in ensuring safe operations while minimizing the risk of in-orbit collisions. This study presents a reinforcement learning (RL) based framework to enhance adaptive collision avoidance in ADR missions, specifically for multi-debris removal using small satellites. Small satellites are increasingly adopted due to their flexibility, cost effectiveness, and maneuverability, making them well suited for dynamic missions such as ADR. Building on existing work in multi-debris rendezvous, the framework integrates refueling strategies, efficient mission planning, and adaptive collision avoidance to optimize spacecraft rendezvous operations. The proposed approach employs a masked Proximal Policy Optimization (PPO) algorithm, enabling the RL agent to dynamically adjust maneuvers in response to real-time orbital conditions. Key considerations include fuel efficiency, avoidance of active collision zones, and optimization of dynamic orbital parameters. The RL agent learns to determine efficient sequences for rendezvousing with multiple debris targets, optimizing fuel usage and mission time while incorporating necessary refueling stops. Simulated ADR scenarios derived from the Iridium 33 debris dataset are used for evaluation, covering diverse orbital configurations and debris distributions to demonstrate robustness and adaptability. Results show that the proposed RL framework reduces collision risk while improving mission efficiency compared to traditional heuristic approaches. This work provides a scalable solution for planning complex multi-debris ADR missions and is applicable to other multi-target rendezvous problems in autonomous space mission planning.
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。
