近地轨道多碎片任务规划的最优方法：结合共椭圆转移与补给的深度强化学习方法
Optimal Multi-Debris Mission Planning in LEO: A Deep Reinforcement Learning Approach with Co-Elliptic Transfers and Refueling
AI总结：
本文提出一种结合共椭圆转移与补给的深度强化学习方法，用于近地轨道多碎片任务规划，实验显示其在任务效率和计算性能上优于传统方法。
摘要：
本文针对近地轨道（LEO）中多目标主动碎片清除（ADR）的挑战，引入了一个统一的共椭圆操作框架，结合霍曼转移、安全椭圆接近操作和显式补给逻辑。我们通过一个具有随机碎片场、禁区和delta V约束的现实轨道仿真环境，对三种不同的规划算法——贪心启发法、蒙特卡洛树搜索（MCTS）和深度强化学习（RL）中的Masked PPO进行基准测试。实验结果表明，在100个测试场景中，Masked PPO在任务效率和计算性能上均优于贪心启发法，能够访问两倍于贪心启发法的碎片数量，并在运行时间上显著优于MCTS。这些发现凸显了现代RL方法在可扩展、安全和资源高效空间任务规划中的潜力，为未来ADR自主性的发展铺平了道路。
英文摘要：
This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling logic. We benchmark three distinct planning algorithms Greedy heuristic, Monte Carlo Tree Search (MCTS), and deep reinforcement learning (RL) using Masked Proximal Policy Optimization (PPO) within a realistic orbital simulation environment featuring randomized debris fields, keep out zones, and delta V constraints. Experimental results over 100 test scenarios demonstrate that Masked PPO achieves superior mission efficiency and computational performance, visiting up to twice as many debris as Greedy and significantly outperforming MCTS in runtime. These findings underscore the promise of modern RL methods for scalable, safe, and resource efficient space mission planning, paving the way for future advancements in ADR autonomy.
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。
