国防科技大学学报2025，Vol.47Issue(4)：64-75,12.DOI:10.11887/j.issn.1001-2486.25030028
融合动力学特征的自由返回轨道双路网络学习方法
Dual-path neural network learning method for free-return orbit integrating dynamic characteristics
摘要
Abstract
The free-return orbit serves as the preferred orbital scheme for crewed spacecraft in earth-moon transfers,yet its design involves stringent constraints and significant initial-value dependency in existing algorithms.The earth-moon transfer trajectory planning for manned lunar exploration was addressed by proposing a dual-path neural network learning method to optimize free-return orbit initialization.A dynamic model of the free-return orbit was established to analyze the characteristics of the near-earth orbital solution space.Integrating the spatial partitioning characteristics of ascending and descending orbital phase in solution spaces,a dual-path neural network architecture designed via parameter-correlated transformation was proposed to ensure the completeness of orbital solutions.Utilizing ATK.Astromaster,the earth-moon free-return orbit planning under the dual-path network learning-based initialization method was implemented and validated through simulation.The results provide an effective reference for mitigating initial-value dependency in manned lunar mission orbit design.关键词
载人探月任务/自由返回轨道/双路神经网络/ATK机动规划模块Key words
manned lunar exploration mission/free-return orbit/dual-path neural network/ATK.Astromaster分类
航空航天引用本文复制引用
朱彬羽,李海阳,杨震,何俊华,陆林,张宇航..融合动力学特征的自由返回轨道双路网络学习方法[J].国防科技大学学报,2025,47(4):64-75,12.基金项目
国家自然科学基金资助项目(12072365) （12072365）
湖南省自然科学基金资助项目(2023JJ20047) （2023JJ20047）
载人航天工程科技创新团队课题资助项目 （）
