@article{ZHU2025, 
author = {Binyu ZHU and Haiyang LI and Zhen YANG and Junhua HE and Lin LU and Yuhang ZHANG},
title = {Dual-path neural network learning method for free-return orbit integrating dynamic characteristics},
year = {2025},
journal = {Journal of National University of Defense Technology},
volume = {47},
number = {4},
pages = {64-75},
keywords = {manned lunar exploration mission, free-return orbit, dual-path neural network, ATK. Astromaster},
url = {https://www.sciopen.com/article/10.11887/j.issn.1001-2486.25030028},
doi = {10.11887/j.issn.1001-2486.25030028},
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.}
}