Dual-path neural network learning method for free-return orbit integrating dynamic characteristics
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1.College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073 , China ; 2.State Key Laboratory of Space System Operation and Control, Changsha 410073 , China ; 3.China Astronaut Research and Training Center, Beijing 100094 , China

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V412.4

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    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.

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朱彬羽, 李海阳, 杨震, 等. 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.

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  • Received:March 28,2025
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  • Online: July 23,2025
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