Dual-path neural network learning method for free-return orbit integrating dynamic characteristics

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.