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Rapid accessibility evaluation for ballistic lunar capture via manifolds: A Gaussian process regression application
Sandeep K. Singh1(
),
), Abstract
In this study, a supervised machine learning approach called Gaussian process regression (GPR) was applied to approximate optimal bi-impulse rendezvous maneuvers in the cis-lunar space. We demonstrate the use of the GPR approximation of the optimal bi-impulse transfer to patch points associated with various invariant manifolds in the cis-lunar space. The proposed method advances preliminary mission design operations by avoiding the computational costs associated with repeated solutions of the optimal bi-impulsive Lambert transfer because the learned map is computationally efficient. This approach promises to be useful for aiding in preliminary mission design. The use of invariant manifolds as part of the transfer trajectory design offers unique features for reducing propellant consumption while facilitating the solution of trajectory optimization problems. Long ballistic capture coasts are also very attractive for mission guidance, navigation, and control robustness. A multi-input single-output GPR model is presented to represent the fuel costs (in terms of the
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Pages 375-397
Cite this article:
Singh SK, Junkins JL, Majji M, et al. Rapid accessibility evaluation for ballistic lunar capture via manifolds: A Gaussian process regression application. Astrodynamics, 2022, 6(4): 375-397. https://doi.org/10.1007/s42064-021-0130-0
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