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NASA NTRS · 20240015712

Lunar Browser Utilization of Machine Learning for Trajectory Solution Production

Dylan N Morrison-Fogel·Anthony L Genova·Paul G Levinson-Muth·Regina Blue
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Abstract

This paper describes the application of machine learning tools to produce Earth-Moon spacecraft trajectories with applications to NASA’s Commercial Lunar Payload Services (CLPS) and Artemis Human Landing System (HLS) programs. Existing trajectory solutions are used to train and test machine learning models to predict essential details of a trajectory sequence from Earth-launch to Low-Lunar Orbit, populating a database of solutions with future launch dates. The machine learning model will implement hyperparameter optimization for further re-training to improve model performance. Accurate predictive models decrease the time required to produce solutions and are readily implemented in the Lunar Browser tool.

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Trajectory DesignMachine LearningCLPSHLS

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Dylan N Morrison-Fogel, Anthony L Genova, Paul G Levinson-Muth, Regina Blue. Lunar Browser Utilization of Machine Learning for Trajectory Solution Production. https://ntrs.nasa.gov/citations/20240015712

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