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2019 | OriginalPaper | Buchkapitel

Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design

verfasst von : Dario Izzo, Christopher Iliffe Sprague, Dharmesh Vijay Tailor

Erschienen in: Modeling and Optimization in Space Engineering

Verlag: Springer International Publishing

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Abstract

After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth–Mars orbital transfer, extend the findings made previously for landing scenarios and quadcopter dynamics, opening a new research area in interplanetary trajectory planning.

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Titel: Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design
verfasst von: Dario Izzo
Christopher Iliffe Sprague
Dharmesh Vijay Tailor
Verlag: Springer International Publishing
Buch: Modeling and Optimization in Space Engineering
Print ISBN: 978-3-030-10500-6

Electronic ISBN: 978-3-030-10501-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-10501-3_8

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