Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design

Dario Izzo (), Christopher Iliffe Sprague () and Dharmesh Vijay Tailor ()
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Dario Izzo: European Space Agency
Christopher Iliffe Sprague: KTH Royal Institute of Technology
Dharmesh Vijay Tailor: European Space Agency

A chapter in Modeling and Optimization in Space Engineering, 2019, pp 191-210 from Springer

Abstract: 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-10501-3_8

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DOI: 10.1007/978-3-030-10501-3_8

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