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Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design

In: Modeling and Optimization in Space Engineering

Author

Listed:
  • Dario Izzo

    (European Space Agency)

  • Christopher Iliffe Sprague

    (KTH Royal Institute of Technology)

  • Dharmesh Vijay Tailor

    (European Space Agency)

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.

Suggested Citation

  • Dario Izzo & Christopher Iliffe Sprague & Dharmesh Vijay Tailor, 2019. "Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design," Springer Optimization and Its Applications, in: Giorgio Fasano & János D. Pintér (ed.), Modeling and Optimization in Space Engineering, pages 191-210, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-10501-3_8
    DOI: 10.1007/978-3-030-10501-3_8
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