# Machine learning and evolutionary techniques in interplanetary trajectory design

arXiv: https://arxiv.org/abs/1802.00180 · HTML: https://mlsanity.com/paper/1802.00180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [Learning the optimal state-feedback via supervised imitation learning](https://mlsanity.com/paper/1901.02369/) (2019)
- [Feasible Low-thrust Trajectory Identification via a Deep Neural Network Classifier](https://mlsanity.com/paper/2202.04962/) (2022)
- [Efficient low-thrust trajectory data generation based on generative adversarial network](https://mlsanity.com/paper/2209.06427/) (2022)

## Abstract (arXiv)

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.