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.
- Pub Date:
- February 2018
- Computer Science - Neural and Evolutionary Computing;
- Computer Science - Systems and Control
- Submitted to as a book chapter for a Springer book on "Optimization in Space Engineering"