Solar-sail trajectory design for multiple near-Earth asteroid exploration based on deep neural networks
Abstract
In the preliminary trajectory design of the multi-target rendezvous problem, a model that can quickly estimate the cost of the orbital transfer is essential. The estimation of the transfer time using solar sails between two arbitrary orbits is difficult and usually requires to solve an optimal control problem. Inspired by the successful applications of the deep neural networks in nonlinear regression, this work explores the possibility and effectiveness of mapping the transfer time for solar sails from the orbital characteristics using the deep neural networks. Furthermore, the Monte Carlo Tree Search method is investigated and used to search the optimal sequence considering a multi-asteroid exploration problem. The obtained sequences from preliminary design will be solved and verified by sequentially solving the optimal control problem. Two examples of different application backgrounds validate the effectiveness of the proposed approach.
- Publication:
-
Aerospace Science and Technology
- Pub Date:
- August 2019
- DOI:
- 10.1016/j.ast.2019.04.056
- arXiv:
- arXiv:1901.02172
- Bibcode:
- 2019AeST...91...28S
- Keywords:
-
- Solar sail;
- Near-Earth asteroid;
- Deep learning;
- Deep neural network;
- Monte Carlo Tree Search;
- Sequence planning;
- Computer Science - Computational Engineering;
- Finance;
- and Science;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Computer Science - Neural and Evolutionary Computing
- E-Print:
- 34 pages, 19 figures