# Feasible Low-thrust Trajectory Identification via a Deep Neural Network Classifier

arXiv: https://arxiv.org/abs/2202.04962 · HTML: https://mlsanity.com/paper/2202.04962/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Real-time optimal control via Deep Neural Networks: study on landing problems](https://mlsanity.com/paper/1610.08668/) (2016)
- [A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control](https://mlsanity.com/paper/1812.02948/) (2018)
- [Deep Networks as Approximators of Optimal Transfers Solutions in Multitarget Missions](https://mlsanity.com/paper/1902.00250/) (2019)
- [Solar-Sail Trajectory Design for Multiple Near Earth Asteroid Exploration Based on Deep Neural Networks](https://mlsanity.com/paper/1901.02172/) (2019)
- [Machine learning and evolutionary techniques in interplanetary trajectory design](https://mlsanity.com/paper/1802.00180/) (2018)
- [GTOC8: Results and Methods of ESA Advanced Concepts Team and JAXA-ISAS](https://mlsanity.com/paper/1602.00849/) (2016)

## Built on by (most cited first, 1 of 1)

- [Efficient low-thrust trajectory data generation based on generative adversarial network](https://mlsanity.com/paper/2209.06427/) (2022)

## Abstract (arXiv)

In recent years, deep learning techniques have been introduced into the field of trajectory optimization to improve convergence and speed. Training such models requires large trajectory datasets. However, the convergence of low thrust (LT) optimizations is unpredictable before the optimization process ends. For randomly initialized low thrust transfer data generation, most of the computation power will be wasted on optimizing infeasible low thrust transfers, which leads to an inefficient data generation process. This work proposes a deep neural network (DNN) classifier to accurately identify feasible LT transfer prior to the optimization process. The DNN-classifier achieves an overall accuracy of 97.9%, which has the best performance among the tested algorithms. The accurate low-thrust trajectory feasibility identification can avoid optimization on undesired samples, so that the majority of the optimized samples are LT trajectories that converge. This technique enables efficient dataset generation for different mission scenarios with different spacecraft configurations.