Efficient low-thrust trajectory data generation based on generative adversarial network

TL;DR

This paper introduces a GAN-based method to efficiently generate low-thrust trajectory data, improving training data availability for deep learning models in trajectory optimization, demonstrated in a Near-Earth Asteroid mission scenario.

Contribution

The paper presents a novel application of GANs to produce feasible low-thrust transfer data, enhancing data generation efficiency for trajectory optimization tasks.

Findings

GAN achieves 84.3% convergence rate in data generation.

Generated data closely matches real transfer features.

Method accelerates training data collection for deep learning models.

Abstract

Deep learning-based techniques have been introduced into the field of trajectory optimization in recent years. Deep Neural Networks (DNNs) are trained and used as the surrogates of conventional optimization process. They can provide low thrust (LT) transfer cost estimation and enable more complex preliminary mission designs. However, it is a challenge to efficiently obtain the required amount of trajectory data for training. A Generative Adversarial Network (GAN) is adapted to generate the feasible LT trajectory data efficiently. The GAN consists of a generator and a discriminator, both of which are deep networks. The generator generates fake LT transfer features using random noise as input, while the discriminator distinguishes the generator's fake LT transfer features from real LT transfer features. The GAN is trained until the generator generates fake LT transfers that the discriminator cannot identify. This indicates the generator generates low thrust transfer features that have the same distribution as the real transfer features. The generated low thrust transfer data have a high convergence rate, and they can be used to efficiently produce training data for deep learning models. The proposed approach is validated by generating feasible LT transfers in a Near-Earth Asteroid (NEA) mission scenario. The convergence rate of GAN-generated samples is 84.3%.