  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%.
