  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.
