Feasible Low-thrust Trajectory Identification via a Deep Neural Network Classifier
This addresses a domain-specific problem for aerospace engineers by improving dataset generation efficiency, though it is incremental as it builds on existing deep learning applications in trajectory optimization.
The paper tackles the inefficiency of generating low-thrust trajectory datasets by proposing a deep neural network classifier to identify feasible transfers before optimization, achieving 97.9% accuracy and enabling more efficient data generation.
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.