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

TL;DR

This paper introduces a deep neural network classifier that accurately predicts the feasibility of low-thrust trajectories before optimization, significantly improving the efficiency of trajectory data generation for space missions.

Contribution

The work presents a novel DNN classifier achieving 97.9% accuracy to identify feasible low-thrust transfers, reducing wasted computation and enhancing dataset generation efficiency.

Findings

DNN classifier achieves 97.9% accuracy in feasibility prediction.

The method reduces optimization of infeasible trajectories, saving computational resources.

Enables efficient dataset generation for various mission scenarios.

Abstract

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.