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.

翻译：近年来,在轨迹优化领域引入了深层学习技术,以提高趋同和速度。培训这类模型需要大量的轨迹数据集。然而,低推力优化的趋同在优化进程结束前是不可预测的。对于随机初始化的低推力传输数据生成,大部分计算动力将浪费在优化不可行的低推力传输上,从而导致数据生成过程效率低下。这项工作提议建立一个深神经网络分类器,以便在优化进程之前准确确定可行的远程传输。 DNN分类器总精确度达到97.9%,这在测试的算法中具有最佳性能。准确的低射线轨迹可行性识别可以避免对不理想的样本进行优化,因此大多数优化的样本都是趋同的LT轨迹。这一技术使得不同航天器配置的不同飞行任务情景能够高效生成数据集。