论文标题
通过深神经网络分类器可行的低头轨迹识别
Feasible Low-thrust Trajectory Identification via a Deep Neural Network Classifier
论文作者
论文摘要
近年来，深度学习技术已被引入轨迹优化领域，以提高收敛和速度。培训此类模型需要大型轨迹数据集。但是，在优化过程结束之前，低推力（LT）优化的收敛性是不可预测的。对于随机初始化的低推力传输数据生成，大多数计算能力将被浪费在优化不可行的低推力转移时，从而导致数据生成效率低下。这项工作提出了一个深神经网络（DNN）分类器，以在优化过程之前准确识别可行的LT转移。 DNN分类器的总体准确性为97.9％，在经过测试算法中的性能最佳。准确的低推力轨迹可行性识别可以避免对不希望的样品进行优化，因此大多数优化的样品是收敛的LT轨迹。该技术可为不同的任务方案提供有效的数据集生成，并具有不同的航天器配置。
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.
