Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability

TL;DR

This paper introduces pretrained neural networks that accurately predict fuel consumption and reachability for low-thrust space missions, generalizing across diverse scenarios with high precision and efficiency.

Contribution

It develops the most general and accurate neural approximators for low-thrust trajectory metrics, utilizing a novel homotopy ray method and self-similar data transformation.

Findings

Neural networks achieve 0.78% relative error in velocity prediction.

Models demonstrate strong generalization on third-party datasets.

Predictions are computationally efficient and applicable across various mission scenarios.

Abstract

In trajectory design, fuel consumption and trajectory reachability are two key performance indicators for low-thrust missions. This paper proposes general-purpose pretrained neural networks to predict these metrics. The contributions of this paper are as follows: Firstly, based on the confirmation of the Scaling Law applicable to low-thrust trajectory approximation, the largest dataset is constructed using the proposed homotopy ray method, which aligns with mission-design-oriented data requirements. Secondly, the data are transformed into a self-similar space, enabling the neural network to adapt to arbitrary semi-major axes, inclinations, and central bodies. This extends the applicability beyond existing studies and can generalize across diverse mission scenarios without retraining. Thirdly, to the best of our knowledge, this work presents the current most general and accurate low-thrust trajectory approximator, with implementations available in C++, Python, and MATLAB. The resulting neural network achieves a relative error of 0.78% in predicting velocity increments and 0.63% in minimum transfer time estimation. The models have also been validated on a third-party dataset, multi-flyby mission design problem, and mission analysis scenario, demonstrating their generalization capability, predictive accuracy, and computational efficiency.