Global Search for Optimal Low Thrust Spacecraft Trajectories using Diffusion Models and the Indirect Method

TL;DR

This paper introduces a novel approach combining diffusion models with the indirect method to efficiently explore low-thrust spacecraft trajectories, significantly speeding up the global search process in complex mission scenarios.

Contribution

It integrates diffusion models with the indirect trajectory optimization method, enabling rapid prediction of solution structures and accelerating the global search for optimal low-thrust trajectories.

Findings

Diffusion models successfully predict changes in costate structures.

The approach increases solution generation speed by 10-100 times.

The method is effective in complex three-body problem scenarios.

Abstract

Long time-duration low-thrust nonlinear optimal spacecraft trajectory global search is a computationally and time expensive problem characterized by clustering patterns in locally optimal solutions. During preliminary mission design, mission parameters are subject to frequent changes, necessitating that trajectory designers efficiently generate high-quality control solutions for these new scenarios. Generative machine learning models can be trained to learn how the solution structure varies with respect to a conditional parameter, thereby accelerating the global search for missions with updated parameters. In this work, state-of-the-art diffusion models are integrated with the indirect approach for trajectory optimization within a global search framework. This framework is tested on two low-thrust transfers of different complexity in the circular restricted three-body problem. By generating and analyzing a training data set, we develop mathematical relations and techniques to understand the complex structures in the costate domain of locally optimal solutions for these problems. A diffusion model is trained on this data and successfully accelerates the global search for both problems. The model predicts how the costate solution structure changes, based on the maximum spacecraft thrust magnitude. Warm-starting a numerical solver with diffusion model samples for the costates at the initial time increases the number of solutions generated per minute for problems with unseen thrust magnitudes by one to two orders of magnitude in comparison to samples from a uniform distribution and from an adjoint control transformation.