Algorithmic Considerations for Effective Global Search of Robust Low-Thrust Trajectories

TL;DR

This paper develops algorithmic enhancements for global search of robust low-thrust trajectories in cislunar space, demonstrating improved convergence and robustness by leveraging non-robust solutions as initial guesses.

Contribution

It introduces a novel approach combining non-robust solutions to warm-start the search for robust trajectories, enhancing efficiency and solution quality.

Findings

Non-robust solutions improve convergence rate.

Warm-starting enhances robustness of solutions.

Algorithmic enhancements lead to better trajectory robustness.

Abstract

The growing interest in the cislunar domain over the past decade has led to an increasing demand for low-thrust missions to key orbits within this region. These low-thrust missions, typically characterized by long thrust arcs, are highly susceptible to operational disruptions such as unforeseen thruster outages or missed thrust events. Consequently, there is a critical need for efficient trajectory design frameworks which incorporate robustness against such anomalies. In this study, we utilize a robust trajectory design framework to explore the solution space for the Power and Propulsion Element (PPE) module to the Earth-Moon L2 Southern 9:2 Near Rectilinear Halo Orbit. We propose algorithmic enhancements to improve the global search for robust solutions, and present a comprehensive analysis of two approaches: a nonconditional approach which involves a purely random search for robust solutions versus a conditional approach which involves warm-starting the search for robust solutions using the non-robust solutions. Our results indicate that by using non-robust solutions as initial guesses for the robust solutions, it is possible to achieve significant improvements in both the rate of convergence and the robustness of the final solutions.