Initial Guess Generation for Low-Thrust Trajectory Design with Robustness to Missed-Thrust-Events

TL;DR

This paper compares two initial guess strategies for robust low-thrust trajectory design in cislunar space, demonstrating that a conditional global search improves convergence and solution quality in mission planning.

Contribution

It introduces a conditional global search method for initial guess generation, enhancing robustness and efficiency in low-thrust trajectory design under operational uncertainties.

Findings

Conditional search improves convergence rate

Enhanced robustness of solutions

Better solution quality in case studies

Abstract

The growing interest in cislunar space exploration in recent years has driven an increasing demand for efficient low-thrust missions to key cislunar orbits. These missions, typically possessing long thrust arcs, are particularly susceptible to operational uncertainties such as missed thrust events. Addressing these challenges requires efficient robust trajectory design frameworks during the preliminary mission design phase, where it is necessary to explore the solution space at a rapid cadence under evolving operational constraints. However, existing methods for missed thrust design rely on solving high-dimensional nonlinear programs, where generating effective initial guesses becomes challenging. To enhance computational efficiency, quality, and depth of robustness of solutions from global search, we compare two initial guess strategies: a baseline non-conditional global search, which samples from a static distribution with global support, and a conditional global search, which generates initial guesses conditioned on solutions to problems with less depth of robustness. The conditional search provides a sequential procedure for solving increasingly robust problems. We validate the improvements in the conditional approach using a low-thrust case study for the Lunar Gateway Power and Propulsion Element, where our results demonstrate that it significantly improves convergence rate and solution quality, highlighting its potential in preliminary robust trajectory design.