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

The increasing strategic emphasis on long-term cislunar operations has catalyzed academic research efforts in recent years toward the development of efficient low-thrust mission architectures to key cislunar orbits. These missions, typically characterized by long thrust arcs, are particularly vulnerable to missed thrust events, which can disrupt mission performance if not adequately addressed early in the trajectory design process. Existing approaches for missed thrust design involve solving high-dimensional nonlinear programs, where constructing effective initial guesses can be challenging. Efficient global search approaches are therefore essential during the preliminary mission design phase, where rapid exploration of the solution space is necessary under evolving operational constraints. To improve the computational efficiency, solution quality, and depth of robustness of solutions from global search, we compare two initial guess generation strategies: a baseline nonconditional approach, which samples from a static distribution with global support, and a conditional approach, which generates initial guesses conditioned on solutions to simpler problems with lower depths of robustness. The conditional approach offers a sequential procedure for solving increasingly robust problems. We validate the improvements in the conditional approach using a low-thrust, minimum-fuel case study for the Lunar Gateway Power and Propulsion Element, where our results show significant improvements in feasibility ratio, convergence rate, and solution quality (measured by reduced propellant consumption), demonstrating its potential in missed thrust design.