Problem dimensionality reduction in low-thrust gravity assist trajectory optimization through shape-based intercept arcs

A bi-level nested optimization approach combining global optimization algorithms with shape-based low-thrust trajectory design methods enables preliminary design of fuel-efficient low-thrust gravity assist (LTGA) trajectories adhering to thrust-related constraints. This study delves into enhancing its optimization process by incorporating intercept-type arcs, which eliminate the need for terminal velocity as a boundary condition, to reduce the number of decision variables in the global optimization problem. Although this dimensionality reduction reduces the reachable solution space, the impact on the quality of solutions is anticipated to be minimal owing to the redundancy in swing-by geometry. We present three novel LTGA problem models that leverage intercept-type arcs, alongside two existing models that utilize solely rendezvous-type arcs. These five models are assessed across various hypothetical LTGA trajectory design scenarios involving one or two gravity assists. Models relying on intercept-type arcs outperform those utilizing rendezvous-type arcs in terms of optimization time, mean solution cost, and optimization success rate. This suggests that the problem dimensionality reduction through intercept-type arcs can expedite the nested optimization approach for LTGA trajectory design while maintaining solution (c) 2025 Published by Elsevier B.V. on behalf of COSPAR.