Global Optimization Framework for Automated Low-Thrust Gravity-Assist Trajectory Design
Gravity-assist trajectory design requires searching numerous leg combinations, but applying nonlinear programming (NLP) for low-thrust trajectory optimization to all combinations is computationally prohibitive. Therefore, conventional frameworks first perform broad search using lightweight trajectory models such as Lambert-based calculation and simplified deep-space maneuver models, while pruning infeasible candidates. However, to maintain efficiency, conventional approaches handle only low-dimensional formulations with limited constraints such as up to a couple of impulse maneuvers per leg. Consequently, they cannot accommodate the constrained multivariable optimization required for low-thrust design, running the risk of prematurely pruning viable trajectories. To address this, this paper introduces a convex pruning approach capable of solving constrained multivariable problems directly within broad search while retaining the computational efficiency. We then augment the broad search stage with a robust local low-thrust optimization algorithm based on thrust regularization, which together enable global exploration and optimization in an automated fashion. We apply the proposed framework in a BepiColombo-inspired scenario involving nine gravity assists, successfully demonstrating its broad search capability to discover a far greater number of solutions (36,335) compared to a conventional approach (4,664) while identifying a wider feasible launch window by one year.