发表机构
Tohoku University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于凸剪枝和推力正则化的全局优化框架，用于自动化低推力引力辅助轨迹设计，在九次引力辅助场景中发现36,335个解，远超传统方法，并拓宽发射窗口一年。
AI 中文摘要
引力辅助轨迹设计需要搜索大量的航段组合，但对所有组合应用非线性规划（NLP）进行低推力轨迹优化在计算上是不可行的。因此，传统框架首先使用轻量级轨迹模型（如基于兰伯特的计算和简化的深空机动模型）进行广泛搜索，同时剪除不可行的候选方案。然而，为了保持效率，传统方法仅处理低维公式，且约束有限，例如每航段最多进行几次脉冲机动。因此，它们无法容纳低推力设计所需的约束多变量优化，存在过早剪除可行轨迹的风险。为解决这一问题，本文引入了一种凸剪枝方法，能够在广泛搜索中直接求解约束多变量问题，同时保持计算效率。然后，我们通过基于推力正则化的稳健局部低推力优化算法增强广泛搜索阶段，共同实现自动化的全局探索与优化。我们将所提出的框架应用于一个受BepiColombo启发的场景，涉及九次引力辅助，成功展示了其广泛搜索能力，发现解决方案数量（36,335个）远超传统方法（4,664个），同时识别出更宽一年的可行发射窗口。
英文摘要
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.
