Preliminary trajectory design is a global search problem that seeks multiple qualitatively different solutions to a trajectory optimization problem. Due to its high dimensionality and non-convexity, and the frequent adjustment of problem parameters, the global search becomes computationally demanding. In this paper, we exploit the clustering structure in the solutions and propose an amortized global search (AmorGS) framework. We use deep generative models to predict trajectory solutions that share similar structures with previously solved problems, which accelerates the global search for unseen parameter values. Our method is evaluated using De Jong's 5th function and a low-thrust circular restricted three-body problem.

翻译：初步轨迹设计是一个全局搜索问题，旨在寻找轨迹优化问题中多个本质不同的解。由于问题的高维性、非凸性以及参数频繁调整，全局搜索的计算成本很高。本文利用解中的聚类结构，提出了一种摊销全局搜索（AmorGS）框架。我们采用深度生成模型来预测与已解决问题共享相似结构的轨迹解，从而加速未知参数值下的全局搜索。该方法在De Jong第五函数和低推力圆型限制性三体问题上进行了验证。