用生成模型加速航天器轨道优化，快速找到高质量多样解。
Global Search of Optimal Spacecraft Trajectories using Amortization and Deep Generative Models
- 构建条件生成模型，学习参数变化下最优解邻域的分布
- 在三体问题中实现比传统方法快数倍的全局搜索
- 揭示低推力轨道优化的多峰漏斗结构，适合航天任务设计
航天器轨迹优化是依赖参数的全局搜索问题，旨在生成高质量且多样化的解。传统数值方法受原最优控制问题、控制参数化方式及梯度求解器行为影响。本文将参数化全局搜索建模为在高质量解局部吸引域邻域上采样条件概率分布的问题，利用深度生成模型学习该分布，并预测解空间随参数变化的演化。在圆形限制性三体问题中的低推力轨迹优化任务上进行测试，结果表明该方法相比简单多起点法和基础机器学习方法有显著加速。论文还深入分析了低推力轨迹优化问题的多模态漏斗结构。
原文摘要 · Abstract (English)
Preliminary spacecraft trajectory optimization is a parameter dependent global search problem that aims to provide a set of solutions that are of high quality and diverse. In the case of numerical solution, it is dependent on the original optimal control problem, the choice of a control transcription, and the behavior of a gradient based numerical solver. In this paper we formulate the parameterized global search problem as the task of sampling a conditional probability distribution with support on the neighborhoods of local basins of attraction to the high quality solutions. The conditional distribution is learned and represented using deep generative models that allow for prediction of how the local basins change as parameters vary. The approach is benchmarked on a low thrust spacecraft trajectory optimization problem in the circular restricted three-body problem, showing significant speed-up over a simple multi-start method and vanilla machine learning approaches. The paper also provides an in-depth analysis of the multi-modal funnel structure of a low-thrust spacecraft trajectory optimization problem.
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