标题： 扩散政策在航天器轨迹生成建模中的应用 显示英文标题

标题： Diffusion Policies for Generative Modeling of Spacecraft Trajectories

摘要： 机器学习在解决轨迹生成问题以及为资源受限的航天器在线使用轨迹优化铺平道路方面展现出了非凡的潜力。然而，当前基于机器学习的轨迹生成方法存在一个关键的不足之处，即它们需要大量的数据集，并且即使是原始轨迹设计需求的微小变化，也需要重新训练新的模型来学习参数到解的映射关系。 在这项工作中，我们利用组合扩散建模，在少量样本框架下高效适应分布外数据和问题变化，用于6自由度（DoF）动力下降轨迹生成。与只能学习单一特定轨迹优化问题潜在结构的传统深度学习方法不同，扩散模型是一种强大的生成建模框架，它将解表示为概率密度函数（PDF），这使得可以组合涵盖各种轨迹设计规范和约束条件的PDF。 我们展示了组合扩散模型在推理阶段6自由度最小燃料着陆点选择和可组合约束表示的能力。使用这些样本作为6自由度动力下降制导的初始猜测，能够实现动态可行且计算高效的轨迹生成。 显示英文摘要

摘要： Machine learning has demonstrated remarkable promise for solving the trajectory generation problem and in paving the way for online use of trajectory optimization for resource-constrained spacecraft. However, a key shortcoming in current machine learning-based methods for trajectory generation is that they require large datasets and even small changes to the original trajectory design requirements necessitate retraining new models to learn the parameter-to-solution mapping. In this work, we leverage compositional diffusion modeling to efficiently adapt out-of-distribution data and problem variations in a few-shot framework for 6 degree-of-freedom (DoF) powered descent trajectory generation. Unlike traditional deep learning methods that can only learn the underlying structure of one specific trajectory optimization problem, diffusion models are a powerful generative modeling framework that represents the solution as a probability density function (PDF) and this allows for the composition of PDFs encompassing a variety of trajectory design specifications and constraints. We demonstrate the capability of compositional diffusion models for inference-time 6 DoF minimum-fuel landing site selection and composable constraint representations. Using these samples as initial guesses for 6 DoF powered descent guidance enables dynamically feasible and computationally efficient trajectory generation.