Diffusion Policies for Generative Modeling of Spacecraft Trajectories

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-tosolution 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 degreeof- 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.

搜集汇总

背景与挑战

背景概述

该数据集是关于航天器轨迹生成的机器学习研究，具体聚焦于使用组合扩散模型来高效生成6自由度动力下降轨迹。它解决了传统方法需要大量数据和重新训练的问题，支持少样本适应和推理时的约束组合。数据集以PDF文件形式提供，包含相关论文，用于演示如何为轨迹优化提供动态可行且计算高效的初始猜测。