Diffusion Policies for Generative Modeling of Spacecraft Trajectories

• URL: http://arxiv.org/abs/2501.00915v1
• Date: Wed, 01 Jan 2025 18:22:37 GMT
• Title: Diffusion Policies for Generative Modeling of Spacecraft Trajectories
• Authors: Julia Briden, Breanna Johnson, Richard Linares, Abhishek Cauligi,
• Abstract summary: A key shortcoming in current machine learning-based methods for trajectory generation is that they require large datasets. In this work, we leverage compositional diffusion modeling to efficiently adapt out-of-distribution data. We demonstrate the capability of compositional diffusion models for inference-time 6 DoF minimum-fuel landing site selection.
• Score: 1.2074552857379275
• License:
• Abstract: 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.

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