2 citations · 4 across the 8 of their papers we have counts for
11 papers
From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design
Mariko Storey-Matsutani, Anatoly Zlotnik, Lee Holguin +2
As the complexity of space missions increases, autonomous spacecraft guidance must accommodate transitions between mission phases with distinct dynamics, objectives, and constraint…
Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition
Mariko A. Storey-Matsutani, Richard Linares
Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adaptable to a variety of mission sc…
GUIDE: Guided Updates for In-context Decision Evolution in LLM-Driven Spacecraft Operations
Alejandro Carrasco, Mariko Storey-Matsutani, Victor Rodriguez-Fernandez +1
Large language models (LLMs) have been proposed as supervisory agents for spacecraft operations, but existing approaches rely on static prompting and do not improve across repeated…
Fine-Tuned Language Models as Space Systems Controllers
Enrico M. Zucchelli, Di Wu, Julia Briden +3
Large language models (LLMs), or foundation models (FMs), are pretrained transformers that coherently complete sentences auto-regressively. In this paper, we show that LLMs can con…
Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance
Julia Briden, Trey Gurga, Breanna Johnson +2
This work introduces Transformer-based Successive Convexification (T-SCvx), an extension of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient six-degr…
Diffusion Policies for Generative Modeling of Spacecraft Trajectories
Julia Briden, Breanna Johnson, Richard Linares +1
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-cons…
Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints
Julia Briden, Yilun Du, Enrico M. Zucchelli +1
This work introduces TrajDiffuser, a compositional diffusion-based flexible and concurrent trajectory generator for 6 degrees of freedom powered descent guidance. TrajDiffuser is a…
Enhancing Solar Driver Forecasting with Multivariate Transformers
Sergio Sanchez-Hurtado, Victor Rodriguez-Fernandez, Julia Briden +2
In this work, we develop a comprehensive framework for F10.7, S10.7, M10.7, and Y10.7 solar driver forecasting with a time series Transformer (PatchTST). To ensure an equal represe…
Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction
Julia Briden, Trey Gurga, Breanna Johnson +2
In this work, we present Transformer-based Powered Descent Guidance (T-PDG), a scalable algorithm for reducing the computational complexity of the direct optimization formulation o…
Constraint-Informed Learning for Warm Starting Trajectory Optimization
Julia Briden, Changrak Choi, Kyongsik Yun +2
Future spacecraft and surface robotic missions require increasingly capable autonomy stacks for exploring challenging and unstructured domains, and trajectory optimization will be…
Transformer-based Atmospheric Density Forecasting
Julia Briden, Peng Mun Siew, Victor Rodriguez-Fernandez +1
As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for at…