Our research findings are published in leading journals and presented at international conferences, focusing on space engineering, AI integration, and astrodynamics.
Molly K. Macauley Award for Research on Space Economics
Award Winner, 2026
Develops Catalyst as an economics-informed decision-support framework that connects E-MOCAT orbital-debris simulations with policy and economic analysis. The work translates collision risk, population growth, shell-level density, fragmentation, and orbital-capacity effects into interpretable metrics for regulators, operators, and mission planners while preserving uncertainty and evidentiary limits.
View PDF →Molly K. Macauley Award for Research on Space Economics
Finalist, 2026
Presents Catalyst as a simulation-grounded explainable AI system for orbital-capacity and space-sustainability decision support. The study evaluates whether AI-generated explanations remain grounded in E-MOCAT simulation evidence, policy and economic sources, provenance, assumptions, and defined evidentiary limits.
View PDF →Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS)
September 2026
Introduces a simulation-driven framework that connects space situational awareness, E-MOCAT debris modeling, and evidence-bounded policy interpretation. A 500-run matched-seed simulation campaign evaluates policy-relevant controls including post-mission disposal, fragmentation prevention, launch activity, and integrated mitigation to quantify their effects on the future orbital environment.
View PDF →Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS)
September 2026
Presents the Catalyst Code World Model assistant for connecting governance requirements, bounded E-MOCAT model settings, simulation evidence, and AI-supported interpretation. The work combines a 500-run orbital-debris simulation campaign with controlled evaluations of Catalyst-adapted language models to examine traceability, repeatability, evidence grounding, and policy-oriented decision support.
View PDF →arXiv
2026
An AI-augmented model predictive control framework for safe and adaptive spacecraft rendezvous and proximity operations. The approach combines a structured MPC controller with an adaptive AI-based supervisory layer that modifies controller behavior as mission conditions change.
View PDF →Scientific Reports
In-press 2025
Benchmarking large language model performance in fundamental astrodynamics problems for space engineering applications.
View PDF →AMOS
Best Pitch Award, September 2024
Temporal analysis and quantification for policies in space sustainability using the MOCAT framework.
View PDF →AMOS
September 2024
Early classification of space objects using astrometric time series data and machine learning techniques.
View PDF →AAS
August 2024
Comparative analysis of analytical and data-driven Koopman operators for the J2 problem with atmospheric drag.
View PDF →AAS
August 2024
Application of fine-tuned language models as controllers for space systems.
View PDF →Advances in Space Research
73 (1), 64–84, 2024
Development of proper elements for Resident Space Objects and their application to maneuver detection.
View PDF →Celestial Mechanics and Dynamical Astronomy
135 (4), 44, 2023
Investigation of space debris of unknown origin using proper elements and neural network techniques.
View PDF →AAS
August 2021
Secular dynamics-based features for characterizing small debris clouds after fragmentation events.
View PDF →AAS
August 2020
Neural network-based Two-Line Element error modeling and quantification for improved orbital predictions.
View PDF →Science Advances
6 (48), eabd1313, 2020
Discovery and analysis of chaotic transport structures in the Solar System known as the arches of chaos.
View PDF →Explore news coverage, institutional features, and video content highlighting XDLab research, student achievement, and participation in the Capture the Satellite competition.
University News · January 23, 2026
Embry-Riddle highlights XDLab’s first-place finish in the Capture the Satellite competition at AIAA SciTech Forum 2026. The feature discusses the team’s hybrid use of classical orbital mechanics and artificial intelligence, as well as the students’ experience developing autonomous spacecraft-control methods.
Read Article →Industry Feature · January 16, 2026
Aerospace America reports on XDLab’s victory in the Capture the Satellite competition, where the team navigated a spacecraft through a rendezvous scenario involving a malfunctioning target and an opposing guard satellite.
Read Article →Video · AIAA SciTech Forum 2026
Watch the Forum 360 discussion and competition overview, including the spacecraft autonomy challenge and the student teams’ technical approaches.
Watch on YouTube →Our publications have been widely cited and have contributed to significant advances in space engineering, AI integration, and astrodynamics research.
Selected Publications
Published in leading journals including Scientific Reports, Science Advances, and Advances in Space Research.
Conference Presentations
Regular presentations at major international conferences including AAS and AMOS.
Research Focus
Bridging the gap between traditional space engineering and modern artificial intelligence techniques.