Guidance, navigation, and control
Optimal and autonomous decision-making for landing, proximity operations, transfers, and other constrained aerospace missions.
Explore GNC research →Guidance & control · Space safety · Scientific machine learning · Astrodynamics
Engineering intelligence for the next frontier in aerospace engineering.
The CIRO research group develops theory, algorithms, and computational tools for optimal guidance and control, space situational awareness, and scientific machine learning for aerospace applications, with astrodynamics providing the physical foundation.
Department of Mechanical & Aerospace Engineering
University of South Florida · Tampa
Our mission
Safer autonomy. A more sustainable space environment.
CIRO advances autonomous aerospace systems that can make safety-critical decisions under uncertainty—and whose behavior can be understood, trusted, and certified.
We combine optimal guidance and control, astrodynamics, and scientific machine learning to develop trustworthy learning-enabled controllers, rigorous decision-making tools, and space-environment models. Our goal is to enable safe autonomous operations while helping preserve the long-term sustainability of the space environment.
Meet the laboratory ↗Research areas
Four connected areas.
One ambitious mission.
Physics-based models, optimization, estimation, and learning come together to improve aerospace autonomy, safety, and sustainability.
Space situational awareness & orbital sustainability
Orbit determination, object characterization, surveillance, and orbital-capacity analysis for a safer, more sustainable space environment.
Explore SSA research →
Scientific machine learning for aerospace applications
Physics-informed learning, interpretable dynamics, and stability certification for safety-critical aerospace applications.
Explore SciML research →
Astrodynamics
Orbital mechanics, dynamical systems, mission design, and environmental evolution from Earth orbit to cislunar and small-body regimes.
Explore astrodynamics →Selected research
Ideas become
mission capability.
A preview of CIRO’s work in optimal guidance and control, certified ML controllers, orbit determination, and orbital capacity.
01Optimal guidance and control
Intelligent guidance for constrained missions
Physics-based optimization and learning methods produce fast, reliable guidance and control solutions for landing, orbital transfers, inspection, and proximity operations.
02Trustworthy autonomy
Certified machine-learning controllers
Interpretable models, Lyapunov analysis, and stability certificates turn learning-enabled control policies into safer and more trustworthy aerospace autonomy.
03Space situational awareness
Physics-informed orbit determination
Physics-informed methods reconstruct and track trajectories from sparse observations without requiring an initial orbital estimate, supporting GEO, X-GEO, and cislunar awareness.
04Orbital sustainability
Orbital capacity and environment evolution
Source–sink population models quantify collision risk, debris growth, and the carrying capacity of orbital regions to support evidence-based decisions for long-term space sustainability.
Featured publications
Recent work from the research program.
Including physics-informed orbit determination for X-GEO space situational awareness.
Browse all publications →Pontryagin Neural Networks for High-Fidelity Cislunar Orbital Transfers
Conti, M.; D’Ambrosio, A.; Circi, C.; Furfaro, R.
Journal of Guidance, Control, and Dynamics 49(7), 1914–1926
Physics-Informed Orbit Determination for X-GEO Space Situational Awareness
Scorsoglio, A.; D’Ambrosio, A.; Le Corre, L.; Gray, B.; Reddy, V.; Furfaro, R.
Acta Astronautica 238, 271–285
Physics-Informed Pontryagin Neural Networks for Path-Constrained Optimal Control Problems
D’Ambrosio, A.; Benedikter, B.; Furfaro, R.
Journal of Guidance, Control, and Dynamics 48(8), 1861–1877 · Editor’s Choice
Carrying Capacity of Low Earth Orbit Computed Using Source-Sink Models
D’Ambrosio, A.; Linares, R.
Journal of Spacecraft and Rockets 61(6), 1447–1463
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