Stardust-R · Project

AI-Powered Space Debris Tracking and Asteroid Threat Assessment for Satellite Operators

transportIdeaTRL 2Thin data (2/5)

Imagine Earth's orbit is a busy highway, and it's getting more cluttered with broken-down cars (space debris) every year. Meanwhile, there are rocks flying through the neighborhood (asteroids) that could cause real damage. Stardust-R trained 15 specialists to build better tools for tracking all that orbital junk, predicting collisions, and figuring out which asteroids we should worry about. They also worked on tiny autonomous spacecraft that could one day land on asteroids to study them up close.

By the numbers

Early Stage Researchers trained across the network

partner organizations in the consortium

countries represented in the network

total deliverables produced

industry partners involved

The business problem

What needed solving

Earth's orbit is getting dangerously crowded with debris, and planned mega-constellations will make it worse. Satellite operators face growing collision risks but lack accurate prediction tools and traffic management systems. At the same time, small asteroids remain poorly catalogued, leaving planetary defense teams without clear priorities for which ones to track, explore, or deflect.

The solution

What was built

The project produced 42 deliverables including AI tools for space traffic management, collision probability models with uncertainty quantification, a criticality index for small asteroids, autonomous navigation algorithms for nano-spacecraft, and educational wiki resources on chaos dynamics, debris linkage, and asteroid criticality. The primary output was 15 trained specialists across these domains.

Audience

Who needs this

Commercial satellite operators managing mega-constellations (e.g., broadband, Earth observation)Space insurance underwriters pricing collision and debris riskDefense contractors developing space situational awareness systemsSpace agencies building planetary defense programsAsteroid mining startups needing target identification and autonomous landing capabilities

Business applications

Who can put this to work

Satellite Operations & Telecommunications

enterprise

Target: Commercial satellite operators and mega-constellation providers

If you are a satellite operator managing assets in increasingly crowded orbits — this project developed AI-based space traffic management tools and collision probability models that could help you predict and avoid debris impacts. With 15 research projects across 23 partner organizations, the network produced methods to correlate debris families back to their parent objects, giving operators better situational awareness.

Space Insurance & Risk Assessment

enterprise

Target: Aerospace insurance underwriters and space risk consultancies

If you are an insurance firm underwriting satellite missions and struggling to price collision risk — this project built uncertainty quantification methods for celestial mechanics that improve impact and collision probability predictions. These models, developed across 11 countries with 3 industry partners, could sharpen your actuarial models for orbital asset coverage.

Aerospace & Defense

enterprise

Target: Space agencies and defense contractors developing planetary defense systems

If you are a defense contractor or agency tasked with planetary protection — this project created a criticality index for small asteroids that identifies which ones need exploration, which can be exploited for resources, and which require deflection. They also developed algorithms for autonomous nano-spacecraft to explore and land on minor bodies.

Frequently asked

Quick answers

What would it cost to license or access these tools?

Based on available project data, Stardust-R was a Marie Curie training network (MSCA-ITN), meaning its primary output is trained researchers and published research rather than commercial products. Licensing terms would need to be negotiated directly with the University of Strathclyde or the specific partner institution that developed the tool of interest.

Can these tools work at industrial scale for real-time satellite operations?

The project developed AI tools and methods for space traffic management across 15 research sub-projects. However, as a training network, outputs are primarily research-grade algorithms and models. Scaling to operational real-time systems would require further engineering and integration work.

What is the IP situation — who owns the research outputs?

IP from MSCA-ITN projects typically belongs to the host institution of each Early Stage Researcher. With 23 partners across 11 countries, IP is likely distributed. Contact the University of Strathclyde as coordinator for guidance on specific outputs.

How accurate are the collision probability predictions?

The project specifically aimed to quantify uncertainty in celestial mechanics to accurately predict the probability of impact and collision. Based on available project data, the 42 deliverables include research on dynamics characterization, but specific accuracy benchmarks are not provided in the summary data.

Is this relevant if we only operate in low Earth orbit?

Yes. The project's first objective was to globally characterize the dynamics of objects around the Earth to define disposal solutions. This covers LEO, where debris density is highest and mega-constellation traffic is growing fastest.

What is the timeline to get something operational from this research?

The project closed in June 2023 after 4.5 years of research. The outputs are primarily trained specialists (15 Early Stage Researchers) and research publications. Moving any specific algorithm to operational deployment would likely require additional development and testing phases.

Consortium

Who built it

The Stardust-R consortium is heavily academic: 15 of 23 partners are universities, with only 3 industry partners and just 1 SME (13% industry ratio). This is typical for MSCA-ITN training networks, which prioritize research training over commercial development. The network spans 11 countries including non-EU partners in Japan, the US, and Serbia, giving it broad geographic reach. For a business looking to access outputs, the low industry involvement means most results live in academic settings and would need translation to commercial applications. The coordinator, University of Strathclyde in the UK, is the primary point of contact.

UNIVERSITY OF STRATHCLYDECoordinator · UK
UNIVERSITA DEGLI STUDI DI ROMA TOR VERGATAparticipant · IT
ARIZONA BOARD OF REGENTSpartner · US
ACADEMY OF ATHENSparticipant · EL
ARISTOTELIO PANEPISTIMIO THESSALONIKISpartner · EL
DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBHparticipant · DE
DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EVparticipant · DE
UNIVERSITATEA ALEXANDRU IOAN CUZA DIN IASIparticipant · RO
UNIVERSITA DI PISAparticipant · IT
UNIVERSITAET BREMENpartner · DE
Faculty of Mathematics, University of Belgradeparticipant · RS
THE UNIVERSITY OF TEXAS SYSTEMpartner · US
POLITECNICO DI MILANOparticipant · IT
EUROPEAN SPACE AGENCYpartner · FR
CENTRE NATIONAL D'ETUDES SPATIALES - CNESpartner · FR
UNIVERSIDAD AUTONOMA DE MADRIDparticipant · ES
UNIVERSIDAD POLITECNICA DE MADRIDparticipant · ES
AIRBUS DEFENCE AND SPACE LTDpartner · UK
UNIVERSIDAD CARLOS III DE MADRIDpartner · ES
TECHNISCHE UNIVERSITEIT DELFTparticipant · NL
DEIMOS SPACE SOCIEDAD LIMITADA UNIPERSONALparticipant · ES

How to reach the team

University of Strathclyde, UK — search for the Stardust-R project lead in the Mechanical & Aerospace Engineering department

Order a report on this consortium See UNIVERSITY OF STRATHCLYDE profile →

Next steps

Talk to the team behind this work.

Want to connect with the Stardust-R team about space debris tracking or asteroid assessment tools? SciTransfer can arrange an introduction and help evaluate which research outputs match your operational needs.

Order a report on this topic More in Transport & Mobility