AI-driven collision risk prediction and debris optimization in low earth orbit
Emad Alkhazraji, Mohamed E. Yahia, R. E. Tolba, N. S. Alharthi · 2026
Read AI-driven collision risk prediction and debris optimization in low earth orbit by Emad Alkhazraji, Mohamed E. Yahia, R. E. Tolba, N. S. Alharthi (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13542115.txt.
Description
Emad Alkhazraji and three co-authors build an AI framework for judging crash risk in low Earth orbit, in a paper dated 2026. That band of space grows crowded with satellites and debris. Operators need to know which orbits are nearing their safe capacity.
The team first computed debris counts, mass spread, and close-approach risk across heights and orbit tilts. A fast AI stand-in model then learned to predict risk from those drivers. Studying the model's reasoning showed how height, tilt, crowding, and mass interact. Used inside a search tool that balances risk against usable capacity, the model found orbit plans offering much more room for only a modest rise in risk. The work could inform rules for sharing crowded space.
Catalog record
| CREATOR | Emad Alkhazraji, Mohamed E. Yahia, R. E. Tolba, N. S. Alharthi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |