Space Debris & AI: Machine Learning for Debris Analysis

Inspiration

Space debris is a relevant problem that is growing due to commercializing satellite technology, such as Starlink. We predict that this will result in an increase of space debris in the future. We wanted to incorporate our knowledge of machine learning into the project to use our skills to create a real life solution to the problem before it grows bigger.

What it does

Groups debris orbits together using SATCAT data and KMeans clustering to generate 7 orbit paths for debris removal machines to take.

How we built it

Python and Google Colab, using SATCAT data

Challenges we ran into

Data cleaning, controlling randomization, understanding the TLE data format and different variables used to categorize orbits

Accomplishments that we're proud of

Being able to visualize and generate our different paths

What we learned

Debris removal is challenging but solvable

What's next for Space Debris & AI: Machine Learning for Debris Analysis

More accurate predictions and understanding the debris field better

Built With

• collab
• python
• reentry
• satcat