Machine learning to remove space debris

October 30, 2020 // By Nick Flaherty
Machine learning to remove space debris
The Clearspace-1 project will use machine learning algorithms, trained on simulated data, to capture a payload adapter in space.

Researchers are using machine learning algorithms trained on simulations of space debris as part of a key project.

With more than 34,000 pieces of junk orbiting around the Earth, their removal is becoming a matter of safety. Earlier this month an old Soviet Parus navigation satellite and a Chinese ChangZheng-4c rocket were involved in a near miss and in September the International Space Station conducted a manoeuvre to avoid a possible collision with an unknown piece of space debris.

A project led by ClearSpace-1, a spin off from research lab EPFL in Zurich, will recover the now obsolete Vespa Upper Part, a payload adapter orbiting 660km above the Earth that was once part of the European Space Agency’s Vega rocket. The mission, set for 2025, aims to ensure that it re-enters the atmosphere and burns up in a controlled way.

One of the first challenges is to enable the robotic arms of a capture rocket to approach the Vespa from the correct angle. For this is will use a camera to control the grasping of the Vespa and then pull it back into the atmosphere.

“A central focus is to develop deep learning algorithms to reliably estimate the 6D pose (3 rotations and 3 translations) of the target from video-sequences even though images taken in space are difficult. They can be over- or under-exposed with many mirror-like surfaces,” said Mathieu Salzmann in EPFL’s Computer Vision Laboratory led by Professor Pascal Fua, in the School of Computer and Communication Sciences.

EPFL’s Realistic Graphics Lab is simulating what this piece of space junk looks like as the ‘training material’ to help Salzmann’s deep learning algorithms improve over time.

“We are producing a database of synthetic images of the target object, including both the Earth backdrop reconstructed from hyperspectral satellite imagery, and a detailed 3D model of the Vespa upper stage. These synthetic images are based on measurements of real-world

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