AI Battling Spacejunk

AI Battles the Bane of Space Junk Neural nets navigate nuances of rogue orbital objects—and near-misses    By   Sara Well

Photos from low-earth orbit (LEO) are often strikingly beautiful. But what they typically fail to capture is the tens of thousands of debris pieces, or “space junk,” that orbit around Earth’s face like hungry mosquitos—and threaten to hit satellites and other orbiting assets with enough force to be destructive. Such pieces of space junk ---only a fraction of which space agencies like NASA and ESA can track with ground-based telescopes--- are only going to multiply as mega-constellations like Starlink or OneWeb enter LEO.

A growing number of planners and researchers are concerned about whether further crowding could lead to a higher risk of catastrophic collisions that knock out communications satellites or even one day send fiery debris back home to Earth. To better anticipate and avoid these situations, some are turning to computer simulations and artificial intelligence to better see what humans cannot.

Researchers are, for instance, using machine learning to investigate methods of debris removal and reuse. In a paper presented earlier this year at the European Space Agency’s second NEO and Debris Detection Conference in Darmstadt, Germany, Fabrizio Piergentili and colleagues presented results of their evolutionary “genetic” algorithm to monitor the rotational motion of space debris.

“Objects that move too fast cannot be easily captured,” Piergentili says. “So, if I have one mission to go into orbit, it is better to identify objects that move slowly, so they are easier to catch.”

In addition to developing neural networks to anticipate these collisions ---which can take time and considerable resources to train and test--- other researchers like Lieutenant Colonel Robert Bettinger are turning to computer simulations to anticipate satellite behavior.... '