This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Docking with the ISS may seem simple. However, actually doing so shows how difficult orbital mechanics can be.
It's like traveling down a highway at 28,000 km/hr (17,000 mph) and parallel parking in an open garage on a multibillion-dollar laboratory traveling at the same speed. If you try to accelerate forward, you actually drift up, and there's no air friction to naturally slow you down. Oh, and if you hit the lab, everyone aboard both your craft and the station dies, and the resulting debris field could wipe out dozens of satellites and even harm people on the ground.
For decades, aerospace engineers have docked successfully using hard-coded physics equations and human pilots to make corrections. But now, a new paper posted to the arXiv preprint server from researchers at Stanford is taking a shot at building an AI to perform a series of "mental simulations" that could fundamentally change how future spacecraft interact with each other. Their solution is called the Out-of-this-World-Model (OWM), but before we get to what that is, it's best to recap how we typically navigate in low Earth orbit (LEO).
Traditionally, navigation computers use a type of algorithm called a guidance, navigation and control (GNC) algorithm. They also take advantage of another mathematical tool called an extended Kalman filter, which helps them take in data from GPS receivers and star trackers and output thruster burn duty cycles. Unfortunately, Kalman filters are terrible at processing high-speed video.
So engineers turn to traditional computer vision techniques. However, those techniques face a huge hurdle in space—they're very fragile. If sunlight glints off a solar array or part of the target is suddenly covered in shadow, those computer vision algorithms can be thrown off.
Sounds like a perfect place for AI, right? Researchers thought so too, so they began applying reinforcement learning (RL) algorithms—the kind famously behind computers winning at chess or Dota. However, they're only good at dealing with scenarios with very specific sets of rules.
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