Today’s aerospace industry is working on autonomous systems to launch in space. Current satellite constellations in Low-Earth Orbit (LEO) are paving the way for future robotic experimentation in space. Here’s a look at how autonomous systems are evolving for aerospace applications
Ushering in the Space Age of AI
When the International Space Station (ISS) was launched in 1998, it marked a new partnership among the United States, Russia, Japan, Europe, and Canada. It signaled a beginning of international collaboration in space. The station features autonomous systems and functions as a research lab for the global science community. The ISS completes its orbit around Earth every 93 minutes, which amounts to 15 orbits per day.
This century, several thousand satellites have joined the ISS in space, occupying various altitude levels in low-earth orbit. The ISS houses long-term space travelers who work with autonomous machines that gather and transmit data. It also utilizes forms of AI such as machine learning (ML) software, which can provide analysis of data captured in space. Robotic equipment manages the spacecraft and its inhabitants, providing tools to measure and enhance diagnostic and prognostic performance.
Autonomous vs. Automation
Within the context of space, it is important to note the difference between autonomy and automation. An automated system does not make choices for itself. It simply follows a highly advanced script where all possible courses of action have already been made. When an automated system is met with an unplanned situation that does not have a previously identified solution, it stops and waits for human intervention
When it comes to an autonomous system, it can respond to and rectify issues without human intervention. This problem-solving functionality is crucial because the communications latency between spacecraft and earth-based mission control centers can be as long as 40 minutes. Fundamentally, the ability to run thousands of solution scenarios in a short time frame can mean the difference between mission success or failure
Machine Learning in Space
One of the most important forms of AI used in space is fault management. ML software is constantly scanning systems looking to predict and detect vulnerabilities that trigger automated response. Fault management is a process that involves verification and validation. ML is a subset of AI and is distinguished in the sense that ML teaches itself through scanning a constantly growing database.
As big data is fed into an algorithm, ML programs process information similar to how humans do. People typically make decisions based on choosing the best solution from a set of options. ML-based robots are able to make decisions based on historical data and probability factors. The machine will make decisions based on how the options are prioritized by the programmer. ML can also be used to send alerts to analysts when new risks arise.
Advanced ML encompasses specialities such as deep learning (DL), in which the machine teaches itself to perform complex tasks such as image recognition. By feeding the system various photos of an object from different perspectives, the machine builds an image “in its mind” capable of memorizing and recognizing visuals. These capabilities have yet to be fully utilized for space applications
Groundwork for Future Space Applications
NASA scientists are currently exploring AI for space applications particularly to improve satellite operations. The fact that satellites only have a lifespan of about 15 years means they need to be monitored to determine when end-of-life conditions appear. Robots are more capable than astronauts of gathering and analyzing thousands of data points in mere seconds.
ML can further play a deep and powerful role in gathering Earth observation data from a spacecraft perspective. More knowledge is likely to be learned from robots than humans that travel in space. Mars rovers, for example, are already smart enough to teach themselves how to navigate on another planet, whereas that achievement might be difficult for a human without the help of robots. The concepts of space travel and space communications have been around a while, but the technology has finally come of age to generate actionable data about space that’s useful to humans. AI still has limitations, though, as it will still take decades for robots to take over the aerospace industry. In the meantime, researchers will continue to work on technology solutions that address gaps in the availability of human input during spacecraft operations. Altogether, the rise of autonomous systems in outer space will help increase equipment longevity for deep space exploration out into the farthest corners of the universe.