April 15, 2024

Seven Aerospace Applications of Artificial Intelligence

3 min read

Artificial intelligence has been used so extensively in spaceflight that some have questioned, “Why do we still have to send people to work in the sky?” — but of course, that’s another story.

AI is an important tool in the search for knowledge in the modern age of space exploration; scientists use AI and ML models to automate spacecraft operations, analyse large amounts of data, and even save lives, according to an article published by US education website Springboard on 8 February. The article lists 7 typical space applications of AI.

1. Robotics

AI’s ability to autonomously navigate around obstacles is nothing new. Mars rovers like Curiosity have been performing fully autonomous navigation on the surface of Mars for more than a decade.

The rover’s sensors detect hazards present in the environment, such as rocks, craters, and other conditions. The onboard AI system then analyses the data to determine the best path forward, ensuring that the rover can pass safely without any risk of collision.

AEGIS, a computer vision-based detection system running on the Trail rover, found interesting rocks that could be sampled. This is a huge step forward and lays the foundation for a fully autonomous space exploration rover.

2. Satellite operations

AI is revolutionising the way satellites are operated, providing more efficient, smarter and faster solutions for managing them. For example, SpaceX uses an AI-driven algorithm to help its satellites avoid collisions with other satellites in orbit. The algorithm uses comprehensive data from the satellite’s sensors – including its position and velocity – to identify potentially dangerous manoeuvres, and an onboard computer controls and adjusts the satellite’s speed and direction to avoid collisions.

The AI also optimises the process of manoeuvring the satellite into the correct orbit, reducing the amount of propellant required and the time it takes to reach a working orbit.

3. Data analysis

AI contributes to data analysis in space exploration by providing a more accurate and efficient method of analysing mission data. Machine learning algorithms can help identify data patterns from satellites, probes, and other space exploration tools to detect anomalies that could indicate potential discoveries or risks.

AI can also help identify data trends and provide more detailed insights through predictive analytics than traditional data analysis methods.

4, Astrogeology

Using AI, scientists can detect and classify geological features on planets and moons, such as craters, volcanoes and other surface features. The technology is also used to generate detailed 3D models of planetary surfaces to help scientists better understand the environment, history of the planet or its moons.

5, Rocket Recovery

SpaceX has been enhancing and improving the way rockets fly. They use AI to monitor and analyse data from the rocket’s sensors and telemetry systems to make better decisions and control the rocket’s trajectory and speed more accurately.SpaceX also uses AI to automate certain parts of the rocket’s landing procedure, such as controlling the engines and landing gear, to make sure that the rocket is in the best possible position to land.

6. Mapping stars and galaxies

Thanks to the ability of AI algorithms to detect, classify and recognise patterns in stellar and galactic data, astronomers can now map the universe in unprecedented detail. These algorithms allow them to accurately identify stars and galaxies in space and even understand their physical properties (such as mass and age). At the same time, by using AI to predict the behaviour of stars and galaxies at different times, scientists are able to gain valuable knowledge to use in future mapping and exploration efforts.

7. Preventive maintenance

We have already seen that AI analyses large amounts of data to help with satellite operations and rocket landing procedures. In addition, it can use the same data to determine what preventative maintenance work should be carried out.

Machine learning models can predict future failures or performance issues and give an action plan to reduce the risk. This could significantly reduce maintenance costs and help save countless lives.

Seven Aerospace Applications of Artificial Intelligence

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