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Artificial intelligence is used to solve problems big and small – on earth and beyond. The rapid developments in AI are very promising for advancing space exploration by leaps and bounds. From personal support for astronauts to a better understanding of the sun, this is how AI can help with space exploration.
Virtual help for astronauts
We’re far from a real R2D2, but AI-based virtual assistants that can help astronauts are currently in development. A recently developed KI-Assistant is able to identify the risks and dangers of long space missions by sensing things like increased carbon dioxide levels in the spacecraft’s atmosphere. These assistants can alert the crew to sensor malfunctions and other issues that require inspection.
Also in 2019, a similar AI assistant known as the Cimon visited the International Space Station ISS, where she will “live” for three years. Cimon is tested to reduce the stress of astronauts by performing the tasks required of him. It’s an interstellar butler, if you will. In addition, NASA developed Robonaut, an AI bot that works with the astronauts in the difficult conditions of space – and even takes on tasks that are potentially too dangerous for humans.
Study the sun
Understanding the sun is a unique challenge. Solar telescopes expire quickly due to intense solar radiation and an “infinite flow of solar particles” NASA. AI is being used by some researchers to improve solar research. AI can help ensure that the data used by scientists to understand the sun is still accurate and allow scientists to periodically recalibrate their telescopes to maintain data quality.
The result: Since 2010, NASA’s Solar Dynamics Observatory has been able to continuously monitor the sun and record images over 10 wavelengths every 12 seconds. Using machine learning and AI, they can determine how much the degradation will affect the telescopes’ current images and how much calibration is required for each one. This leads to a much better understanding of the most important star in our universe.
Optimized mission planning
Exploring the great beyond requires planning for an abundance of unknowns. Because astronauts go where few, if any, have traveled before, they have to rely on limited information obtained from previous studies or space missions. This information can be both scarce and inaccessible.
Mission planning teams are now using AI to make it much easier to merge information from previous explorations. “One day there could be a smarter system – similar to Wikipedia, but with artificial intelligence that can answer complex queries with reliable and relevant information – to help with the early planning and planning of new space missions,” he wrote World Economic Forum.
An AI assistant is already in place to reduce the time it takes to create a preliminary mission design. Additionally, daphne is an intelligent wizard for the design of earth observation satellite systems. Clearly, AI has the potential to reduce the number of man-hours required to create a mission plan and quickly gather feedback and information.
Avoidance of space debris
As it turns out, space is actually running out. There is almost 34,000 objects larger than 10 cm that could endanger the existing space infrastructure, cause collisions or otherwise damage space missions.
Machine learning helps to avoid possible collisions with space debris by “Collision avoidance maneuver design. “The AI performs complex calculations to map the best possible path for a satellite or spacecraft to avoid oncoming debris. These models can help improve security and keep satellites orbiting longer.
Understand satellite data
Satellites produce a huge amount of data – so much that scientists and researchers cannot possibly process it in real time. AI can help and create reports that have a real impact on life on earth. For example, AI can use satellite data to estimate Heat storage in cities or meteorological data to better estimate the wind speed. AI can also help scientists understand when satellites need to be recalled for maintenance, identify problems or malfunctions, and predict performance over time.
Image source: iurii / Shutterstock.com
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