Space exploration has long piqued the interest of scientists and governments around the world since it contains the secret to humanity's origins as well as many other wonderful aspects of the cosmos, such as the possibility of extraterrestrial life.
The visible universe is made up of the areas of space that can be seen through telescopes. Scientists and explorers believe, however, that the cosmos may be greater.
Scientists have only examined around 4% of the observable universe, which consists of planets, stars, galaxies, and other celestial phenomena that astronomers and scientists can see and understand. The remaining 96 percent has yet to be discovered.
Artificial intelligence (AI) assists astronauts on their strenuous space trips and assists in the execution of space tasks that would be impossible if only human capabilities were used.
In space research, such as charting unknown galaxies, stars, black holes, and analyzing cosmic occurrences, as well as communication, autonomous starcraft navigation, monitoring, and system control, AI has demonstrated its immense potential and is a game-changer.
Mission planning and operations, data collecting, autonomous navigation and manoeuvring, and spacecraft maintenance are all areas where AI and machine learning are used in current space research missions. Future missions will have to rely on the same technology.
(Must Read: Top Applications of Artificial Intelligence )
Imagine how easy it would be for scientists and explorers to attain their goals and how it would affect our lives if we combined the ideas of these two gigantic phrases, AI and Space Exploration, keeping in mind recent breakthroughs in the field of machine learning and AI.
Let's combine these two concepts and see what's been done, what's happening, and what else could be done:
To get through the "seven minutes of fear" during the Perseverance mission, the Entry, Descent, and Landing (EDL) flight dynamics team had to rely significantly on AI for mission planning and complicated scheduling systems.
Because of the intricate planning required with these systems, mission scientists and engineers view scheduling to be a domain where AI approaches can be applied. Without AI, mission scheduling necessitates big teams working long hours to meet the mission's requirements.
By programming the spaceship to determine for itself how to intelligently execute a command given a specific function based on past experiences and its environment, we may eliminate the requirement for these human resources.
Realistic scheduling problems typically involve many constraints. The complexity of scheduling is substantially increased by balancing conflicting constraints and making trade-offs. We may apply artificial intelligence to address critical scientific and engineering mission restrictions and optimise the timeline to meet all requirements.
For space missions, AI is used for autonomous operations. The Italian start-up business AIKO developed a software library called MiRAGE that is utilised to enable autonomous operations for space missions as part of the European Space Agency's (ESA) technology transfer programme.
The spacecraft can execute autonomous replanning, detect events (both internal and external), and react accordingly using these procedures, ensuring mission objectives are met without the delays caused by ground-based decision-making.
AI and machine learning can also be used to evaluate operational risk analysis and assess safety-critical missions. These technologies enable risk mitigation systems to process vast volumes of data based on signs from normal operations as well as previous performance where anomalies occurred.
We couldn't confidently assign a risk level to occurrences without deep learning using ANNs, thus we'd need professional supervision. Once a model has been taught to recognise risk classification, it may use that information to score real-time risk assessments.
(Check out one of the frontrunners in the field of Space Exploration - SpaceX Success Story)
Ground infrastructures used to distribute and convey data collected from Earth-observing spacecraft, deep space probes, and planetary rovers have seen a dramatic growth in the volume of data collected over the last decade.
The capacity to optimise the massive amounts of data collected from scientific missions and evaluate it using AI automation has a favourable impact on how data is handled and distributed to end users.
To build maps and data sets, AI aboard spacecraft may independently discover and classify normal features, such as common weather patterns, and differentiate them from atypical patterns, such as smoke plumes from volcanic activity.
We can utilize AI to identify which data sets should be sent to ground segments for processing. We can also employ AI technology to eliminate data that is of little or no use. This may alleviate the difficulties or limits that space-to-ground networks face while transmitting massive amounts of data.
Are you familiar with the terms TARS and CASE? Yes, I'm referring to the robots from the well-known film 'Interstellar' (which, if you haven't seen it yet, I strongly urge you to do so). Imagine how beneficial TARS and CASE would be in supporting astronauts in real life if you recall their roles in the movie.
Scientists are working on artificial intelligence-based assistants to support astronauts on missions to the Moon, Mars, and beyond. These assistants are designed to anticipate and understand the crew's needs, as well as understand astronauts' emotions and mental health and take appropriate action in the event of an emergency.
So, how do they accomplish this? Sentiment analysis is the solution. Sentiment Analysis (also known as opinion mining or emotion AI) is a sub-field of Natural Language Processing (NLP) that aims to recognise and extract opinions from a given text across blogs, reviews, social media, forums, news, and other sources.
Robots, on the other hand, can be more useful when it comes to physical assistance, such as assisting with spacecraft piloting, docking, and handling harsh situations that are dangerous to people. The majority of it may seem speculative, but astronauts will benefit much from it.
(Founded by Jeff Bezos, learn more about Blue Origin which also operates in the field of Space Exploration)
The Kepler Telescope was built to determine the frequency of Earth-sized planets orbiting Sun-like stars, but these planets were on the cusp of being detected by the mission's detection sensitivity.
Even with a low signal to noise ratio, determining the occurrence rate of these planets needed automatic and precise assessment of the chance that individual candidates are truly planets.
To solve this constraint, Google and other scientists developed AstroNet K2, a Convolutional Neural Network that can determine whether a signal from Kepler's space telescope is a transiting exoplanet or a false positive generated by astrophysical or instrumental phenomena.
They discovered two new exoplanets, Kepler 80g and Kepler 90i, circling the Kepler 80 and Kepler 90 star systems, respectively, after training their neural network model to 98 percent accuracy.
Must Watch: Artificial Intelligence in Space | StarTalk
There are a plethora of different research projects involving the application of AI to space exploration. Nothing can be guaranteed, as with other AI applications.
At the end of the day, anything AI can do requires human intervention. AI is getting closer to delivering newer insights and showing to be a benefit for humanity in exploring interplanetary space with novel machines, projects, and researches with each new innovation.
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