This article has been written by Pathipati Sahithi Chowdary pursuing a Remote freelancing and profile building program from Skill Arbitrage.

This article has been edited and published by Shashwat Kaushik.

Introduction

If a machine can interpret data, learn from it, and apply that knowledge to do particular tasks, then the machine is said to have artificial intelligence.

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Artificial intelligence is used by many services that surround us today, such as Siri, the way emails are sorted into spam and inbox folders, and how you landed on this article by searching a few specific keywords on the Google search engine. They also play a crucial role in a few lesser-apparent situations, like disease detection and the screening of job applications. Whether we realise it or not, we are all exposed to AI.

Though AI has recently gained popularity on a broader scale, scientists, engineers, and science fiction writers have been thinking about it for a century. 

The popular movie Interstellar featured two artificial intelligence robot characters, “TARS” and “CASE,” who are seen planning courses to various destinations, calculating the spacecraft’s speed and spin, taking control of the spacecraft, and manoeuvring through space with ease. They were seen doing this all while speaking and reacting with an element of humour and a touch of robot charm. This kind of AI is more of an artificial general intelligence robot. A possible general AI is still a long way off. We currently deal with more task-specific AI. So, the question of robots endangering humans is a long shot.

Machine learning

Be it humans or machines, learning is the most essential phase in achieving anything. This process of teaching a machine to perform a certain activity is known as “machine learning.”

Supervised learning, unsupervised learning, and reinforcement learning are a few machine learning techniques.

Supervised learning is a process of learning with training labels provided by a supervisor. Training is done on a large set of data, and then the machine is tested on some more data to evaluate the progress made. The machine learns from mistakes; each time it makes an error, the input weights are updated using the update rule.

Unsupervised learning is learning without training labels provided by a supervisor; instead, it uses labels from the environment around it; this is also called clustering or grouping. In this technique, learning happens by clustering or grouping based on properties and attributes. These specific clusters are called K-clusters. An algorithm called K-means clustering is used to predict these clusters based on the averages of the data. After prediction, AI will have to learn to update its beliefs to match its observations of reality. Another way of unsupervised learning is representational learning, where two images are compared to see if they are similar or not.

Reinforcement learning, in simple terms, is learning by doing to achieve a difficult goal. This technique is helpful when we wish to teach AI to perform a particular task. Once the task is complete, we can inform AI that it succeeded and ask them to explain how it did it. Reinforcement learning makes use of a concept called credit assignment to navigate which actions helped to get the work done. Values are assigned to different states, and the best policy is selected for task completion in the most optimised way.

Spacecraft navigation

We spend billions of dollars every year learning more about the cosmos and our place in it. Space technology is one area where precision and accuracy are crucial. Although there is a long way to go for the construction of robots like TARS and CASE from Interstellar, robotic rovers like Perseverance and Curiosity are one of those that employ artificial intelligence and machine learning in many of their operations.

Ground-based tracking systems were mostly employed in the past for spacecraft navigation, and they had their share of problems. Data transmission between the spacecraft and the ground stations is slow due to the great distance involved. Precision and cost reduction are generally key factors in these endeavours, so the technology of ground-based radiometric tracking began to wane.

Autonomous spacecraft navigation and AI

The notion of the spacecraft having little to no communication with the ground stations became the new trend in the spacecraft navigation field to adjust the delay issues. This idea of the spacecraft autonomously navigating itself through space also required significant contributions from multiple engineering disciplines and computer science.

In 1998, NASA launched the Deep Space 1 (DS1) mission. This was a test flight that flew by an asteroid and a comet. It had autonomous navigation software called AutoNav. AutoNav allows a spacecraft to autonomously adjust its course by analysing images of its area of vision. Deep Space 1 laid the foundation for later missions to travel space more efficiently and autonomously. This ability of the computer to extract high-level understanding from images is the goal of computer vision (a type of artificial intelligence). 

Computer vision in space

Computer vision, a subfield of artificial intelligence, plays a crucial role in the navigation systems of robotic rovers exploring extraterrestrial environments. Techniques like convolutional neural networks (CNNs) and perspective-n-point (PnP) are employed to extract vital information from visual data captured by the rovers’ cameras.

CNNs are a class of deep neural networks specifically designed to process visual data. They are particularly adept at recognising patterns and features in images and videos. In the context of rover navigation, CNNs are used to identify and track landmarks, such as rocks, craters, and other geological formations. This information is then used to determine the rover’s position and orientation relative to its surroundings.

PnP is a computer vision technique used to estimate the position and orientation of a camera (or other sensor) relative to a known set of 3D points. In the case of rover navigation, the known 3D points are typically landmarks identified by the rover’s cameras. PnP algorithms use the 2D coordinates of these landmarks in the image and their known 3D coordinates to calculate the rover’s pose (position and orientation).

The combination of CNNs and PnP enables rovers to accurately navigate their surroundings and perform tasks such as autonomous exploration, sample collection, and hazard avoidance. These techniques are essential for enabling rovers to operate autonomously in complex and unpredictable extraterrestrial environments.

Computer vision is a rapidly evolving field, and new developments are constantly emerging. As computer vision techniques continue to advance, rovers will become even more capable and versatile, paving the way for even more ambitious space exploration missions in the future. There is a significant amount of research focused on developing intelligent robotic spacecraft that use computer vision to track and determine the motion of unknown targets, such as space debris or retired missions, solely based on vision. Artificial neural networks are the result of connecting many perceptrons. A perceptron is a programme that mimics one neuron. As the number of neurons grows in the input layer, these networks become more complicated and are hence used to solve more intricate problems.

Path planning and control

Path planning involves calculating the best trajectory to follow, from the current position to the destination, while ensuring collision-free navigation. A lot of research has also been done in path planning, which relies on techniques like artificial intelligence, vector fields, and deep reinforcement learning methods to determine the trajectory towards a target object, such as space debris. To execute the desired trajectory calculated through path planning, spacecraft need to activate their thrusters to follow this path. Advanced control theories are essential for generating and maintaining the desired motion.

AstroSLAM is a space-adapted version of SLAM software that helps in finding out the spacecraft’s relative position.

Along with spacecraft navigation, AI is extensively used in space exploration. AI can sift through mountains of data from telescopes and find patterns in them. The recent discovery of Kepler-90i is a work of AI. AI algorithms were trained to spot minute changes in the starlight that could indicate the presence of a planet passing by.

AI’s crossover applications

Artificial intelligence (AI), initially developed for space exploration, has transcended its extraterrestrial origins to permeate various domains back on Earth. Once confined to the realm of rocket science and satellite navigation, AI has charted a bold course into uncharted territories, revolutionising industries and redefining human experiences.

In the realm of healthcare, AI has emerged as a game-changer. From analysing medical images to identify diseases at an early stage to developing personalised treatment plans, AI is transforming the way healthcare is delivered. By leveraging deep learning algorithms, AI can process vast amounts of medical data, leading to more accurate diagnoses and more effective treatments.

Transportation has also witnessed a profound transformation due to AI. Self-driving cars, once a futuristic concept, are now a tangible reality, promising safer and more efficient travel. AI-powered traffic management systems optimise traffic flow, reducing congestion and minimising travel time. Additionally, AI is being harnessed to develop innovative solutions for public transportation, making it more accessible and convenient for commuters.

In the financial sector, AI is driving a wave of innovation. From automating repetitive tasks to detecting fraudulent transactions, AI is transforming banking operations. AI-powered chatbots provide instant customer service, offering personalised advice and assistance. Moreover, AI is revolutionising risk assessment and portfolio management, enabling financial institutions to make more informed decisions.

Retail and e-commerce have also been disrupted by AI. AI-driven product recommendations, personalised marketing campaigns, and virtual fitting rooms are enhancing the customer experience and increasing sales conversions. AI-powered supply chain management systems optimise inventory levels, reduce shipping times, and improve overall efficiency.

The entertainment industry has not been immune to the transformative power of AI. AI-generated content, such as music and art, is blurring the lines between human creativity and machine intelligence. AI-powered virtual reality experiences are taking entertainment to the next level, immersing users in captivating and immersive worlds.

AI’s impact on education is equally significant. AI-powered tutoring systems provide personalised learning experiences, adapting to each student’s needs and learning style. AI-driven language translation tools break down language barriers, making education accessible to learners worldwide.

In summary, AI’s crossover applications have transcended the boundaries of space exploration, touching virtually every aspect of our lives. From healthcare and transportation to finance, retail, entertainment, and education, AI is transforming industries, enhancing experiences, and unlocking new possibilities for the future.

Marine biology

AI algorithms designed for star matching in the Hubble space telescope are now being used to track endangered whales by recognising the unique patterns of spots on their skin. Biologists collaborated with NASA to adapt the AI algorithm originally developed for the Hubble space telescope to identify these patterns, which are as unique to whales as fingerprints are to humans. This technology has also been adapted to identify other sea creatures with unique spot patterns. This innovative technology has transformed the way we study whale sharks. Researchers can now catalogue sightings and track their movements across the oceans with great accuracy. All the statistics on the encounters and sightings of whale sharks using this technology are being documented in a database called the Sharkbook. This wealth of data provides invaluable insights into the behaviour and population trends of these majestic creatures.

Medical imaging

The benefits of AI reach far beyond space exploration and wildlife, touching many areas of our daily lives. This crossover from space technology to marine biology highlights the incredible versatility of AI. For instance, AI technology originally developed for space missions is now being used in other fields to great effect. Take medical imaging as an example. The same AI algorithms designed for image recognition in space telescopes are now helping doctors detect diseases like cancer at their earliest stages. These AI systems can analyse medical images with great precision, spotting variations that might escape even the most trained human eyes. This capability helps in earlier diagnosis and significantly improves treatment outcomes for patients.

Ethical considerations

As we keep tapping into the power of AI, we’re opening doors to tackle some of the biggest challenges humanity faces and explore new horizons both here on Earth and in space. Efforts should be made to create ethical guidelines and standards for AI development and use, with the help of technologists, policymakers, and ethicists, so that we can establish a framework that encourages the responsible use of AI and ensures that it has the best possible impact on society.

Conclusion

The future can be a collaboration of both artificial intelligence and actual intelligence working to make the world a better place. Integration of AI into space technology would increase the efficiency of these missions. AI’s influence on spacecraft navigation could be revolutionary in addressing issues like space debris, which is a major threat to orbiting satellites. With AI, autonomous navigation can be more sophisticated and capable, helping space agencies successfully lead various missions like interplanetary missions, flybys, robotic rovers, etc. Ultimately, this results in us understanding a bit more about ourselves and our place in the universe.

References

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