The vast expanse of space remains uncharted, and the immense distances involved in space travel engender multiple challenges including life-threatening extreme temperatures, ultra-vacuum, atomic oxygen and high energy radiation, making it impractical for humans alone to exploit these unprecedented opportunities. Thus, AI can be used to effectively address these challenges assisting mankind to expand their space knowledge by venturing further into our galaxy.
The first ever case of AI being used in space exploration is the Deep Space 1 probe, a technology demonstrator conducting the comet Borrelly and the asteroid 9969 Braille in 1998. The algorithm used during the mission was called Remote Agent and diagnosed failures on board. Since then, AI has successfully been used in space rover system softwares and other space infrastructure to enhance their performance during missions in outer space.
Human-Space Interfaces that are Marking Space Renaissance
Earth-for-Space economy assists Earth-based infrastructure to observe or reach space
The scope of this economy extends far more than telescopes and launch pads. AI enables astronomical discoveries as it rapidly processes data with precision. Currently, ML models are being trained for planet-hunting, determination of habitability of exoplanets, advanced source classification of celestial objects, changing the Search for Extraterrestrial Intelligence (SETI) landscape, optimization of safety and cost in the design of components of rockets and space vehicles, complex task scheduling, development of new technologies and life support systems, accelerate innovations in spacecraft materials, and testing space-suitable food production systems.
Space–for–Earth economy exploits space-based infrastructure to improve life on Earth
Technological development has expanded the scope from Geosynchronous Equatorial Orbit (GEO) and Medium-Earth Orbit (MEO) to Low-Earth Orbit (LEO) and beyond. AI is assisting the development of telecommunication, earth observation, and planetary defense. ML techniques have multiple applications in this field, such as beam-hopping, anti-jamming, energy management, improving network control, security, and health of satellite systems, improvisation in Global Navigation Satellite Systems (GNSS), enhancing satellite technology for weather monitoring and Earth observation, processing data produced by observational satellites, reliable estimations of solar radiation and heat storage in urban areas, wind speed estimation, autonomous trajectory optimization of Distributed Space Systems (DSS) and collision avoidance maneuvers. AI is also being deployed to aid planetary defense efforts in finding, tracking, and reacting to Near-Earth Objects (NEOs), such as asteroids and comets.
Space-for-Space economy encompasses activities in Earth’s orbit and self-sustaining human presence in space
AI can be employed to evaluate operational risk and prioritize critical tasks to ensure the safety of space flights and successful space mission execution, development of intelligent navigation systems for exploratory missions to other celestial bodies, maintenance of life support system necessary to establish human settlements in space, detection. Neural Language Processing (NLP) and sentiment analysis has been used to develop virtual assistants that diagnose and resolve anomalies in critical spacecraft atmosphere and water recovery systems, monitor and preserve signs of cryogenic sleep for longer space missions, anticipate and support the mental and emotional needs of the human crew, assist astronauts with in-space tasks. Similarly, robots can be programmed to develop cybernetically enhanced humans or humanoids.
Use Cases of AI in Space Exploration in India
Chandrayaan 2: AI-powered ‘Pragyan’ Rover
On 22nd July 2019, ISRO launched Chandrayaan 2 spacecraft into the Earth’s orbit as part of its second lunar mission. The spacecraft was powered by AI and was capable of communicating only with the lander. However, it included a piece of motion technology that used AI. The technology helped the rover to maneuver on the surface of the moon to aid in landing. The algorithm was designed to assist the rover to trace water and other minerals on the lunar surface and share pictures for research and examination.
Multi-Object Tracking Radar (SDSC-SHAR)
ISRO used the capabilities of AI to build a space object tracking solution that will be proficient in the successful sustenance of satellites through the difficult terrain of open space with millions of unknown objects. ISRO first developed target identification using machine learning algorithms from MOTR radar data. It then moved on to the development of real-time JPDA & MHT-based data association in dense multi-target tracking environments.
Image Processing and Pattern Recognition (IIRS)
ISRO leveraged an algorithm called backpropagation belonging to the class named Artificial Neural networks (ANN) of machine learning algorithms. It also explored different deep learning algorithms for various applications of earth observation data such as self-learning-based classification, prediction, multi-sensor temporal data in crop/forest species identification, and remote sensing time series data analysis.
Autonomously Navigating Robot for Space Mission
ISRO leveraged artificial intelligence enabled Path Navigation algorithms to build and send unmanned robots to help fetch critical space information. It was developed by the ISRO Inertial Systems Unit (IISU) which is responsible for the design and development of inertial systems for launch vehicles and spacecraft programs of ISRO.
Use Cases of AI in Space Exploration around the world
Development of Advanced Learning Algorithms by NASA
NASA has been collaborating with tech giants like Google, Intel, and IBM to develop advanced learning algorithms for their projects. They are primarily using AI and ML to facilitate auto-corrections, track complex structures inside solar winds, and facilitate the replacement of multiple filters fitted on payloads for observations which considerably reduces their mission’s cost.
Curiosity Rover Developed by NASA
In 2016, NASA sent an autonomous system to Mars named Curiosity rover, to explore the Gail crater. It used an AI arrangement called AEGIS (Autonomous Exploration for Gathering Increased Science) system. By helping to zap dozens of laser targets on the Red Planet, it inherently changed how Mars is studied. AEGIS was also used as part of the Mars 2020 mission for autonomous target selection, identifying geological targets in images from the rover’s navigation camera and choosing targets for itself without the permission of Earth. This led to a time reduction during the mission as both parties, the robot and Earth, did not have to wait for mutual action.
Development of Twin Earth by ESA
The European Space Agency (ESA) is developing an AI-powered digital twin of Earth to better monitor, predict, and respond to human and natural events, including challenges such as earthquakes and biodiversity loss. This technology promises to enhance socioeconomic conditions, such as monitoring the dynamics of urban sprawl, helping farmers to optimize productivity. Additionally, it also aims to address environmental issues, such as deforestation and desertification. Security and peace related applications include targeted humanitarian assistance, counter human trafficking capabilities, and war crime detection.
Encapsulating, AI and ML are rigorously being tested and utilized for the purpose of space exploration. This is attributed to the fact that AI techniques increase the levels of autonomy and automation and allow for a wider variety of space missions while being critical to their success. AI-assisted space exploration systems offer enhanced communication with ground operators to provide frequent human monitoring which potentially avoid hazardous situations. This also explains the multifold applications for AI during Space exploration including designing and planning of mission, data collection and analysis, autonomous navigation and mitigation of risks associated with human health during space flight to list a few. Thus, it is safe to say that AI has a vast future to explore in outer space.