Challenges and applications of Artificial Intelligence (AI) in space

Artificial intelligence has revolutionized all sectors of activity, and its application in the space sector is no exception. This technology is already helping to improve many key aspects of space mission development.

The space sector is evolving rapidly, incorporating the latest technologies to connect people, explore the universe and contribute to the safety and care of our planet more efficiently and reliably.

Artificial intelligence faces unique and complex challenges in space. In this article, we will explore the main challenges to be overcome such as cosmic radiation, communication latency or energy consumption. Overcoming these obstacles is crucial to the success of future exploration or satellite deployment, allowing AI to play a vital role in advancing humanity in the cosmos.

What is Artificial Intelligence and what are the differences with deep learning and machine learning?

Artificial intelligence is a technology that seeks to create systems capable of performing tasks similar to those performed by human intelligence, such as speech recognition and decision making. Machine learning (ML) is a subset of AI that uses algorithms to learn from data and improve over time without explicit programming. Deep learning (DL), a specialization of ML, uses deep neural networks to process large volumes of data, being highly effective in complex tasks such as image recognition. In summary, AI is the general field, ML is a technique within AI, and DL is an advanced technique within ML.

Main challenges of AI in space missions

Artificial intelligence, deep learning and machine learning are gaining prominence in the space sector, however, the implementation of AI in space faces several challenges, among the main ones we highlight:

• Limited resources and access

Access to systems, satellites or space platforms once in orbit can be very costly and risky. All onboard systems must function perfectly during their lifetime.

• Latency requirements

Latency requirements in space are crucial due to the long distances to be connected. Communications with Earth, probes and rovers must be efficient, as even small delays can critically impact mission performance. Latency must be minimized to ensure fast and accurate responses, improving autonomy and real-time decision making.

• Data processing

Data processing in space is complex due to limited storage capacity and low on-board processing power. Data transmission to Earth is slow and expensive. These challenges require highly efficient systems and optimized algorithms to handle large volumes of information in real time.

• Temperature extremes and cosmic radiation

Extreme temperatures and cosmic radiation in space are critical challenges for any mission. Temperatures can vary drastically from –90º to +125º, affecting equipment functionality. Cosmic radiation damages electronic components and poses health risks to astronauts, requiring advanced protection and technological design solutions.

• Reliability of data through simulations

Data matching between different types of sensors is essential for consistency and accuracy in space missions. Introducing variabilities through simulations allows predicting and correcting possible discrepancies. These techniques improve data reliability, optimizing decision making and system performance in complex environments.

• Quality of the models

Bias detection is critical to ensure fair and accurate models in spatial applications. Identifying and mitigating biases in data and algorithms promotes diversity and fairness, improving the reliability of decisions and accurate representation of observed phenomena in space.

AI use cases in space and satellites

Artificial intelligence can have infinite applications in space, from its use in autonomous spacecraft navigation, scientific data analysis or predictive maintenance of equipment, to assistance in manned missions, optimization of manufacturing processes or image recognition. It can also facilitate the exploration of planets and asteroids, optimizing the collection of samples and the identification of valuable resources.

• Earth observation and image classification

Artificial intelligence image recognition technology from a satellite uses advanced algorithms to interpret visual data captured from space. This identifies geographic features, environmental changes and human activities, providing valuable information for resource management, security and urban planning. The AI chooses the images that are considered relevant and analyzes them before sending them back to Earth.

• Exoplanetary research

Exoplanetary research with artificial intelligence optimizes the detection and analysis of planets outside our solar system. Using advanced algorithms, AI analyzes large volumes of astronomical data, identifying patterns and signals that indicate the presence of exoplanets. This accelerates discoveries and improves our understanding of the universe.

• Deep Space Missions

Deep space exploration missions with artificial intelligence improve spacecraft autonomy and efficiency. AI enables real-time decision making, precise navigation and resource management, crucial in remote and unknown environments, extending our exploration capabilities beyond the solar system. A practical example could be the real-time choice of parameters to achieve a safe landing.

• Services in orbit

In-orbit services with artificial intelligence optimize the management of satellites and space stations. AI enables autonomous maintenance, real-time repair and improved operational efficiency. These advances extend equipment lifetime and improve space surveillance, benefiting telecommunications and earth observation.

Where is the future of AI in the space sector headed?

Energy efficiency is an essential factor in the continued advancement of AI in space. Some innovative technologies, such as neuromorphics, quantum and photonics, are being explored to overcome current limitations and improve the performance of AI systems in space.

• Training on board satellites

It is one of the main challenges and, so far, most applications of neural networks in space have been limited to inference. That is, receiving input data and providing results. However, real-time training requires higher capacity and memory, a hurdle at present due to power and heat dissipation limitations in satellites.

• Satellites and autonomous vehicles

Satellites and autonomous vehicles with artificial intelligence are transforming space exploration and monitoring. Equipped with AI, these systems can navigate, perform maintenance tasks and collect data without human intervention. This increases efficiency and accuracy in scientific, surveillance and communication missions, facilitating operations in extreme and remote environments. They could avoid collisions with space debris and respond to threats, improving mission safety and efficiency.

• Data fusion

Currently, there are no civilian satellites that integrate several types of sensors -such as SAR radar or optical sensors-, although there are already some initiatives and technologies that seek to deploy collaborative satellites that use and combine different types of sensors.

• Space debris management

This critical problem requires innovative solutions. The use of artificial intelligence can help combat space debris by accurately identifying and tracking debris. Advanced algorithms make it possible to predict collisions and plan evasive maneuvers. In addition, AI optimizes space cleanup missions, guiding autonomous vehicles to capture and remove debris, ensuring a safer and more sustainable space environment.

• Security and Defense

Satellites with advanced maneuvering capabilities, similar to drones in space, are already being developed to perform evasive and offensive maneuvers. Thus, this hybrid approach promises to improve security in space.

The implementation of AI in all types of space missions presents numerous challenges, but a multitude of advantages to solve complex problems today. In ARQUIMEA we contribute to transform the space sector through the application of artificial intelligence with different objectives improving the efficiency and autonomy of space missions.