AI Agents in Space Exploration

 

The vast amount of data generated by space probes and telescopes needs advanced AI tools for efficient analysis. Taskade Real-Time Sync Machine learning (ML) algorithms are able to sift through the information quickly and identify patterns, which accelerates the discovery of new celestial bodies and cosmic phenomena.

ML is also used in remote satellite health monitoring to detect issues and predict performance for informed decision-making. It is also being explored for use in swarm robotics, where fleets of small robots work together to accomplish tasks, like mapping planets and conducting experiments.
AI for Detecting Threats

Every satellite creates massive amounts of data, which needs to be processed for weather and climate research, city planning, asteroid tracking, and more. While crowdsourcing projects can help with some of this work, AI can provide a more efficient analysis, processing millions of images in seconds and spotting changes as they happen.

AI-based algorithms can also analyze star and galaxy data to help map the universe, speeding up processes that would take astronomers countless hours to complete. Similarly, AI can help manage space debris, allowing satellites to make the necessary adjustments on their own to avoid collisions with celestial objects.

In the case of a potential threat, such as an anti-satellite missile launched by an adversary, AI can rapidly assess the situation and recommend courses of action to minimize the impact. This includes analyzing trajectory information, calculating the impact of a collision, and recommending maneuvers to reduce the probability of damage.

Lockheed Martin is working on a system that will process this vast amount of imagery and data, providing human astronauts with the insights they need to safely explore other planets or protect themselves from threats. However, it’s important to remember that artificial intelligence is only as good as the data it’s trained on. AI systems are susceptible to bias, which can lead to errors or security breaches. Booz Allen is helping clients identify these risks and develop solutions to mitigate them.

During space missions, humans need to be able to rapidly assess their environment and react in a split second. This means determining what equipment to bring aboard, how to navigate in space, and how to deal with any emergencies that might arise. AI can help reduce the complexity of these tasks, enabling astronauts to operate more efficiently and potentially save lives in the process.

In addition, AI can help scientists analyze the results of their experiments. For example, a machine learning program created by NASA called Morpheus has helped them track and classify galaxies, making it possible to study the early structures of our universe in unprecedented detail. This is important for predicting how galaxies might change over time, and could help us understand what’s happening in our own universe as it expands.
AI for Detecting Space Debris

As the world becomes increasingly reliant on space technology, there is a growing risk of collisions between satellites and other debris in low Earth orbit. This trash poses a significant threat to current and future space missions and to ground-based infrastructure, such as power grids on Earth. Several startups are using AI to improve tracking of space junk, with some companies even developing algorithms that can distinguish between debris and active satellites.

The number of objects in orbit is steadily increasing, and the debris risk is expected to rise further as companies like SpaceX launch megaconstellations of thousands of satellites. Fortunately, it is possible to prevent some collisions between debris and satellites by continuously tracking their locations with radar and optical sensors on Earth and in space. However, the sheer number of objects is putting a strain on expert teams that must constantly issue conjunction alerts and determine how to respond.

Some startups are looking to use AI to predict and mitigate collisions in LEO, including one startup that uses a “genetic” algorithm to track the rotational motion of debris. Another, by utilizing radar and optical data from Earth and in space, is working to build up an independent high-accuracy LEO debris catalogue. These projects could help to inform the design of future space missions, ensuring they avoid or mitigate potential collisions with existing debris.

However, some of the more dangerous debris is less detectable because it moves so quickly and is not illuminated by Earth’s sunlight. This includes a large amount of man-made debris, such as defunct satellites and rocket parts. This type of debris is hard to track, and it presents a significant risk for manned and unmanned space missions.

One startup is working on a system that will capture and incinerate space debris with robotic lasers on board a spacecraft. The resulting energy can be used to power the spacecraft and accelerate it to remove debris from the orbit. This could be a more sustainable and effective way to address the issue than simply burning debris in the atmosphere, which is not environmentally friendly or safe for manned flights.
AI for Data Processing

Often, space missions need to process a lot of data. AI is able to help with this, as it can scan visuals for objects of interest and frame them automatically. This allows human operators to focus on the higher-value tasks and potentially save time. AI can also be used to improve the efficiency of satellite systems and networks, as it is able to spot patterns and minimise unnecessary data transmissions. For example, it can identify weather patterns from atypical ones, so it can minimize or eliminate unimportant data for transmission to end-users.

In addition, AI can make operations more efficient by reducing resource consumption. For instance, it can optimise the use of energy and other resources on board a spacecraft, so that they last longer. It can also detect any issues with the spacecraft and offer solutions, so that the crew is aware of potential problems and can take action.

Scientists are already using AI for a variety of tasks across different space exploration projects. For example, ESA’s Hera planetary defence mission will use AI as it steers itself through space towards an asteroid by fusing information from multiple sensors to make decisions autonomously. This reduces the amount of control needed from Earth and increases the quality of the data it can provide to scientists. AI is also being used on board rovers on Mars to increase the amount of useful data transmitted back to Earth. Intelligent data transmission software removes scheduling errors that can occur due to human error, meaning more data is usefully sent.

AI can also assist with the design and planning of space exploration missions. For example, NASA’s Jet Propulsion Laboratory developed an AI assistant called Daphne that can provide engineers with reliable answers about how to build Earth observation satellite systems based on their parameters and constraints. The system uses natural language processing and draws on a database of knowledge from past Earth-observing satellite missions and domain-specific expertise.

AI can also be used to monitor and predict performance of remote satellites, so that they can be repaired before they break down. Using this approach, it is possible to extend the lifespan of satellites and cut costs. This is a valuable contribution to the future of space exploration as it can allow humans to explore further into the universe than ever before.
AI for Autonomous Operations

The space environment is so complicated that even a minor mistake could have devastating consequences. That’s why AI is being used to assist humans during space missions and reduce risk by identifying faults, improving decision-making, and predicting complications.

One of the most challenging aspects of long-duration space travel is communication between Earth-based operations teams and astronauts in space. Traditionally, communications are conducted using radio waves that travel at the speed of light (670,000,000 mph) and take a great deal of time to transmit and receive. AI is helping to change this by enabling astronauts to communicate with each other in natural language and speech. This will help save significant amounts of time and improve mission efficiency.

In addition, AI is being used to automate some of the tasks that are currently done by human analysts. For example, an AI agent can be programmed to scan satellite images for specific targets. The algorithm then highlights those objects in the image, allowing analysts to focus their attention on higher-value activities.

AI is also being used to automate on-board data processing and transmission. For example, NASA’s Jet Propulsion Laboratory uses an AI system to recognize and interpret images from Mars rovers. These images are then transmitted back to Earth and processed by scientists for further analysis. In this way, AI is assisting in the collection and analysis of huge quantities of data that would be impossible to manage without automation.

A spacecraft’s navigation systems are constantly monitoring the surrounding environment to ensure it doesn’t collide with debris or other satellites. A collision can be catastrophic, but AI is able to reduce this risk by analyzing radar and optical data to identify potential threats and predict the likelihood of a collision. This allows operators to take evasive action or launch deorbit missions to remove and dispose of debris.

In a similar vein, AI can assist with the development and operation of robots that are capable of carrying out space tasks autonomously. For example, IBM, in partnership with DLR and Airbus, developed the CIMON (Crew Interactive Mobile Companion), an AI robot that can interact with astronauts aboard the International Space Station and answer questions.