How Artificial Intelligence Is Helping With Space
Introduction Artificial intelligence (AI) is increasingly vital in space operations and exploration, from evaluating the landscape on Mars to boosting.
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Key takeaways
- Introduction Artificial intelligence (AI) is increasingly vital in space operations and exploration, from evaluating the landscape on Mars to boosting communications between satellites and ground stations.
- In time-sensitive scenarios, an AI/machine learning system could offer solutions in minutes, rather than the hours or days required by more traditional methods.
- To realize AI’s immense promise, we must also ensure that AI systems are built and maintained safely, and that commanders and operators have the necessary training and expertise to trust this disruptive technology.
Introduction
Artificial intelligence (AI) is increasingly vital in space operations and exploration, from evaluating the landscape on Mars to boosting communications between satellites and ground stations. It has a capacity that has a plethora of uses and holds enormous potential for the data-dense and complex environment of space.
Despite broad unfamiliarity, artificial intelligence is a technology that is revolutionizing every aspect of life. It is a versatile tool that helps people to reconsider how we combine information, evaluate data, and use the ensuing insights for better decision making. Our goal with this thorough overview is to educate policymakers, opinion leaders, and interested observers about artificial intelligence and to highlight how it is already affecting the world and raising critical questions about society, economy, and governance.
The huge amount of data presently being given in real-time by Earth observation assets (e.g., the Copernicus sentinel network) and astronomical missions (e.g., the Kepler Space Telescope) provides intriguing new datasets and necessitates the use of novel machine learning techniques. Indeed, remote sensing is driving deep learning innovation for large-scale image processing tasks, whereas AI is being applied in a wide range of astrophysical applications, including planet discovery, galaxy classification, and cosmological modelling.
AI usage is also aiding less theoretical applications in the space sciences. Machine learning techniques are being used by mission planners to optimize spacecraft trajectories, conserving fuel and increasing mission lifespan. Closer to home, scientists are increasingly finding space junk and possibly hazardous near-Earth objects. Solar physicists are using artificial intelligence to anticipate the magnitude and direction of solar flares, which may cause billions of dollars in damage to satellite constellations, power grids, and telecommunication networks throughout the world if they go undetected.
Today’s space environment is congested, complicated, and contested—no longer a safe haven for US or partner space assets. AI has the ability to dramatically improve domain awareness and command and control decision-making, as well as to boost the resilience of satellites and their networks.
To realize the full potential of these prospective improvements, however, we must strengthen the security and trust in AI technology. Consider the assessments made by AI that assist humans in making decisions. Can commanders and operators have confidence that the algorithms that underpin these analyses were developed objectively, using suitable data and without bias? Can they be certain that the data they are utilizing has not been tampered with or modified by adversaries? These are key questions to address to ensure safety and mission-critical assets are not jeopardized.
Let’s discuss the potential applications of AI in the space sector one by one.
Improving The Space Domain Awareness #
Space is becoming increasingly congested. Over 2,600 active satellites, over 34,000 objects 10 centimeters or larger, and over 900,000 pieces of space junk between 1 and 10 cm orbit the Earth today. All are orbiting in distinct orbits, traversing distinct planes, and travelling at varying speeds. Developing a comprehensive image of this complicated environment is a critical first step toward securely working in space and safeguarding space assets.
To begin, organizations can compile a complete catalogue of known and observed Earth-orbiting objects using accessible data and an AI system. This same artificial intelligence system may continuously monitor and assess the likelihood of collisions, warning satellite and spacecraft operators in the event of an increased risk.
This is how such a scenario could unfold. Once operators identify a vulnerable satellite using their “space catalogue,” AI can assist them in determining the best course of action for securing that satellite. This AI has the potential to mix traditional modelling and simulation with a deep learning network and collision avoidance algorithms to rapidly generate a list of possible moves to avoid the space object.
As is the case on Earth, each possible avoidance technique has a number of advantages, disadvantages, and associated consequences. For instance, one course of action may result in a reduction in fuel costs while minimizing operational impact. Another possibility is that operators will be able to “see ahead” in order to avoid downstream interference or collisions.
Organizations can program an artificial intelligence/machine learning system to propose the most optimal avoidance techniques depending on the mission’s most pertinent criteria. Users—the “people in the loop”—can then utilize their judgement and mission knowledge to select the most appropriate maneuver to keep important space assets safe.
In time-sensitive scenarios, an AI/machine learning system could offer solutions in minutes, rather than the hours or days required by more traditional methods. This is the ability of AI to accelerate domain awareness — and thereby avoid costly collisions — in today’s more congested space environment.
Accelerate Command and Control in Decision Making #
Another area in which AI has significant potential is command-and-control decision-making, particularly when assets are threatened with little time to react.
Consider a scenario in which an operator must defend space assets from an anti-satellite (ASAT) attack launched from direct ascent. The operator may have only minutes to decide what to do in such a case. AI and data analytic have enabled decision-makers to accomplish a previously near-impossible task: efficiently analyzing massive volumes of data and rapidly arriving at a set of probable actions.
The AI system analyses ASAT trajectory data in order to determine potential targets. It then rapidly develops a variety of alternative courses of action, which may involve navigating, employing countermeasures, or engaging in offensive or defensive operations. The system sifts through several different courses of action using machine learning, taking into account connected effects and downstream repercussions. Operators and commanders are then presented with a timely menu of optimum options, accelerating command-and-control decision-making and strengthening space defense in mission-critical scenarios.
Strengthening Resilience Through Machine Learning and Automation #
Satellite constellations and the networks that connect them are growing in size and complexity in response to business demands for worldwide communication and data transportation. Additionally, these networks are growing more susceptible to sophisticated kinetic and non-kinetic threats.
Operators can reduce these dangers and strengthen the resilience of space networks and constellations by integrating AI into their systems. Organizations can employ artificial intelligence to rapidly examine data for network vulnerabilities. They can then use AI algorithms to “heal” or “self-adapt” in response, ensuring that all network nodes are reconnected. Additionally, organizations can incorporate self-learning algorithms into satellites to make them more self-sufficient and durable in the event that up-link and down-link contacts with ground operations are lost.
Additionally, artificial intelligence (AI) can automate the monitoring of a satellite’s “health status,” the resolution of anomalies, and the execution of defensive operations against threats. By automating these processes on satellites, operators may focus on more difficult, mission-critical tasks.
Building Trust Through Algorithm Development and Operator Training #
As with any new technological use, whether in space or elsewhere, security and trust are critical to acceptance and effectiveness. The development of AI algorithms is the first step toward AI security. Organizations must assure the provenance of the data used to train the algorithms, that algorithms are built with as little bias as possible, and that security is maintained throughout the software development and data storage processes.
Furthermore, enterprises owning space assets and systems will need to train operators in AI and machine learning, which involves knowing how AI systems are constructed and designed. Operators must also be fully aware of the capabilities and limitations of their AI-powered systems. Only via extensive training and education, as well as the implementation of safe processes, can operators and decision-makers have enough confidence in AI systems to employ them to better their missions.
Conclusion #
Deterring and defending our space assets has become both a requirement for national security and a significantly more difficult undertaking as the space environment rapidly evolves and proliferates with new users, new capabilities, and increasingly sophisticated threats. AI solutions offer a transformative opportunity for protecting, improving, and enhancing space missions and assisting the United States in maintaining space dominance by improving space domain awareness, accelerating command-and-control decisions, making satellites and their networks more resilient, and more. To realize AI’s immense promise, we must also ensure that AI systems are built and maintained safely, and that commanders and operators have the necessary training and expertise to trust this disruptive technology.
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