The term ‘artificial intelligence (AI)’ comprises all techniques that enable computers to mimic intelligence. For example, computers that analyse data or the systems embedded in an autonomous vehicle. Usually, artificially intelligent systems are taught by humans . Which is a process that involves writing an awful lot of complex computer code.
But artificial intelligence can also be achieved through machine learning (ML). Which teaches machines to learn for themselves. ML is a way of ‘training’ a relatively simple algorithm to become more complex. Huge amounts of data are fed into the algorithm, which adjusts and improves itself over time. In ML, machines process information in a similar way to humans by developing artificial neural networks. This type of artificial intelligence has taken major leaps forward since the dawn of the internet. Deep learning (DL) is a specialised technique within ML. Whereby the machine utilises multi-layered artificial neural networks to train itself on complex tasks like image recognition.
Pritish Kumar give illustration about use of artificial intelligence in space research:
AI/ML in space
The most successful AI implementations based on DL are rarely used in the space industry today. As the (statistical) models developed within the neural network. They are not human readable and have been impossible to replicate thus far.
ML systems are also commonly used in space applications to approximate complex representations of the real world. For instance, when analysing massive amounts of Earth observation data or telemetry data from spacecraft.
Potential applications of AI are also being thoroughly investigated in satellite operations. Specially to support the operation of large satellite constellations, including relative positioning, communication and end-of-life management.
In addition, it is becoming more common to find ML systems analysing the huge amount of data that comes from each space mission. The data from some Mars rovers is being transmitted using AI. These rovers have even been taught how to navigate by themselves.
Its development has come a long way over the last couple of decades. But the complicated models and structures necessary for ML will need to be improved before it can be extensively useful. AI also currently lacks the reliability and adaptability required in new software. These qualities will need to be improved before it takes over the space industry.
Discovery & Preparation activities
Currently spacecraft need to communicate with Earth to do their job. But developing autonomous spacecraft that use artificial intelligence to take care of themselves would be very useful. Especially for exploring new parts of the Solar System and reducing mission costs. An older study on autonomy requirements for future spacecraft constellations identified the necessary technology to improve automation. Which also including autonomous navigation, automated telemetry analysis and software upgradability.
A more recent study focused on the management of complex constellations for which novel automated procedures. This procedures are being studied to reduce the active workload of ground operators. Automation of both the ground and space segments will reduce the need for human intervention . It especially for large constellations, automated collision avoidance maneuvers could be a real help.
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