The current article focuses on GNC algorithms, AI algorithms, and the interaction among AI and GNC algorithms. Unsupervised learning, or reward-based learning algorithms could also be used also for GNC purposes. Good and up-to-date understanding of the current state-of-the art of GNC algorithms enable us to build better survey of using AI algorithms in spacecraft GNC. So, reviewing state-of-the-art GNC algorithms is also mandatory. GNC algorithms could be used to provide AI algorithms with the required data sets of training. This is because using AI algorithms may require the designer to train his algorithms using training examples (data sets) via a supervised learning (SL) algorithms. Developing AI algorithms for GNC purposes with enhanced and robust performance depends heavily on the models used for GNC algorithms. This article reviews state-of-the-art of the problem of using Artificial Intelligence (AI) for spacecraft Guidance, Navigation, and Control (GNC).
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