Stefano Silvestrini
Several areas of technology and robotics research are being influenced by an increasing interest in AI. The space community has only recently begun to investigate artificial neural networks and deep learning techniques for space systems. The most important aspects of these topics for controlling, guiding, and navigating spacecraft dynamics will be discussed in this paper. In an effort to draw attention to the benefits and drawbacks of employing the most prevalent architectures of artificial neural networks and the training strategies that go along with them, we examine these components. Quantitative and qualitative metrics are used to compare and review particular system identification, control synthesis, and optical navigation applications of artificial neural networks. The end-to-end deep learning frameworks for spacecraft guidance, navigation, and control are presented in this overview, as are the hybrid approaches that combine neural techniques with conventional algorithms to boost their performance.
இந்தக் கட்டுரையைப் பகிரவும்
English 
Spanish 
Chinese 
Russian 
German 
French 
Japanese 
Portuguese 
Hindi 