@inproceedings{2170b8624cc64354af4c7fc3a1b43544,
title = "Research on Autonomous Landing Method of Flying Vehicle Based on Deep Learning",
abstract = "In recent years, vision technology is more and more widely used in aviation. The purpose of this paper is to realize the autonomous landing of the aircraft, and complete a series of processes from algorithm design to verification effect on the X-Plane simulation flight platform, so as to do some forward-looking work for the application and verification of AI algorithm in the field of aircraft landing. In the existing work, algorithm selection and simulation verification have been realized. In this paper, the VGG16 convolutional neural network is built firstly. The input of the network is the preprocessed pictures collected during landing, and the output is the four manipulations of the joystick in longitudinal, transverse, heading and throttle. After the analysis of training results, the algorithm is verified in X-Plane environment. The experimental results show that the method is verified and demonstrated in the X-Plane environment.",
keywords = "Autonomous landing, Convolutional neural network, Image preprocessing, X-Plane",
author = "Zhang Xiulin and Zhen Chong and Quxiao Lei and Wang Yifeng and Li Yuangan and Zhang Jingcheng and Li Ke",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",
year = "2023",
doi = "10.1007/978-981-19-6613-2\_378",
language = "英语",
isbn = "9789811966125",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3882--3890",
editor = "Liang Yan and Haibin Duan and Yimin Deng and Liang Yan",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control",
address = "德国",
}