Volume 39, Number 1, February 2021
|Page(s)||77 - 84|
|Published online||09 April 2021|
An unsupervised learning neural network for planning UAV full-area reconnaissance path
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China
2 CETC Key Laboratory of Data Link Technology, Xi'an 710068, China
3 AVIC Xi'an Aeronautics Computing Technique Research Institute, Xi'an 710068, China
To plan a UAV's full-area reconnaissance path under uncertain information conditions, an unsupervised learning neural network based on the genetic algorithm is proposed. Firstly, the environment model, the UAV model and evaluation indexes are presented, and the neural network model for planning the UAV's full-area reconnaissance path is established. Because it is difficult to obtain the training samples for planning the UAV's full-area reconnaissance path, the genetic algorithm is used to optimize the unsupervised learning neural network parameters. Compared with the traditional methods, the evaluation indexes constructed in this paper do not need to specify UAV maneuver rules. The offline learning method proposed in the paper has excellent transfer performances. The simulation results show that the UAV based on the unsupervised learning neural network can plan effective full-area reconnaissance paths in the unknown environments and complete full-area reconnaissance missions.
Key words: unmanned aerial vehicle (UAV) / full-area reconnaissance / neural network / genetic algorithm / unsupervised learning
关键字 : 无人机 / 全区域侦察 / 神经网络 / 遗传算法 / 无监督学习
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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