An unsupervised learning neural network for planning UAV full-area reconnaissance path
| Autor: | Bo Li, Zhi-peng Yang, Zhuoran Jia, Hao Ma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
full-area reconnaissance
Artificial neural network neural network Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Engineering TL1-4050 ComputerApplications_COMPUTERSINOTHERSYSTEMS Plan (drawing) unsupervised learning Machine learning computer.software_genre Path (graph theory) Offline learning Genetic algorithm genetic algorithm Unsupervised learning Artificial intelligence unmanned aerial vehicle (uav) business computer Motor vehicles. Aeronautics. Astronautics |
| Zdroj: | Xibei Gongye Daxue Xuebao, Vol 39, Iss 1, Pp 77-84 (2021) |
| ISSN: | 2609-7125 1000-2758 |
| DOI: | 10.1051/jnwpu/20213910077 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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