中圖分類號(hào)： TN609,；TP242.6；TP249 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211665 中文引用格式： 楊豫龍,，趙娟,，黃原. 基于全連接神經(jīng)網(wǎng)絡(luò)的車輛短預(yù)瞄電磁導(dǎo)引方案[J].電子技術(shù)應(yīng)用，2022,，48(3)：22-26. 英文引用格式： Yang Yulong,，Zhao Juan，Huang Yuan. Electromagnetic guidance scheme for limited-preview vehicles based on fully connected neural network[J]. Application of Electronic Technique,，2022,，48(3)：22-26.
Electromagnetic guidance scheme for limited-preview vehicles based on fully connected neural network
Yang Yulong1，Zhao Juan1,，2,，Huang Yuan1
1.School of Automation，China University of Geosciences，Wuhan 430074,，China,； 2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems，Wuhan 430074,，China
Abstract： As one of the autopilot schemes of automatic guided vehicle(AGV), electromagnetic guidance is widely used in industry, logistics and other fields. Traditional electromagnetic guidance schemes have high requirements on mechanical structure and are easily limited by the small preview range of sensors. Thus, it is difficult to apply them to small AGV. In order to remedy the defect of limited detection ability, which is caused by limited preview, a fully connected neural network model is designed and trained to detect both vehicle′s posture and rear road information. Both simulation and actual tests show that the presented scheme greatly improves the control effect of electromagnetic guidance system with small size and limited-preview sensors. In the whole process, the vehicle runs rapidly and steadily.
Key words : neural network,；supervised learning；limited preview,；electromagnetic guiding,；AGV
