基于全连接神经网络的车辆短预瞄电磁导引方案

Electromagnetic guidance scheme for limited-preview vehicles based on fully connected neural network

电磁导引是一种车辆自动导引方案,广泛应用于工业、物流等领域。为解决现有电磁导引方案对车辆机械结构要求较高、易受传感器预瞄距离短的限制、难以应用于小型自动导引车辆的问题,提出了一种基于全连接神经网络的导引方案。通过数据分析寻找有限预瞄距离内的最优传感器排布方案,设计和训练全连接神经网络模型,对车身姿态及车后道路的信息进行全面预测,以弥补传感器短预瞄所造成的前向道路探测能力的不足。经模拟和实际测试,该方案能极大改善较小体积车辆的短预瞄电磁导引系统的控制效果,实现车辆的稳定快速运行。

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.