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基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统设计
引用本文:左浩. 基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统设计[J]. 计算机测量与控制, 2023, 31(1): 106-112
作者姓名:左浩
作者单位:西安汽车职业大学
摘    要:变磁力吸附爬壁机器人是一种具有快速、灵活移动方式的爬行机器人,但其吸附力难以控制,越障稳定性较差,难以保证机器人的平稳爬行;为实现爬壁机器人在大型建筑结构外表面的自主避障,提升机器人与运动平面之间的吸附紧密性,设计基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统;按照PCB控制要求,连接外置SRAM设备与传感器模块,借助驱动I/O口电路提供的电力驱动作用,控制气动阀门的闭合情况,完成变磁力吸附爬壁机器人控制系统硬件结构设计;建立Netvlad神经网络体系,通过划分控制指令程序任务的方式,确定移植参数取值范围,实现对控制协议的移植处理,联合相关硬件应用结构,完成基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统设计;实验结果表明,在所设计系统作用下,障碍物所在位置与爬壁机器人所在位置之间的实测距离未大于30cm,能够有效实现自主避障,保证机器人与运动平面之间的紧密吸附。

关 键 词:Netvlad神经网络  变磁力吸附  爬壁机器人  PCB控制原则  传感器模块  神经损伤函数
收稿时间:2022-09-13
修稿时间:2022-10-20

Design of control system of variable magnetic adsorption wall-climbing robot based on Netvlad neural network
Abstract:Variable magnetic force adsorption wall climbing robot is a kind of crawling robot with fast and flexible movement mode. However, its adsorption force is difficult to control, and its obstacle climbing stability is poor, which makes it difficult to ensure the smooth crawling of the robot. In order to realize the autonomous obstacle avoidance of the wall climbing robot on the outer surface of large building structures and improve the adsorption tightness between the robot and the moving plane, a variable magnetic force adsorption wall climbing robot control system based on Netvlad neural network is designed. According to the PCB control requirements, the external SRAM device and the sensor module are connected, and the closing of the pneumatic valve is controlled by the electric drive provided by the drive I / O port circuit. The hardware structure design of the variable magnetic force adsorption wall climbing robot control system is completed. Set up the Netvlad neural network system, determine the range of porting parameters by dividing the tasks of the control instruction program, realize porting of the control protocol, and complete the control system design of the variable magnetic force adsorption wall climbing robot based on the Netvlad neural network in combination with the relevant hardware application structure. The experimental results show that under the action of the designed system, the measured distance between the position of the obstacle and the position of the wall climbing robot is not more than 30cm, which can effectively achieve autonomous obstacle avoidance and ensure the close adsorption between the robot and the moving plane.
Keywords:Netvlad neural network   Variable magnetic adsorption   Wall-climbing robot   PCB control principles   Sensor module   Nerve damage function  
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