摘要
针对传统拉线式位移传感器会因碰撞、恶劣天气等原因造成测量可靠性差、精度低等问题,提出一种通过神经网络建立油缸位移长度与标识点像素坐标间映射关系的挖掘机工作装置虚拟位移传感器系统。利用图像处理技术提取液压缸标识点的圆心像素坐标,以像素坐标和实际油缸位移信号作为输入,通过遗传算法优化的神经网络建立油缸位移与标识点圆心坐标的映射关系,预测油缸位移进而获得挖掘机工作装置的姿态。实验表明,该方法预测得到的油缸位移准确率高达99.5%,预测获得的工作装置姿态均方误差为1.132 9,满足实际应用要求,可以应用于挖掘机位姿的实际测量中。
In view of the problems of poor measurement reliability and low accuracy caused by traditional pull-wire displacement sensors due to collisions, bad weather, etc., a virtual displacement sensor for excavator working devices that establishes the mapping relationship between the cylinder displacement length and the pixel coordinates of the marking point through a neural network is proposed system. Using image processing technology to extract the center pixel coordinates of the hydraulic cylinder marking point, taking the pixel coordinates and the actual cylinder displacement signal as input, and establishing the mapping relationship between the cylinder displacement and marking point center coordinates through the neural network optimized by genetic algorithm, predicting the cylinder displacement and obtaining mining The attitude of the machine working device. Experiments show that the accuracy of the cylinder displacement predicted by this method is as high as 99.5%, and the predicted mean square error of the working device′s attitude is 1.132 9, which meets the requirements of practical applications and can be used in the actual measurement of the excavator′s attitude.
作者
倪佳敏
马伟
童欣
谢文昕
冯浩
殷晨波
Ni Jiamin;Ma Wei;Tong Xin;Xie Wenxin;Feng Hao;Yin Chenbo(Institute of Vehicles and Construction Machinery,Nanjing Tech University,Nanjing 211816,China;Joint Institute of Excavator Key Technology,Nanjing Tech University-Sany Heavy Machinery,Suzhou 215300,China)
出处
《电子测量技术》
北大核心
2022年第9期44-49,共6页
Electronic Measurement Technology
基金
国家青年科学基金(52105064)项目资助。
关键词
挖掘机
虚拟位移传感器
标识点坐标
神经网络
油缸位移
excavator
virtual displacement sensor
marking point coordinates
neural network
cylinder displacement
