摘要
针对现有掘进机航向角测量方法存在测量误差较大的问题,提出了一种基于卡尔曼滤波的航向角测量算法。首先利用速度传感器建立掘进机的运动模型,作为掘进机航向角的预测模型;然后利用激光发射仪建立掘进机航向角的观测模型;最后采用卡尔曼滤波算法对预测值和观测值进行融合,有效减小了航向角的测量误差,实现了掘进机长时间的精确定位。测试结果验证了该算法的有效性。
In view of problem of big measurement error existed in current measurement method of heading angle of roadheader,a measurement algorithm of heading angle of roadheader based on Kalman filter was proposed.Firstly,the speed sensor was used to establish movement model of roadheader,and the model was taken as prediction model of heading angle of the roadheader;Then,laser emission instrument was used to establish observation model of heading angle of the roadheader;Finally,the Kalman filter algorithm was used to fuse predicted and observed values.The algorithm effectively eliminates the measurement error of heading angle,and realizes precise localization of the roadheader.The effectiveness of the proposed algorithm is verified by test.
出处
《工矿自动化》
北大核心
2014年第11期100-103,共4页
Journal Of Mine Automation
关键词
掘进机
航向角
卡尔曼滤波
预测模型
观测模型
融合
roadheader
heading angle
Kalman filter
prediction model
observation model
fusion
