基于捷联惯导和推移油缸信息融合的智能掘进机器人位姿测量方法

Pose measurement of intelligent roadheader based on strapdown inertial navigation and oil cylinder movement

针对煤矿智能掘进机器人系统位姿实时精确测量问题,基于两级护盾结构相互推移油缸实现机器人行驶原理,提出了一种基于捷联惯导和推移油缸信息融合的智能掘进机器人位姿测量方法。利用捷联惯导实时测量智能掘进机器人的姿态信息,再结合推移油缸位移数据,进行航位推算。采用标准卡尔曼滤波算法将捷联惯导数据和油缸数据进行融合,抑制惯导测量误差,提高位姿测量精度。井下实验结果表明:在智能掘进机器人系统静止1h左右,惯导和油缸组合测量的俯仰角、横滚角和航向角的漂移量分别为0.018596°、0.004635°和0.011898°;在智能掘进机器人前进21m(截距为1m)的过程中,组合测量系统解算得到的位置误差在X轴和Y轴方向分别小于3.5cm和2cm。

In order to accurately measure the roadheader’s position and attitude in real-time at work, a combined measurement method based on strapdown inertial navigation and cylinder displacement is proposed. The method uses strapdown inertial navigation to measure the attitude information of the roadheader in real time, and combines the displacement data of the cylinder moving the roadheader forward with the attitude information of the roadheader to calculate dead-reckoning, and fuses the strapdown inertial navigation data and the cylinder data using standard Kalman filter algorithm, to suppress the divergence of inertial navigation measurement results and improve the accuracy of pose measurement. The results of underground experiment show that the pitch angle, roll angle and heading angle measured the method are 0.018596, 0.004635°, and 0.011898° when the intelligent tunneling robot is stationary for 1 hour;in the process, the position error calculated by the combined measurement system is less than 3.5 cm and 2 cm in the x-axis and y-axis directions, respectively, indicating that problem of cumulative error using inertial navigation solution alone is solved.