Research on positioning algorithm of electric mine shovel based on UWB technology
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摘要: 矿山工作环境恶劣,传统的电缆卷放方法无法保证电铲长时间供电,且供电过程中存在安全隐患。跟随电铲移动的新型电缆卷放车可解决上述问题。为了实现电缆卷放车对电铲的自主跟随,保证矿山环境下电铲的长时间供电,提出了一种基于超宽带(UWB)技术的矿山电铲定位算法,并选用到达时间差(TDOA)算法构建矿山电铲定位模型。根据TDOA测距算法,测量各基站到目标电铲位置的距离并计算差值;对获取的距离差信息进行滑动平均滤波,以抑制测距过程中产生的噪声,平稳数据;根据滤波修正后的距离差计算出标签位置;用强跟踪扩展卡尔曼滤波(STFEKF)算法跟踪目标电铲位置,进一步消除噪声,提高运动过程中目标电铲的定位精度。仿真结果表明,在不同观测噪声的影响下,滑动平均滤波+STFEKF的定位方案误差小于传统EKF算法,有效解决了距离增加或电铲运动状态突变时定位误差增大的问题;定位均方差较传统EKF算法降低70%以上,定位轨迹更接近于目标的真实移动轨迹,具有良好的定位跟踪及噪声抑制能力。Abstract: In the harsh working environment of mines, the traditional cable reeling method cannot guarantee the power supply of electric shovel for a long time, and there are hidden safety hazards in the process of power supply. A new cable reel car which follows the shovel is proposed to address the above problems. In order to realize the autonomous following of electric shovel by cable reel car and ensure the long time power supply of electric shovel in mining environment, the positioning algorithm of electric mine shovel based on ultra wide-band (UWB) technology is proposed and time difference of arrival (TDOA) algorithm is used to construct the positioning model of electric mine shovel. Based on the TDOA ranging algorithm, the distance from each base station to the target electric shovel position is measured and the difference is calculated. The distance difference information obtained is moving average filtered to suppress the noise generated in the ranging process and achieve smooth data. The tag position is calculated according to the distance difference after filtering correction. The target electric shovel position is tracked with the strong tracking extended Kalman filter (STFEKF) algorithm to further eliminate noise and improve the positioning accuracy of the target electric shovel during movement. The simulation results show that under the influence of different observation noises, the error of the moving filter + STFEKF positioning method is smaller than that of the traditional EKF algorithm. This method effectively solves the problem of positioning error increasing with the distance increasing or the sudden change of shovel movement. The positioning mean square deviation is reduced by more than 70% compared with the traditional EKF algorithm, and the positioning trajectory is closer to the real movement trajectory of the target with good performance of positioning tracking and noise suppression.
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