SINS/OD integrated navigation system for shearer based on robust closed-loop path calibration
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摘要:
基于非完整性约束和闭合路径校准的捷联惯导与里程计的组合导航系统是被广泛应用的采煤机定位方案，其中闭合路径校准法需要能够准确测量液压支架的实际推移距离，但传统的卡尔曼滤波器（KF）难以应对观测量中的异常值，而错误的预测位置会严重影响闭合路径校准法的精度，导致难以有效检测采煤机轨迹直线度。最大相关熵准则卡尔曼滤波器（MCCKF）可获得测量值的高阶统计量，但是MCCKF中的核带宽通常根据经验设定，影响了MCCKF对复杂环境的适用性。针对上述问题，提出了一种基于鲁棒闭合路径校准的采煤机捷联惯性导航系统（SINS）/里程计（OD）组合导航系统。首先，基于采煤机运动约束模型分别建立速度测量误差方程和位置测量误差方程，并建立KF模型完成对采煤机位置的最优估计；然后，采用MCCKF取代传统的KF，降低传统闭合路径校准法中错误的预测位置对直线度检测的干扰；最后，建立具有自适应核带宽算法的MCCKF(AMCCKF)，在不预设核带宽参数的情况下即可获得良好的鲁棒性。 实验结果表明：AMCCKF的东向均方根误差（RMSE）为0.192 0 m，比MCCKF（核带宽=1）高2.65%；AMCCKF的北向RMSE为0.049 6 m，比MCCKF（核带宽=1）低30.53%。结合东向和北向误差，AMCCKF的圆概率误差（CEP）为0.142 2 m，较MCCKF（核带宽=1）降低了6.51%。在引入自适应核带宽后，AMCCKF可以达到甚至优于经过多次测试得到的固定核带宽MCCKF的性能，说明基于AMCCKF的组合导航系统具备更好的环境适应性。
Abstract:The Strapdown Inertial Navigation System (SINS) and Odometer (OD) integrated navigation system based on non-holonomic constraints and closed-loop path calibration is a widely used positioning scheme for shearers. In this method, the closed-loop path calibration requires accurate measurement of the actual advancing distance of the hydraulic support. However, traditional Kalman filtering (KF) struggles to handle outliers in the observations, and incorrect predicted positions can significantly reduce the accuracy of the closed-loop calibration, making it difficult to effectively detect the straightness of the shearer’s trajectory. The Maximum Correntropy Criterion Kalman filter (MCCKF) can capture higher-order statistics of the measurements, but the kernel bandwidth in MCCKF is usually empirically set, which limits its applicability in complex environments. To address the above issues, a shearer SINS/OD integrated navigation system based on robust closed-loop path calibration was proposed. First, velocity and position measurement error equations were established based on the shearer's motion constraint model, and a Kalman filter (KF) model was constructed to achieve optimal position estimation. Then, the MCCKF was adopted to replace the traditional KF, reducing the interference caused by erroneous predicted positions in the traditional closed-loop calibration method on straightness detection. Finally, an Adaptive Kernel Bandwidth algorithm was introduced into the MCCKF (AMCCKF), achieving good robustness without pre-setting the kernel bandwidth parameter. Experimental results showed that the eastward root mean square error (RMSE) of AMCCKF was 0.1920 m, which was 2.65% higher than that of MCCKF with a kernel bandwidth of 1. The northward RMSE of AMCCKF was 0.0496 m, 30.53% lower than that of MCCKF (bandwidth = 1). Considering both eastward and northward errors, the Circular Error Probable (CEP) of AMCCKF was 0.1422 m, which was 6.51% lower than that of MCCKF (bandwidth = 1). With the introduction of adaptive kernel bandwidth, the performance of AMCCKF can match or even exceed that of MCCKF with a fixed kernel bandwidth obtained through multiple tests, demonstrating that the AMCCKF-based integrated navigation system has better environmental adaptability.
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图 1 基于采煤机运动约束校准的组合导航系统原理
Figure 1. Principle of the integrated navigation system calibrated based on shearer motion constraints
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图 8 实验中第3次截割平面位置误差
Figure 8. Plane position error of the third cutting cycle in the experiment
表 1 实验中位置均方根误差
Table 1 Position RMSE in the experiment
m 截割次数 校准方案 东向RMSE 北向RMSE CEP 第1次 KF 0.342 3 0.155 9 0.293 3 第2次 KF 0.339 1 0.182 8 0.307 2 第3次 KF 0.464 7 0.825 0 0.759 2 Sage−Husa AKF 0.436 1 0.327 3 0.449 4 MCCKF（σ=3） 0.386 0 0.157 1 0.319 7 MCCKF（σ=1） 0.186 9 0.071 4 0.152 1 AMCCKF 0.192 0 0.049 6 0.142 2 
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