SINS/OD integrated navigation system for shearer based on robust closed-loop path calibration

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.