Abstract
Accurate positioning of the shearer with a strapdown inertial navigation system (SINS) is the key technology to realize the automation of the longwall face. Unfortunately, the existing positioning methods have a strong dependence on the attitude accuracy of the SINS. The position errors gradually increase with the drift of the SINS attitude. To reduce the dependence on the SINS attitude and further increase the shearer positioning accuracy, this paper proposes a positioning method based on SINS and light detection and ranging (LiDAR) with velocity and absolute position constraints. A Kalman filter (KF) model based on these constraints was established. Simulation analysis shows that the attitude calibration between the shearer body, SINS and LiDAR, and the initial attitude alignment of the SINS are the keys to determining the shearer positioning accuracy. Even if there are small horizontal bends in the running track of the shearer and the features have small horizontal errors, an excellent positioning effect can still be obtained. In addition, four cutting processes were simulated with a reciprocating travel of 44.6 m and an advance distance of 1.2 m. Compared with the relative positioning method, the positioning accuracy of the proposed method was improved by 37%, 63%, 76%, and 69% from the first to the fourth cutting cycle, respectively, calculated by spherical error probable (SEP) values, and positioning accuracy had a lower dependence on the installation deflection angles between the SINS, the LiDAR, and the SINS attitude accuracy.
Highlights
IntroductionCoal still accounts for a large proportion of the world’s primary energy consumption structure
As a precious resource, coal still accounts for a large proportion of the world’s primary energy consumption structure
Compared with the relative positioning method, the positioning accuracy of the proposed method was improved by 37%, 63%, 76%, and 69% from the first to the fourth cutting cycle, respectively, calculated by spherical error probable (SEP) values, and positioning accuracy had a lower dependence on the installation deflection angles between the strapdown inertial navigation system (SINS), the light detection and ranging (LiDAR), and the SINS attitude accuracy
Summary
Coal still accounts for a large proportion of the world’s primary energy consumption structure. The poor mining environment with high temperatures, high gas quantities, high ground stresses, etc., poses a huge threat to the personal safety of miners [1]. Pneumoconiosis is one of the diseases that threatens the health of miners which is widespread in major coal-producing countries in the world. In Poland, there was a total of 7340 cases of coal worker’s pneumoconiosis in 2000–2017 [2], while in. China the number was nearly 7949 in 2019 alone [3]. Coal mine safety accidents directly threaten the safety of miners.
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