Content area
A positioning method for a roadheader based on fiber-optic strap-down inertial navigation and binocular vision is proposed to address the issue of low measurement accuracy of the mining machine position caused by single-sensor methods in underground coal mines. A vision system for the mining machine position is constructed based on the four-point target fixed on the body of the roadheader, and the position and attitude information of the roadheader are obtained by combining the inertial navigation on the body. To deal with the problem of position detection inaccuracies caused by the accumulation of errors in inertial navigation measurements over time and disturbances from body vibrations to the combined positioning system, an Adaptive Derivative Unscented Kalman Filtering (ADUKF) algorithm is proposed, which can suppress the impact of process variance uncertainties on the filtering. The simulation results demonstrate that, compared to the Unscented Kalman Filtering algorithm, the position errors in the three directions are reduced by 20%, 20.68%, and 28.57%, respectively. Experiments demonstrate that the method can compensate for the limitations of single-measurement methods and meet the positioning accuracy requirements for underground mining standards.
Details
Position measurement;
Mines;
Underground mining;
Coal mining;
Cameras;
Accuracy;
Adaptive systems;
Binocular vision;
Underground mines;
Adaptability;
Sensors;
Fiber optics;
Mining machinery;
Algorithms;
Methods;
Navigation systems;
Inertial coordinates;
Vision systems;
Optics;
Coal mines;
Kalman filters;
Inertial navigation;
Position errors
; Wang, Dongjie 1
; Liu, Jiming 1 ; Wang, Pengjiang 2
; Zheng, Weixiong 3 ; Li, Rui 4 ; Wu, Miao 1 1 Department of Mechanical Engineering, School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China;
2 China Academy of Safety Science and Technology, Beijing 100012, China;
3 Department of Energy and Power Engineering, School of Mechanical Engineering, Tsinghua University, Beijing 100084, China;
4 Beijing Bluevision Science and Technology Co., Ltd., Beijing 100085, China;