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A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation

Sábháilte in:
Foilsithe in:Machines vol. 13, no. 2 (2025), p. 128
Príomhchruthaitheoir: Cheng, Jiameng
Rannpháirtithe: Wang, Dongjie, Liu, Jiming, Wang, Pengjiang, Zheng, Weixiong, Li, Rui, Wu, Miao
Foilsithe / Cruthaithe:
MDPI AG
Ábhair:
North Pole
Measurement methods
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
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Achoimre: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.
ISSN:2075-1702
DOI:10.3390/machines13020128
Foinse:Engineering Database