A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/2/128 |
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| author | Jiameng Cheng Dongjie Wang Jiming Liu Pengjiang Wang Weixiong Zheng Rui Li Miao Wu |
| author_facet | Jiameng Cheng Dongjie Wang Jiming Liu Pengjiang Wang Weixiong Zheng Rui Li Miao Wu |
| author_sort | Jiameng Cheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eba702278c1944fcb45e440a88da4ab6 |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-eba702278c1944fcb45e440a88da4ab62025-08-20T02:03:40ZengMDPI AGMachines2075-17022025-02-0113212810.3390/machines13020128A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial NavigationJiameng Cheng0Dongjie Wang1Jiming Liu2Pengjiang Wang3Weixiong Zheng4Rui Li5Miao Wu6Department of Mechanical Engineering, School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaDepartment of Mechanical Engineering, School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaDepartment of Mechanical Engineering, School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaChina Academy of Safety Science and Technology, Beijing 100012, ChinaDepartment of Energy and Power Engineering, School of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaBeijing Bluevision Science and Technology Co., Ltd., Beijing 100085, ChinaDepartment of Mechanical Engineering, School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaA 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.https://www.mdpi.com/2075-1702/13/2/128boom-type roadheaderfiber-optic inertial navigationbinocular visioncombined positioningKalman filtering |
| spellingShingle | Jiameng Cheng Dongjie Wang Jiming Liu Pengjiang Wang Weixiong Zheng Rui Li Miao Wu A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation Machines boom-type roadheader fiber-optic inertial navigation binocular vision combined positioning Kalman filtering |
| title | A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation |
| title_full | A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation |
| title_fullStr | A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation |
| title_full_unstemmed | A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation |
| title_short | A Combination Positioning Method for Boom-Type Roadheaders Based on Binocular Vision and Inertial Navigation |
| title_sort | combination positioning method for boom type roadheaders based on binocular vision and inertial navigation |
| topic | boom-type roadheader fiber-optic inertial navigation binocular vision combined positioning Kalman filtering |
| url | https://www.mdpi.com/2075-1702/13/2/128 |
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