Research on an Error Compensation Method of SINS of a Mine Monorail Crane

Hai Jiang, Xiaodong Ji (), Yang Yang, Jialu Du and Miao Wu
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Hai Jiang: School of Mechanical Electronic and Information Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
Xiaodong Ji: State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Yang Yang: Shijiazhuang Coal Mining Machinery Co., Ltd., Shijiazhuang 050043, China
Jialu Du: School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
Miao Wu: School of Mechanical Electronic and Information Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China

Energies, 2023, vol. 16, issue 16, 1-18

Abstract: Underground coal mines belong to the GNSS-denied environment, and the Strapdown Inertial Navigation System (SINS) has a significant advantage in the precise positioning of equipment in this environment because of its operation without requiring interaction with external information and strong anti-interference capabilities. Nonetheless, the vibrations of the installation platform adversely affect the positioning accuracy of SINS. This article focuses on the monorail crane in coal mines as the subject of research, developing a dynamic model for the motion unit consisting of the “track + drive unit + driver’s cab”, while analyzing the relationship between track roughness conditions and the vibration excitation of this unit. Subsequently, utilizing the dynamic model, the study calculated the angular and linear vibration characteristics and formulated models to address coning error and sculling error specific to the SINS in this vibration condition. Lastly, by employing a multi-sample compensation algorithm, this article compensated for positioning errors in the SINS caused by track roughness-induced vibrations during uniform straight-line motion of the motion unit, thus achieving optimal positioning information for the monorail crane. The simulation results demonstrated that employing a four-sample compensation algorithm reduces the coning error in SINS positioning calculations by a minimum of 50% and decreases the sculling error by at least 31%, satisfying the positioning accuracy requirements for precise parking of the monorail crane during the transportation phase, while establishing the foundation for autonomous precise positioning and integrated navigation of underground track transport equipment in coal mines.

Keywords: monorail crane; strapdown inertial navigation; precise positioning; coning error; sculling error; multi-sample algorithm; error compensation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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