It is essential to level the drilling platform across which a drilling robot travels in a slant underground coal mine tunnel to ensure smooth operation of the drill rod. However, existing leveling methods do not provide dynamic performance under the drilling conditions of the underground coal mine. A four-point dynamic leveling algorithm is presented in this paper based on the platform attitude and support rod displacement (DLAAD). An experimental drilling robot demonstrates its dynamic leveling capability and ability to ensure smooth drill rod operations.
The attitude coordinate of the drilling robot is established according to its structure. A six-axis combined sensor is adopted to detect the platform attitude, thus revealing the three-axis Euler angles. The support rod displacement values are continuously detected by laser displacement sensors to obtain the displacement increment of each support rod as needed. The drilling robot is leveled according to the current support rod displacement and three-dimensional (3 D) attitude detected by the six-axis combined sensor dynamically.
Experimental results indicate that the DLAAD algorithm is correct and effectively levels the drilling platform dynamically. It can thus provide essential support in resolving drill rod sticking problems during actual underground coal mine drilling operations.
The DLAAD algorithm supports smooth drill rod operations in underground coal mines, which greatly enhances safety, reduces power consumption, and minimizes cost. The approach proposed here thus represents considerable benefits in terms of coal mine production and shows notable potential for application in similar fields.
The novel DLAAD algorithm and leveling control method are the key contributions of this work, they provide dynamical 3 D leveling and help to resolve drill rod sticking problems.
The authors would like to acknowledge Project of Shandong Province Higher Educational Science and Technology Program, China (No. J18KB020), and the Key Research and Development Plan of Shandong Province, China (No. 2019GGX104102).
Funding: This research was supported by Project of Shandong Province Higher Educational Science and Technology Program, China (No. J18KB020), and the Key Research and Development Plan of Shandong Province, China (No. 2019GGX104102).
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