Papers
4
Total Citations
28
H-Index
4
About
Jianbo Dai is a leading researcher in intelligent mining robotics, with a focus on autonomous systems for hazardous underground environments. His work spans kinematics analysis, computer vision, and robust control for specialized mining equipment. Dai’s most cited paper (2024, 9 citations) addresses the challenge of automating pile driving by developing a 6-DOF hydraulic robotic arm using an improved Denavit-Hartenberg parameter method for precise kinematics and trajectory planning. He also pioneered a novel pressure relief hole recognition method for drilling robots in rockburst mines (2022, 8 citations), combining SinGAN with an improved Faster R-CNN to enable accurate hole detection for intelligent drilling. To enhance robot perception in low-visibility coal mines, Dai proposed an adaptive image enhancement method based on no-reference quality evaluation (2024, 6 citations), critical for autonomous operation. His work on walking trajectory tracking control for rockburst prevention robots (2024, 5 citations) uses uncertainties estimation and a state observer to improve robustness. With over 28 citations across these foundational papers, Dai is advancing the next generation of autonomous, safe, and efficient mining robotics.
Research Focus
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