Papers
7
Total Citations
69
H-Index
5
About
Lei Si is a leading researcher in intelligent robotics for underground mining, with a focus on autonomous navigation, perception, and control in extreme environments. His work addresses the critical challenges of deploying mobile robots in coal mines, where narrow roadways, heavy dust, and poor illumination severely limit conventional positioning and sensing methods. Si’s major contributions include developing a novel fusion positioning framework that integrates redundant IMUs, UWB, and visual images, achieving robust localization for anti-punching drilling robots and mobile equipment in GPS-denied underground settings. He has also advanced path planning with an improved bat algorithm incorporating inertial weight and Levy flight to prevent premature convergence. In perception, Si pioneered a SinGAN-based data augmentation method combined with improved Faster R-CNN for accurate pressure relief hole recognition, and an adaptive image enhancement technique using no-reference quality evaluation. His work on walking trajectory tracking control, employing state observers to estimate uncertainties, further enhances drilling robot precision for rockburst prevention. With over 70 citations across his most-cited papers, Si’s research is pivotal for realizing intelligent, autonomous coal mining and improving safety in hazardous underground operations.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7