Papers
8
Total Citations
119
H-Index
4
About
Yuming Cui is a leading researcher in robotic manipulation and autonomous navigation for complex, unstructured environments—particularly underground and industrial settings. His work centers on motion planning, obstacle detection, and multi-sensor fusion, with a strong emphasis on real-world deployability. Cui’s most influential contribution is the Improved_RRT path planning algorithm for robotic manipulators in cluttered spaces, which has earned 92 citations by developing collision detection models using cylinder and sphere bounding boxes. He has also advanced obstacle detection in human–machine interaction workspaces with a lightweight YOLOv3 variant, achieving faster and more accurate results. His research extends to full-coverage cutting path planning for robotized roadheaders to enhance stability when cutting through gangue, and cooperative motion control for multi-arm tunnel drilling robots using genetic algorithms and neural networks. Notably, Cui addresses the critical challenge of autonomous positioning in dark, GPS-denied underground environments through visual-inertial fusion, enabling reliable navigation for mining vehicles and robots. With over 100 cumulative citations and a portfolio spanning from anchor beam supporting robots to multi-sensor pose perception, Cui’s work directly supports the intelligentization of mining and industrial automation, making him a key figure in applied robotics.
Research Focus
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