Xueguan Song
Papers
2
Total Citations
28
H-Index
2
About
Xueguan Song is a leading researcher in robotics and intelligent manufacturing, with key contributions in autonomous mining machinery and precision sensor design. His most cited work, "Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning" (2022, 25 citations), pioneers a novel approach to automating heavy excavation equipment. By integrating point cloud perception with optimal trajectory planning, Song enables unmanned electric shovels to operate with high efficiency and safety in complex mining environments—a critical advancement for the industry’s push toward full autonomy. In parallel, his recent work on "Design of a Three-Axis Force Sensor Using Decoupled Compliant Parallel Mechanisms" (2024, 3 citations) tackles the persistent challenge of cross-axis coupling in multiaxis force sensors. This innovation, which enhances measurement accuracy for robotics and machine monitoring, demonstrates Song’s versatility in addressing fundamental sensing problems. Together, these contributions highlight his impact on both applied autonomous systems and core sensor technology, making him a notable figure in modern mechanical and robotic engineering.
Research Focus
Key Achievements
Top Papers
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- 2