Xiaolong Yu
Papers
5
Total Citations
35
H-Index
4
About
Xiaolong Yu is a robotics researcher whose work spans surgical robotics, industrial automation, and intelligent motion planning. His early contributions focused on robot-assisted surgery, where he investigated control strategies for soft tissue grasping, comparing transient performance across multiple controller designs to address the challenges posed by nonlinear tissue mechanics — work that has accumulated 13 citations and remains relevant to surgical tool precision. Over time, Yu expanded his focus toward industrial robotics, developing sophisticated trajectory planning methodologies that simultaneously optimize energy efficiency and operational productivity. His neural network-based approach to time-energy optimal trajectory resolution and his multi-objective optimization framework for customized industrial robots reflect a commitment to bridging theoretical control theory with smart manufacturing demands. Yu has also made notable contributions to robotic perception, proposing a multi-view keypoint detection method for object pose estimation that improves grasping reliability in cluttered environments. His work on 3D point cloud-driven trajectory planning for non-destructive testing further demonstrates his versatility across application domains. Collectively, Yu's research addresses critical challenges in making robots more precise, efficient, and adaptable — qualities increasingly vital as automation becomes central to modern industry and healthcare.
Research Focus
Key Achievements
Top Papers
- 1Comparison of transient performance in the control of soft tissue grasping13 citations · 2007
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