Papers
32
Total Citations
917
H-Index
16
About
Yangming Li is a versatile robotics and computational intelligence researcher whose work spans neural dynamics, surgical robotics, and machine learning-based control systems. His most influential contribution, "Neural Dynamics for Cooperative Control of Redundant Robot Manipulators" (2018, 203 citations), established a distributed neural-dynamic framework enabling multiple redundant manipulators to achieve coordinated motion under limited communication constraints—a landmark result in multi-robot systems. Complementing this, his work on discrete computational neural dynamics models addresses time-dependent Sylvester equations with direct applications to robotics and MIMO systems. A significant thread of Li's research focuses on cable-driven surgical robotics, where he has made pioneering contributions to sensorless grip force estimation using Gaussian Process Regression and dynamic modeling, addressing the critical absence of haptic feedback in minimally invasive surgery. His hysteresis and cable-pulley friction modeling work further advances the fidelity of surgical robot simulations. He also contributed to the development of "Roboscope," a flexible single-portal surgical robot, and applied Unscented Kalman Filtering combined with 3D vision to improve joint angle estimation accuracy. Across computer vision, data association, and instrument segmentation for endoscopic surgery, Li's diverse portfolio—accumulating over 600 citations—reflects a researcher meaningfully bridging theoretical rigor with real-world clinical and robotic applications.
Research Focus
Key Achievements
Top Papers
- 1Neural Dynamics for Cooperative Control of Redundant Robot Manipulators203 citations · 2018
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