Wanquan Liu
Papers
12
Total Citations
114
H-Index
5
About
Dr. Wanquan Liu is a leading researcher at the intersection of robotics, control systems, and intelligent perception. His work centers on developing advanced robotic systems with enhanced autonomy, precision, and adaptability, spanning from point cloud-based perception to the control of complex manipulators. A key contribution is his novel graph convolutional network (GCN) model for point cloud classification, which robustly handles pose variances (33 citations), significantly advancing 3D scene understanding. In medical and surgical robotics, Dr. Liu has made notable strides, including visual servo control for endoscope-holding robots using multi-objective optimization (22 citations) and kinematic modeling for acupuncture robots (10 citations). He has also pioneered the design and self-calibration of modular cable-driven snake-like manipulators (22 citations), addressing critical challenges in compliant control. His theoretical work on recurrent neural networks for repetitive tracking control (10 citations) tackles the practical problem of random initial errors compounded by noise. More recently, Dr. Liu has contributed to enhanced LiDAR-based SLAM frameworks (5 citations) and rehabilitation monitoring robots for quantitative gait measurement in Parkinson’s disease (3 citations). With over 100 citations across his most-cited works, Dr. Liu’s research is driving innovation in autonomous systems, medical robotics, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1A novel GCN-based point cloud classification model robust to pose variances33 citations · 2021
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- 5Kinematic modeling and simultaneous calibration for acupuncture robot10 citations · 2024
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