Yuanqing Li
Papers
4
Total Citations
531
H-Index
4
About
Yuanqing Li is a leading researcher in robotics and neural dynamics, whose work has fundamentally advanced the control and motion planning of redundant robot manipulators and humanoid robots. His primary research areas include recurrent neural networks, repetitive motion planning, and joint-angle-drift mitigation. Li’s major contributions lie in developing innovative neural-dynamic methods and numerical schemes to solve complex robotic problems, such as the joint-drift phenomenon that plagues redundant manipulators. His most cited work (185 citations) compares three recurrent neural networks and numerical methods for repetitive motion planning, while another highly influential paper (148 citations) introduces a neural-dynamic-based dual-arm cyclic-motion-generation scheme for humanoid robots. Li also pioneered the varying-parameter convergent-differential neural network (124 citations), which offers a novel solution to joint-angular-drift, and developed a tricriteria optimization-coordination motion scheme (74 citations) for complex path planning. His work is notable for its practical impact on improving robot precision and efficiency, making him a key figure in the field of robotic control and neural computation.
Research Focus
Key Achievements
Top Papers
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