Yueling Wang
Papers
4
Total Citations
23
H-Index
3
About
Yueling Wang is a robotics researcher whose work focuses on advancing autonomous navigation, control systems, and humanoid robot manipulation. Her key research areas include simultaneous localization and mapping (SLAM), iterative learning control, and hybrid robotic arm dynamics. Wang’s most significant contribution is an extended Kalman filter-SLAM method that integrates Harris-scale-invariant feature transform (SIFT) recognition with laser mapping, enabling humanoid robots to navigate unknown environments by combining real-time object recognition with precise localization—a novel approach that avoids exhaustive environmental mapping. Her work on discrete PID-type iterative learning control for mobile robots has demonstrated strong adaptability and fast convergence, while her fuzzy self-tuning disturbance decoupling controller for a 3-DOF serial-parallel hybrid humanoid arm advanced trajectory tracking in throwing tasks. With over 20 citations across her most-cited papers, Wang’s research has practical implications for autonomous robotics, particularly in dynamic and unstructured settings. Her innovative use of variable forgetting factors in open-closed-loop iterative learning control further showcases her ability to refine robot motion precision, making her a notable contributor to the field of intelligent robotics and control systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Discrete PID-Type Iterative Learning Control for Mobile Robot7 citations · 2016
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