Wang Yao-nan
Papers
5
Total Citations
104
H-Index
4
About
Wang Yao-nan is a leading researcher in intelligent robotics, with a primary focus on solving complex control and navigation challenges for autonomous mobile robots and robotic manipulators. His work is distinguished by the innovative integration of computational intelligence methods—including neural networks, fuzzy logic, and bio-inspired optimization—to enhance robot autonomy in dynamic, unstructured environments. A key contribution is his development of a neural-network-based inverse kinematics solution for robot manipulators under joint subspace constraints, a highly cited work (59 citations) that addresses the critical problem of training data collection without explicit inverse kinematic expressions. He has also advanced autonomous navigation through a transferable belief model for dynamic environments (30 citations) and pioneered a model predictive control strategy using an electromagnetism-like mechanism for path-tracking under actuator saturation and external disturbances. Further, his research on fuzzy cerebellar model articulation controllers has improved adaptive tracking control, while his comprehensive review of particle filter algorithms for mobile robot localization and map-building has guided subsequent work in the field. Wang Yao-nan’s contributions are foundational to the development of more intelligent, adaptable, and robust robotic systems.
Research Focus
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Top Papers
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