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About

Dr. Yanxi Yang is a pioneering researcher in intelligent robotic control systems, with a primary focus on visual servoing, neural network-based robot control, and adaptive tracking algorithms. Her work bridges the gap between computer vision and robotic manipulation, developing innovative approaches that enable robots to perceive and interact with their environments without extensive calibration. Dr. Yang's most significant contributions include the development of self-learning visual servoing algorithms that use neural networks to directly map visual inputs to joint movements, eliminating the need for traditional camera calibration—a breakthrough that has garnered 8 citations. She has also advanced robot trajectory tracking through fuzzy immune PD-type controllers, combining biological immune feedback mechanisms with fuzzy logic and PID control to handle dynamic nonlinearities. Her research extends to soccer robotics, where she improved moving object detection and tracking using adaptive Kalman filters to handle maneuverability uncertainties. Additional notable work includes genetic algorithm-based visual servoing for unknown targets and hybrid controllers combining fuzzy neural networks with CMAC networks for robust manipulator control. With over 29 cumulative citations across her six most-cited papers, Dr. Yang's research continues to influence the fields of intelligent robotics and autonomous systems.