Papers
3
Total Citations
40
H-Index
3
About
Gen Yang is a leading researcher in intelligent robotic systems, with a primary focus on welding automation and collaborative robot dynamics. Her work bridges the gap between advanced sensing and precise robotic control, addressing critical challenges in manufacturing and human-robot interaction. Yang’s most impactful contribution is a novel 3D complex welding seam tracking method for symmetrical robotic MAG welding, which uses laser vision sensing to enable real-time, accurate torch guidance along intricate paths—a breakthrough for automated welding processes. This work has garnered 19 citations since 2023. She has also made significant strides in collaborative robotics, developing a systematic error compensation strategy using an optimized recurrent neural network to improve dynamic modeling and control (13 citations). Further, Yang pioneered a parameter identification method for collaborative robots based on an improved artificial fish swarm algorithm (8 citations), enhancing model accuracy for precise control. Her research is characterized by a practical, problem-solving approach that integrates machine learning, optimization algorithms, and sensor fusion. Yang’s achievements are particularly notable for advancing the reliability and autonomy of industrial robots, with her work serving as a foundation for next-generation manufacturing systems.
Research Focus
Key Achievements
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