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About

Xiangzhi Chen is a leading researcher in intelligent robotic welding, with a focused expertise in real-time seam tracking and laser vision systems. His work has fundamentally advanced the precision and autonomy of arc welding robots, enabling them to adapt to complex, curved weld paths. Chen’s major contributions include the development of robust hand–eye calibration methods using semidefinite programming, which ensures accurate coordination between the robot and its laser vision sensor. He has also pioneered the integration of deep reinforcement learning with convolution filters to create adaptive seam tracking systems that learn and optimize their behavior in real-time. The impact of his research is demonstrated by his highly cited papers, including his 2018 work on real-time seam tracking control (86 citations) and his 2021 study on reinforcement learning-based tracking (80 citations). By combining classical control theory with modern machine learning, Chen has produced practical, deployable solutions for automated welding, making his work essential reading for researchers in manufacturing robotics, computer vision, and industrial automation.