Xiaohui Xie

Papers

Total Citations

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About

Xiaohui Xie is a robotics and computer vision researcher whose work focuses on advancing intelligent control systems for industrial automation. Their most-cited paper, "Visual tracking control of SCARA robot system based on deep learning and Kalman prediction method" (2021, 13 citations), addresses critical limitations in industrial machine vision tracking—specifically low accuracy and poor recognition during workpiece movement. In this work, Xie introduced an innovative approach that combines stochastic gradient descent-optimized convolutional neural networks for motion foreground segmentation with Kalman prediction for real-time tracking, significantly improving the precision and reliability of SCARA robot visual servoing. This contribution bridges deep learning and classical control theory, offering practical solutions for manufacturing environments requiring dynamic object tracking. Xie's research sits at the intersection of robotics, deep learning, and automation, with potential applications in assembly lines, pick-and-place operations, and quality inspection. While still early in their citation impact trajectory, Xie's work demonstrates a clear commitment to solving real-world industrial challenges through the integration of modern AI techniques with traditional robotic control systems—a promising direction for the future of smart manufacturing.