Jiuxiang Dong

Papers

Total Citations

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About

Jiuxiang Dong is a leading researcher in robotics and control systems, with a primary focus on image-based visual servoing (IBVS) and robust model predictive control. His work addresses fundamental challenges in real-time robotic vision and autonomous manipulation. In his highly cited 2021 paper (20 citations), Dong introduced a novel Kalman filter-based observer structure for depth estimation in IBVS, enabling more accurate and stable visual feedback control. His 2020 work (17 citations) advanced the field further by developing a robust online model predictive control method for IBVS in polar coordinates, leveraging tensor product model transformation to handle system uncertainties. These contributions have significant implications for autonomous robots, drone navigation, and industrial automation, where precise visual guidance is critical. Dong’s research is distinguished by its practical approach to solving real-time control problems, combining rigorous theoretical foundations with implementable algorithms. His work continues to influence the development of more reliable and adaptive robotic systems, making him a key figure in the intersection of computer vision and control theory.