Shuyong Duan

Papers

Total Citations

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About

Shuyong Duan is a researcher focused on advancing the precision and autonomy of robotic systems, with key contributions in robot arm calibration, uncertainty quantification, and mobile robot path planning. His work addresses fundamental challenges in robotics: how to accurately identify joint stiffnesses and parameter uncertainties in real time to improve control and motion accuracy. Duan’s most cited papers introduce innovative inverse identification techniques using two-way neural networks, specifically TubeNets, to quantitatively determine joint stiffnesses and parameter uncertainties in robot arms—enabling more reliable posture and movement control. These methods have garnered significant attention, with each paper accumulating 9 citations, reflecting their practical relevance in robotics research. More recently, Duan has extended his work to mobile robotics, proposing a novel adaptive path-smoothering optimization method that enhances path smoothness and safety without manual intervention. This 2024 publication, already with 3 citations, demonstrates his ongoing impact in developing intelligent, autonomous navigation solutions. Duan’s contributions are notable for bridging neural network-based uncertainty inversion with real-world robotic applications, offering systematic approaches that improve both static calibration and dynamic path optimization for next-generation robots.