Shengjun Wen
Papers
9
Total Citations
79
H-Index
5
About
Shengjun Wen is a leading researcher in robotics and nonlinear control systems, with a focus on advancing humanoid robot autonomy and precision manipulation. His work spans robust nonlinear control, visual perception, and multi-robot coordination, with major contributions to operator-based robust right coprime factorization for uncertain systems—a framework that enables perfect tracking control for robot arms despite dynamic uncertainties. Wen’s recent research integrates deep learning with monocular vision, as demonstrated in his highly cited 2023 paper on target localization and grasping using the YOLOv8 network and monocular ranging, which addresses distance-related errors in visual positioning. He has also pioneered distributed model predictive control for dual humanoid robots, enabling coordinated transport tasks through leader-follower architectures. With over 80 citations across his top papers, Wen’s impact is evident in both foundational theory and practical applications, including fault diagnosis for industrial robot drive systems using nonlinear spectrum analysis. His work on the NAO robot platform—from trajectory planning to adaptive control—has established him as a key figure in bridging theoretical control methods with real-world robotic systems, making his research essential for students and engineers developing next-generation autonomous robots.
Research Focus
Key Achievements
Top Papers
- 1New Developments on Robust Nonlinear Control and Its Applications23 citations · 2014
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- 5Target Recognition and Navigation Path Optimization Based on NAO Robot6 citations · 2022
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- 8Trajectory planning of NAO robot arm based on target recognition3 citations · 2017
- 9Modelling and adaptive control of NAO robot arm3 citations · 2021