Papers
3
Total Citations
33
H-Index
3
About
Yukun Zheng is a robotics researcher whose work focuses on the intersection of adaptive control, mobile manipulation, and machine vision. His key contributions lie in developing robust control strategies for complex robotic systems operating under uncertainty. Zheng’s most cited work, “Adaptive neural control for mobile manipulator systems based on adaptive state observer” (2022, 23 citations), introduces a novel approach to handle unknown dynamics and external disturbances in mobile manipulators, significantly enhancing their stability and precision. He has also advanced the field of tracked mobile robots (TMRs), proposing an adaptive sliding mode control method (2020, 5 citations) that effectively mitigates the impact of unknown interference forces, a critical challenge for real-world deployment. In the domain of industrial automation, Zheng tackled the problem of dynamic grasping with incomplete visual information (2019, 5 citations), developing a Characteristic Region Minimum Rectangle Fitting algorithm for accurate workpiece pose detection. This work directly addresses practical bottlenecks in automatic feeding processes. Through these contributions, Zheng demonstrates a strong commitment to bridging theoretical control methods with practical robotic applications, making his research highly relevant for students and engineers working on autonomous systems and intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
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