Papers
47
Total Citations
1,193
H-Index
19
About
Di-Hua Zhai is a prominent researcher in robotics and control systems, whose work spans robotic grasp detection, teleoperation, model predictive control, and brain-computer interfaces. With a publication record accumulating hundreds of citations, Zhai has made substantial contributions to both the theoretical foundations and practical applications of intelligent robotic systems. Among Zhai's most recognized contributions is the development of SE-ResUNet (2022, 144 citations), a novel deep learning framework that integrates squeeze-and-excitation channel attention with residual networks to achieve high-accuracy robotic grasp detection from RGB-D imagery. Complementing this, SKGNet (2022, 53 citations) further advances real-time grasp detection through selective kernel convolution. In control theory, Zhai's robust model predictive tracking control for robot manipulators (2020, 132 citations) has been widely adopted for handling real-world disturbances and physical constraints. Zhai's extensive work on teleoperation systems—addressing time-varying delays, input saturation, kinematic uncertainties, and finite-time control—demonstrates a sustained commitment to making remote robotic operation safer and more reliable, collectively earning over 300 citations across multiple studies. More recently, Zhai has ventured into brain-computer interface research, proposing the MVMD-CCA algorithm (2021, 62 citations) for SSVEP-based robot control, reflecting a forward-looking integration of neurotechnology with robotics.
Research Focus
Key Achievements
Top Papers
- 1SE-ResUNet: A Novel Robotic Grasp Detection Method144 citations · 2022
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- 7SKGNet: Robotic Grasp Detection With Selective Kernel Convolution53 citations · 2022
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