Wenhui Wang
Papers
3
Total Citations
12
H-Index
2
About
Wenhui Wang is a robotics researcher whose work focuses on control systems, autonomous navigation, and human-robot interaction for critical applications. Wang’s key research areas include repetitive control for robotic manipulators, deep reinforcement learning for underwater robot path planning, and 3D human body reconstruction for disaster rescue operations. In their most cited work (8 citations), Wang developed a repetitive control scheme for 2-DOF robotic manipulators using improved cubic B-spline functions, integrating an iterative controller with a disturbance observer to enhance precision. Their 2024 paper on large-scale path planning for underwater robots leverages deep reinforcement learning to improve navigation accuracy in complex underwater environments. Wang also proposed a viewpoint auto-changing RGB-D sensor system for reconstructing detailed human bodies in post-disaster scenarios, addressing the critical need for casualty extraction while minimizing rescuer risk. With over 12 total citations across these key publications, Wang’s contributions demonstrate a commitment to advancing robotic autonomy in challenging, real-world environments—from industrial manipulation to search-and-rescue operations.
Research Focus
Key Achievements
Top Papers
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