Tieqi Wang
Papers
2
Total Citations
51
H-Index
2
About
Tieqi Wang is a researcher specializing in robotics and control systems, with a primary focus on visual servoing—the use of visual feedback to control robotic motion. His key contributions lie in advancing image-based visual servoing (IBVS) through innovative model predictive control and neural network techniques. Wang’s most cited work, “Quasi‐Min‐Max Model Predictive Control for Image‐Based Visual Servoing with Tensor Product Model Transformation” (2014, 48 citations), introduces a novel controller that transforms the image Jacobian matrix into a convex combination of linear time-invariant forms using tensor-product modeling. This approach significantly enhances the robustness and performance of visual servoing systems. Additionally, his 2012 paper on neural network-based image moments proposes two novel features that estimate rotational angles for planar objects, addressing a fundamental challenge in feature selection for IBVS. While his citation counts reflect a focused impact within the control and robotics community, Wang’s work demonstrates a deep commitment to solving practical problems in real-time robotic vision and control, making his research valuable for students and engineers working on autonomous systems and visual feedback control.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neural network-based image moments for visual servoing of planar objects3 citations · 2012