Haimei Wu
Papers
2
Total Citations
20
H-Index
2
About
Haimei Wu is a rising researcher in robotics and neural computation, whose work focuses on advancing real-time control and safety for robotic manipulators. Her primary research areas include time-variant quadratic programming (TVQP), zeroing neural networks (ZNN), and obstacle and joint-limit avoidance for redundant robotic systems. Wu’s major contribution lies in developing efficient, non-iterative neural controllers that solve complex, constrained optimization problems—such as those with equality, inequality, and bound constraints—enabling faster and more reliable robot motion planning. Her most-cited paper, “A Lower Dimension Zeroing Neural Network for Time-Variant Quadratic Programming Applied to Robot Pose Control” (2024), has already garnered 17 citations, reflecting its immediate impact on the field. In her 2023 work on obstacle-avoidance solutions, she introduced a novel neural controller that integrates obstacle and joint-limit constraints into a quadratic programming framework, enhancing the safety and dexterity of redundant manipulators. Though early in her career, Wu’s innovative approaches to real-time robotic control are gaining recognition, positioning her as a promising contributor to intelligent robotics and neural optimization.
Research Focus
Key Achievements
Top Papers
- 1
- 2