YU A-long
Papers
3
Total Citations
13
H-Index
2
About
YU A-long has made pioneering contributions to the field of robotic sensing, with a focused research program on the dynamic modeling and compensation of robot wrist force sensors. His work centers on integrating advanced computational intelligence techniques—specifically improved genetic algorithms (IGA) and wavelet neural networks (WNN)—to enhance the accuracy and responsiveness of multi-dimensional force sensors. A-long’s major contributions include developing novel dynamic modeling methods that significantly improve sensor performance under real-world robotic operating conditions. His most cited paper, “Research on the dynamic modeling based on genetic wavelet neural network for the robot wrist force sensor” (2008, 7 citations), introduces a hybrid IGA-WNN approach applied to a new sensor design for the Motoman V3X robot. This work, along with subsequent studies on dynamic compensation (2010, 2 citations), demonstrates his sustained effort to address the critical challenge of sensor lag and error in robotic manipulation. Though his citation counts are modest, A-long’s research represents a dedicated, methodical approach to solving a specific engineering problem—improving the fidelity of force feedback in robotic systems—laying groundwork for more precise and adaptive robotic interactions.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3