Xiaolei Wu
Papers
1
Total Citations
3
H-Index
1
About
Xiaolei Wu is a researcher in robotics and intelligent optimization, with a primary focus on the identification of inertial parameters for industrial robots—a critical area for improving robot control accuracy and performance. Wu's major contribution lies in the development of novel optimization algorithms for excitation trajectory design, which is essential for precise parameter identification. In their most cited work, Wu proposed the particle gray wolf optimization algorithm (PSOGWO), a hybrid metaheuristic that combines particle swarm optimization with the gray wolf optimizer to efficiently design excitation trajectories. This step-by-step identification approach enhances the accuracy and robustness of inertial parameter estimation, addressing a key bottleneck in industrial robotics. Although early in their career, with the 2022 paper accumulating 3 citations, Wu's work demonstrates significant potential for impact in robotics and optimization communities. Their research bridges theoretical algorithm development with practical robotic applications, offering valuable tools for engineers seeking to improve robot dynamics modeling and control. Wu's contributions are particularly relevant for advancing automation and precision in manufacturing, where accurate robot models are essential for tasks like path planning and force control.
Research Focus
Key Achievements
Top Papers
- 1