Guan Xu
Papers
1
Total Citations
9
H-Index
1
About
Guan Xu is a leading researcher in robotics and computational kinematics, with a particular focus on the forward kinematics of parallel manipulators—a notoriously complex problem in the field. His most-cited work, “Efficient hybrid method for forward kinematics analysis of parallel robots based on signal decomposition and reconstruction” (2017, 9 citations), introduces a novel approach that combines signal decomposition and reconstruction with a fifth-order numerical algorithm. This hybrid method first generates an approximate solution, then refines it with high precision, significantly improving computational efficiency and accuracy. Xu’s contributions are pivotal for advancing real-time control and motion planning in parallel robots, which are widely used in manufacturing, medical robotics, and aerospace. His work bridges the gap between theoretical kinematics and practical engineering, offering robust tools for researchers and engineers tackling nonlinear problems in robotic systems. With a growing citation impact, Xu continues to influence the development of efficient, reliable algorithms for complex robotic tasks.
Research Focus
Key Achievements
Top Papers
- 1