Papers
1
Total Citations
2
H-Index
1
About
Xinhua Yu’s research centers on industrial robotics, precision calibration, and intelligent control systems. His most notable contribution is a novel method for calibrating the position and posture of six-degree-of-freedom industrial robots, integrating Particle Swarm Optimization (PSO) with backpropagation (BP) neural networks and Finite Element Analysis (FEA). This approach addresses the critical challenge of nonlinear error compensation in real-world industrial settings, significantly enhancing robot accuracy without costly hardware upgrades. While his work has accumulated over 2 citations, its practical value lies in bridging simulation and real-world performance—a key step toward more reliable automation. Yu’s research exemplifies how neural network-based identification can transform traditional calibration processes, offering a scalable solution for manufacturing environments. His focus on combining data-driven models with mechanical analysis marks him as a thoughtful contributor to the field of industrial robotics, where precision and adaptability are paramount.
Research Focus
Key Achievements
Top Papers
- 1Calibration of A 6-DOF IR Based on PSO-BP Neural Network and FEA2 citations · 2020