Zhangwei Chen
Papers
2
Total Citations
60
H-Index
2
About
Zhangwei Chen is a robotics researcher whose work centers on improving the precision and reliability of robotic systems, with a particular focus on kinematic calibration and positioning accuracy. His research addresses one of the most persistent challenges in industrial robotics: the uneven distribution of positioning errors caused by non-geometric factors such as joint flexibility, link deformation, and heavy mechanical loads — issues that traditional calibration methods struggle to resolve effectively. Chen's most influential contribution, "A Robot Calibration Method Based on Joint Angle Division and an Artificial Neural Network" (2019), has accumulated 56 citations and demonstrates his innovative approach of combining workspace segmentation with machine learning to achieve superior calibration results across a robot's full range of motion. Complementing this, his work on two-stage parameter identification for heavy-load robots further refines calibration strategies for demanding industrial applications where mechanical deformation introduces complex nonlinear errors. By bridging classical kinematic modeling with modern artificial intelligence techniques, Chen has made meaningful strides toward more accurate and adaptable robotic systems. His contributions are particularly valuable for researchers and engineers working on precision manufacturing, automation, and industrial robot deployment, where even marginal improvements in positioning accuracy can yield significant real-world benefits.
Research Focus
Key Achievements
Top Papers
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