Cenglin Yao
Papers
1
Total Citations
15
H-Index
1
About
Cenglin Yao is a researcher focused on advancing industrial automation through intelligent optimization and robotics. His primary research areas include industrial process parameter control, evolutionary algorithms, and robotic trajectory planning. Yao’s major contribution lies in developing improved genetic algorithms to optimize complex manufacturing processes, as demonstrated in his most-cited work on industrial robot polishing parameter optimization. By integrating cubic B-spline curve fitting for joint trajectory smoothing, his approach enhances precision and efficiency in real-world industrial settings—a critical need for modern smart factories. His work has garnered 15 citations, reflecting its practical relevance to both academia and industry. Notably, Yao’s research bridges the gap between theoretical algorithm design and applied robotics, offering scalable solutions for adaptive control in manufacturing. His findings provide a foundation for further exploration into AI-driven process optimization, making his contributions valuable for students and engineers seeking to improve automation reliability and performance.
Research Focus
Key Achievements
Top Papers
- 1