Yuliang Guo
Papers
2
Total Citations
12
H-Index
2
About
Yuliang Guo is a researcher advancing the frontier of smart manufacturing through innovative work in industrial robotics, with a primary focus on multi-objective trajectory planning and dynamic system identification. His research addresses the critical challenge of simultaneously improving robot control accuracy, energy efficiency, and operational speed—a triad of competing objectives essential for modern automated production lines. Guo’s most cited work, "A Novel Resolution Scheme of Time-Energy Optimal Trajectory for Precise Acceleration Controlled Industrial Robot Using Neural Networks" (2022, 8 citations), introduces a neural network-based approach to resolve the complex trade-off between time and energy consumption in robot motion. In a complementary study (2022, 4 citations), he developed a multi-objective trajectory optimization method that leverages reliable dynamic identification to enhance control accuracy for customized robots. By integrating improved dynamic matching torques with optimal motion planning, Guo’s contributions provide a practical framework for boosting both productivity and energy efficiency in industrial settings. His work is particularly valuable for engineers and researchers seeking to deploy high-performance, cost-effective robotic systems in smart manufacturing environments.
Research Focus
Key Achievements
Top Papers
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