Chang Yu
Papers
3
Total Citations
42
H-Index
3
About
Chang Yu is a rising researcher in robotics and intelligent control systems, with a focus on enhancing robot collaboration, motion planning, and adaptive control. Their most cited work, “Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task” (2024, 27 citations), introduces a novel pattern recognition strategy that uses k-means clustering-enhanced Support Vector Machines to rapidly classify robots into flying or mobile categories during curve negotiation—a key step for coordinated multi-robot tasks. Yu’s second major contribution, “Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPC” (2024, 11 citations), advances robust adaptive control by integrating deep model predictive control to handle modeling uncertainties in high-degree-of-freedom manipulators, offering flexibility and precision in dynamic environments. With over 40 total citations in a short span, Yu’s work bridges machine learning and control theory, providing scalable, real-time solutions for complex robotic systems. Their research is particularly impactful for autonomous navigation, industrial automation, and human-robot collaboration, positioning them as a promising voice in next-generation robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3