Papers
9
Total Citations
368
H-Index
7
About
Congcong Ye is a leading researcher in the field of robotic manufacturing and precision motion control, with expertise spanning kinematic calibration, robotic milling, stiffness modeling, and intelligent path planning. His most influential contributions address critical challenges in improving the accuracy and efficiency of 6R serial industrial robots. Notably, his 2020 work on C3 continuous corner smoothing algorithms (85 citations) established a rigorous analytical framework for fluid, high-precision tool path generation, while his research on stiffness deformation prediction and compensation (76 citations) offers practical solutions for counteracting compliance-induced errors in industrial settings. Ye has also made significant strides in end-effector pose optimization and workpiece placement strategies that directly minimize contour errors during robotic milling operations, with both studies garnering over 69 citations collectively by 2021. His earlier foundational work in kinematic calibration using DH models and telescoping ballbar techniques laid the groundwork for his later advances. More recently, Ye has expanded into machine learning applications, exploring Gaussian mixture regression for robot learning from demonstration and domain adaptation for modal analysis. Across his career, his cumulative citation impact underscores his growing influence on bridging theoretical robotics with real-world manufacturing precision.
Research Focus
Key Achievements
Top Papers
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- 5Bagging for Gaussian mixture regression in robot learning from demonstration32 citations · 2020
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