Papers
1
Total Citations
2
H-Index
1
About
Quan Bai is a researcher whose work lies at the intersection of robotics, intelligent control, and advanced manufacturing. His primary research areas include manipulator kinematics, 3D printing automation, and the integration of neural network-based control systems with real-time visual feedback. Bai’s most cited paper, "Manipulators 3D printing trajectory tracking control combined with RBFNNs and visual feedback" (2022), addresses a critical challenge in additive manufacturing: the inherent motion inaccuracy of tandem manipulators, which compromises the geometric precision of printed objects. By proposing a novel control framework that combines Radial Basis Function Neural Networks (RBFNNs) with visual servoing, Bai significantly enhances end-effector trajectory tracking, thereby improving print quality and reliability. This work has garnered attention for its practical approach to bridging robotics and 3D printing, earning 2 citations to date. Bai’s contributions are particularly notable for advancing the use of intelligent, adaptive control in manufacturing contexts, offering a pathway toward more precise and flexible automated production systems. His research continues to influence developments in robotic additive manufacturing and vision-guided control.
Research Focus
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Top Papers
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