Papers
1
Total Citations
2
H-Index
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About
Yinghui Hou is a researcher focused on advanced robotics and intelligent manufacturing, with key contributions in manipulator control and 3D printing precision. Their most cited work, "Manipulators 3D printing trajectory tracking control combined with RBFNNs and visual feedback" (2022), addresses a critical challenge in additive manufacturing: the inherent motion inaccuracies of tandem manipulators that compromise print quality. Hou proposed an innovative solution integrating Radial Basis Function Neural Networks (RBFNNs) with visual feedback to enhance trajectory tracking, significantly improving end-effector accuracy and product geometric fidelity. This research bridges robotics, neural network control, and real-time visual servoing, demonstrating Hou's expertise in cross-disciplinary problem-solving. While their citation count is currently modest, the work represents a foundational step toward more precise, flexible 3D printing systems using robotic arms. Hou's contributions are particularly relevant for researchers exploring automation in manufacturing, offering a pathway to overcome the limitations of traditional 3D printing platforms. Their approach highlights the potential of combining adaptive learning algorithms with sensor feedback for real-time error correction, a promising direction for future industrial robotics.
Research Focus
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Top Papers
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