Papers
3
Total Citations
30
H-Index
2
About
Delan Wei is a rising researcher in intelligent robotics and advanced manufacturing, whose work centers on enhancing the precision and autonomy of industrial manipulators through vision-based control and multi-robot coordination. His primary contributions lie in developing real-time pose estimation methods for trajectory tracking, significantly improving the motion accuracy of robots used in complex tasks like 3D printing. His most-cited paper, "A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots" (2024), has already garnered 22 citations, reflecting its immediate impact on the field. Wei also explores dual-robot cooperative path planning for multi-material additive manufacturing, a key innovation for scalable, high-mix production. Notably, his work integrating Radial Basis Function Neural Networks (RBFNNs) with visual feedback addresses the inherent accuracy limitations of tandem manipulators in 3D printing, directly tackling the trade-off between flexibility and precision. By fusing computer vision, neural networks, and robotic control, Wei is advancing the frontier of autonomous, high-accuracy robotic manufacturing systems.
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