Papers
2
Total Citations
13
H-Index
2
About
Laizhen Luo is a rising researcher in the field of intelligent robotic manufacturing, with a primary focus on robotic grinding, path planning, and the kinematics of complex robotic systems. His work addresses critical challenges in automating the finishing of complicated curved surfaces, a task essential for high-precision industries like aerospace and automotive manufacturing. In his most-cited paper, "Pose Optimization and Tool Path Planning for Robotic Grinding of Complicated Curved Surface" (2022, 10 citations), Luo introduced a novel method that constrains tool attitude changes and smoothly interpolates poses at interference points. This approach significantly improves path smoothness and prevents tool-workpiece collisions, directly enhancing final machining accuracy. His subsequent work, "Inverse Kinematics Solution Based on Redundancy Modeling and Desired Behaviors Optimization for Dual Mobile Manipulators" (2023, 3 citations), extends his expertise to multi-robot coordination, optimizing inverse kinematics for dual-arm systems. Though early in his career, Luo’s contributions are already cited for their practical impact on robotic surface finishing, demonstrating a clear trajectory toward solving real-world manufacturing problems with elegant, computationally efficient solutions.
Research Focus
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Top Papers
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