Papers
2
Total Citations
11
H-Index
2
About
Luchuan Yu is a researcher specializing in robotics, automation, and kinematic optimization, with a particular focus on high-speed industrial manipulators and multi-degree-of-freedom (MDOF) systems. Their work addresses critical challenges in trajectory planning, including the trade-offs between time efficiency, energy consumption, and motion smoothness. Yu’s most cited paper, “Multi-objective trajectory optimization of the 2-redundancy planar feeding manipulator based on pseudo-attractor and radial basis function neural network” (2023, 7 citations), introduces a novel approach combining pseudo-attractor concepts with neural networks to enhance inverse kinematic solutions—a key bottleneck in MDOF robot performance. This work demonstrates a clear impact on improving both trajectory smoothness and energy efficiency. In earlier research, Yu developed a kinematic simulation system for high-speed press line automated feeding robots (2018, 4 citations), contributing to real-time interference avoidance and production process optimization in the automotive industry. By bridging theoretical optimization with practical simulation tools, Yu’s research offers valuable insights for engineers and researchers working on advanced manufacturing automation and intelligent robotic control.
Research Focus
Key Achievements
Top Papers
- 1
- 2