Shan Xue
Papers
4
Total Citations
30
H-Index
3
About
Shan Xue is a rising researcher in robotics and neural network optimization, with a focus on motion planning for redundant and dual-arm robot manipulators. Their work centers on developing advanced zeroing neural network (ZNN) models to solve time-dependent problems, including equality-constrained quadratic programs and linear equations with boundary constraints. A key contribution is the harmonic noise rejection ZNN, which enhances robustness in robot arm control by filtering disturbances—a paper that has already garnered 20 citations since 2024. Xue has also pioneered acceleration-level repetitive motion planning schemes, moving beyond traditional velocity-level approaches to improve precision in redundant manipulators. Their research extends to synchronous motion planning for dual-arm robot systems, addressing joint constraints critical for real-world engineering applications. With a growing citation count across their publications, Xue’s work is gaining recognition for bridging theoretical neural network solutions with practical robotic implementations. Their achievements demonstrate a strong potential to influence future developments in autonomous robotics and real-time optimization.
Research Focus
Key Achievements
Top Papers
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