Zilian Yi
Papers
4
Total Citations
22
H-Index
3
About
Zilian Yi is a robotics researcher specializing in motion planning and control for redundant manipulators, with a focus on real-time obstacle avoidance and joint-limit management. Their work centers on developing neural network-based and quadratic programming (QP) solutions to address critical challenges in robotic safety and precision during cyclical tasks. Yi’s most impactful contribution is a position-level repetitive motion planning (RMP) scheme that simultaneously handles obstacle avoidance (OA) and joint-limit avoidance (JLA) for joint-constrained robots, published in 2024 with 15 citations. They have also advanced non-iterative neural controllers for OA-JLA in redundant manipulators and introduced a mode-switcher-based neural solution for linearly constrained systems. Additionally, Yi has explored spiking cerebellar models to enhance gradient neural networks for time-varying linear equations, with applications in robot motion planning. Their work bridges theoretical neural dynamics and practical robotic control, offering computationally efficient, constraint-aware solutions that improve the reliability of autonomous systems in industrial and service robotics.
Research Focus
Key Achievements
Top Papers
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