Jingwen Yan

Papers

Total Citations

H-Index

About

Jingwen Yan is a leading researcher in robotics and intelligent control systems, with a primary focus on motion planning, kinematic control, and logistics automation. Her work bridges theoretical algorithm development and practical robotic applications, particularly in manufacturing and industrial environments. Yan’s most cited paper, “Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm” (2023), has garnered 51 citations, introducing a novel optimization approach that enhances efficiency in factory material distribution systems. Another influential contribution, “Noise-Suppressing Newton Algorithm for Kinematic Control of Robots” (2019), with 23 citations, presents a discrete-time noise-suppressing Newton algorithm that significantly improves redundancy resolution for robot manipulators under real-world noise conditions. This work demonstrates her expertise in integrating integral control methods with Newton-based optimization to achieve robust, hardware-implementable solutions. Yan’s research is highly impactful for students and engineers working on autonomous navigation, robot arm control, and smart manufacturing, offering both rigorous mathematical frameworks and deployable algorithms. Her achievements highlight a commitment to advancing robotic systems that are both theoretically sound and practically viable for modern industrial challenges.