Shao-Wei Yan

National Taiwan University of Science and Technology

Papers

Total Citations

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About

Shao-Wei Yan is a leading researcher in autonomous mobile robotics, with a primary focus on navigation systems for industrial environments. His work addresses critical challenges in autonomous guided vehicle (AGV) trajectory tracking, particularly in sparse LiDAR feature settings where traditional algorithms struggle. Yan’s most cited paper, "Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments" (2021, 38 citations), provides a rigorous analysis of the Pure Pursuit algorithm—a widely used trajectory-tracking method praised for its simplicity and ease of implementation. By identifying and addressing the algorithm’s limitations in low-feature industrial spaces, Yan has advanced the reliability and efficiency of AGVs in real-world manufacturing and logistics applications. His contributions bridge the gap between theoretical control algorithms and practical deployment, making autonomous navigation more robust in challenging environments. Yan’s work is particularly valuable for researchers and engineers developing cost-effective, scalable solutions for warehouse automation and factory floor robotics. With growing citation impact, he continues to shape the future of intelligent mobility in industry.