Yong Liang Guan
Papers
4
Total Citations
47
H-Index
4
About
Yong Liang Guan is a robotics researcher specializing in intelligent autonomous systems, with key contributions in assembly automation, mobile robot localization, and collaborative SLAM. His work bridges the gap between learning-based optimization and real-world robotic applications, particularly in GPS-denied environments. Guan’s most cited paper (25 citations) introduces a learning-based optimization algorithm for robotic dual peg-in-hole assembly, combining impedance control to reduce assembly time and smooth contact forces with minimal experiments—a significant advance for industrial automation. He has also pioneered low-cost LTE-based localization for mobile robots in urban canyons (8 citations), offering a robust alternative to GPS. His collaborative radio SLAM framework (8 citations) leverages WiFi fingerprint similarity for multi-robot mapping in large-scale environments, addressing efficiency and scalability challenges. Additionally, Guan’s two-stage vSLAM loop closure detection (6 citations) employs sequence node matching and semi-semantic autoencoders to improve visual SLAM accuracy. With a growing citation record and a focus on practical, cost-effective solutions, Guan’s research is shaping the future of autonomous navigation and robotic manipulation in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
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