Yunting Wang

National Cheng Kung University, Guangzhou University

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About

Yunting Wang is a researcher whose work lies at the intersection of robotics, autonomous navigation, and intelligent control systems. Their early contributions include pioneering the use of LIDAR-based scan matching for indoor localization, a method that enables efficient robot navigation with minimal sensor load—a foundational approach that has garnered 11 citations and influenced subsequent indoor robotics research. More recently, Wang has advanced the field of autonomous underwater vehicle (AUV) recycling by developing a novel anti-interference tracking method that integrates deep learning with a Cubature Kalman Filter (CKF), achieving robust visual tracking under harsh sea conditions (7 citations). This work addresses critical challenges in marine resource exploitation and AUV recovery. Wang has also contributed to industrial automation through the design of a long-arm heavy-duty fully automatic loading robot, which optimizes mechanical transmission and electronic control to reduce logistics costs and improve handling efficiency. Across these projects, Wang demonstrates a consistent focus on solving real-world deployment challenges—whether in cluttered indoor spaces, turbulent underwater environments, or demanding warehouse settings—making their research highly relevant for students and engineers working on practical robotic systems.