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About

Hung-Wei Lin is a leading researcher in intelligent robotics and autonomous systems, with a focus on deep learning, multi-robot coordination, and assistive technologies. His work bridges the gap between advanced algorithms and practical robotic applications, particularly through the integration of the Robot Operating System (ROS). Lin’s most cited paper, "Deep learning for object identification in ROS-based mobile robots" (2018, 29 citations), demonstrates how Faster R-CNN can be deployed on low-cost platforms like Raspberry Pi for real-time object detection, enabling accessible robotic intelligence. He has made significant contributions to multi-robot systems, including adaptive distributed fault-tolerant formation control (2018, 17 citations) and adaptive neuro-fuzzy formation control (2013, 13 citations), which enhance robustness and coordination under partial actuator failures. Lin also addresses societal challenges through his work on a ROS-based smart walker with fuzzy posture judgment and power assistance (2021, 14 citations), designed to support elderly mobility and prevent falls. His recent research on a smart machine box for predictive maintenance (2023) extends his expertise to industrial applications, using data-driven analytics to optimize equipment reliability. With over 75 citations across his top papers, Lin’s work exemplifies how robotics can improve both industrial efficiency and quality of life.