Kai Woon Goh

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About

Kai Woon Goh is a researcher advancing the intersection of robotics, automation, and intelligent maintenance systems. His primary research areas include fault detection in industrial machinery, sensor fusion for autonomous vehicle localization, and augmented reality (AR) applications for robot maintenance. Goh’s most significant contribution is the development of an Automated Guided Vehicle (AGV) test bed designed to simulate fault conditions and generate sensor data—a critical tool for Industry 4.0 predictive maintenance. This work, cited 6 times, addresses the challenge of modern machines still susceptible to breakdowns despite advanced sensor integration. His related study on sensor fusion for AGV localization further refines autonomous navigation accuracy. Goh also authored a comprehensive review of AR tracking methods for robot maintenance, cited 5 times, which systematically analyzes AR implementations from large-scale assets to smaller robots, highlighting practical nuances. His research bridges theoretical frameworks with real-world applications, offering students and engineers actionable insights into fault simulation, localization algorithms, and AR-assisted diagnostics. With a focus on making industrial systems more resilient and efficient, Goh’s work is foundational for those exploring smart manufacturing and autonomous robotics.