Chan Ham

Kennesaw State University, University of Central Florida

Papers

Total Citations

H-Index

About

Chan Ham is a robotics and automation researcher whose work bridges industrial manufacturing, biomedical systems, and intelligent control. His key research areas include mechatronics, Industry 4.0 integration, computer vision, and the Internet of Things (IoT). Ham’s most cited work, “Development of an Autonomous Ball-Picking Robot” (2016, 7 citations), demonstrates his hands-on approach to engineering education by showcasing a one-semester senior design project that transformed a 6-axis industrial robot into an autonomous system. He further advanced manufacturing automation in “Computer Vision and Machine Learning to Create an Advanced Pick-and-Place Robotic Operation Using Industry 4.0 Trends” (2022, 5 citations), which integrates vision systems and machine learning into a Kawasaki robot and Vanderlande execution system. Ham also explores interdisciplinary applications, such as in “Advanced Biomedical Laboratory (ABL) Synergy With Communication, Robotics, and IoT” (2023), highlighting low-cost IoT devices for medical and manufacturing settings. His earlier work on learning control for robot manipulators with actuator dynamics (2002) laid theoretical foundations for stable robotic tracking. With a career spanning foundational control theory to modern Industry 4.0 implementations, Ham’s contributions are valuable for students and researchers interested in practical, integrated robotics solutions.