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About

Ching-Yuan Chang is a pioneering researcher at the intersection of robotics, advanced sensing, and sustainable manufacturing. His work focuses on developing autonomous systems and cyber-physical frameworks that enhance industrial efficiency, safety, and environmental responsibility. Chang’s most cited paper, “Autonomous mobile robots for recycling metal shaving at CNC factories” (2023, 11 citations), introduces a novel application of mobile robotics to waste management, directly addressing sustainability challenges in machining industries. He has also made significant contributions to structural health monitoring, as demonstrated in his 2022 study using fiber Bragg grating sensors and cyber-physical systems to monitor linear robot integrity (6 citations). Chang’s research on robot arm path planning with adaptive obstacle avoidance (2023, 5 citations) advances human-robot collaboration by enabling safer, more flexible factory environments. More recently, he has explored non-contact measurement techniques, such as stereo digital image correlation for delta robot displacement analysis (2024, 3 citations), and applied deep learning (YOLO-v7) to activity detection in robotic dogs using dynamic strain sensors (2025, 2 citations). Through these innovations, Chang is shaping the future of intelligent, eco-friendly automation.