Yu-Sying Guo

National Chin-Yi University of Technology

Papers

Total Citations

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About

Yu-Sying Guo is a researcher at the forefront of robotics and automation, with a particular focus on enhancing the adaptability and precision of robotic systems. His most-cited work, "An image vision and automatic calibration system for universal robots" (2019), has garnered 9 citations and addresses a critical challenge in modern manufacturing: the need for flexible, cost-effective automation. Guo's major contribution lies in developing a vision-based calibration system that enables universal robotic arms to autonomously adjust to varying tasks without extensive manual reprogramming. This innovation directly supports the shift toward smart factories, where robots must operate with high accuracy in dynamic environments. By integrating computer vision with automatic calibration, Guo's research reduces setup time and improves operational efficiency, making advanced robotics more accessible to small and medium-sized enterprises. His work exemplifies the practical application of AI and sensor technology in industrial settings, bridging the gap between theoretical robotics and real-world deployment. For students and researchers exploring automation, Guo's contributions offer a clear pathway to understanding how vision systems can transform traditional manufacturing into agile, intelligent production lines.