Jae Sik Jeong

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About

Jae Sik Jeong is a researcher specializing in construction robotics and computer vision, with a particular focus on automated assembly systems for steel structures. His work addresses the critical challenge of enabling robots to reliably detect and align bolt holes under varying environmental conditions, a key bottleneck in construction automation. Jeong’s most cited paper, “Cognition System of Bolt Hole Using Template Matching” (2011), proposes a vision-based approach that leverages template matching algorithms to guide bolting robots in real-time, even when lighting conditions are suboptimal. This contribution has garnered 5 citations, reflecting its foundational role in the niche but growing field of construction robotics. By improving the accuracy and robustness of robotic hole detection, Jeong’s research helps reduce manual labor, enhance safety, and accelerate assembly times on construction sites. His work is particularly notable for addressing practical, real-world constraints—such as glare or shadow—that often undermine computer vision systems in outdoor or industrial settings. Jeong’s contributions are valuable for researchers and engineers developing autonomous systems for heavy construction, offering a stepping stone toward fully automated steel frame assembly.