Shao-Chun Lee

National Taiwan University of Science and Technology

Papers

Total Citations

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About

Shao-Chun Lee is a pioneering researcher in autonomous manufacturing inspection, whose work bridges the critical gap between automated quality control and practical industrial deployment. His research centers on developing intelligent, camera-guided robotic systems that can perform precise visual inspections without requiring expert human operators. Lee’s foundational 2018 paper on autonomous robot-guided inspection systems, which has garnered 14 citations, introduced an innovative approach combining offline programming with RGB-D modeling to create flexible, reprogrammable inspection solutions. He further advanced the field with his 2021 study on camera-based position correction systems, demonstrating how autonomous production lines can achieve remarkable accuracy through real-time visual feedback and calibration. These contributions are particularly significant for modern manufacturing, where traditional automatic optical inspection (AOI) systems remain time-consuming and dependent on skilled operators. Lee’s work directly addresses this bottleneck by enabling non-experts to deploy sophisticated inspection protocols, potentially revolutionizing quality control in smart factories. His research represents a crucial step toward fully autonomous production environments, where machines can self-correct and maintain quality standards without human intervention.