Di Kang

Papers

Total Citations

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About

Di Kang is a researcher whose work bridges robotics, computer vision, and precision manufacturing. His key research areas include visual sensing systems, robotic assembly, and calibration methodologies for complex optical setups. His most notable contribution is a self-calibration method for pyramidal mirror systems used in robotic assembly, which systematically reduces measurement errors by adjusting mirror positions and calibrating the visual sensing system. This work, published in 2005, has garnered 7 citations and remains relevant for improving accuracy in automated assembly tasks. Kang’s approach addresses a critical challenge in industrial robotics: enhancing the precision of multi-mirror visual systems without requiring external references. His research has implications for quality control and automation in manufacturing, where even minor errors can lead to costly defects. While his citation count is modest, his focus on practical, cost-effective calibration solutions underscores his commitment to advancing real-world robotic applications. For students and researchers exploring sensor fusion or optical metrology, Kang’s work offers a foundational method for integrating mirrors into robotic vision systems.