Yulong Zong

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About

Yulong Zong is a researcher advancing the frontiers of intelligent robotic inspection and industrial 3D metrology. His work centers on developing high-efficiency, high-precision automated scanning systems, with a particular focus on solving the critical challenge of geometric calibration for redundant robotic platforms. In his most cited work, "A high-efficiency and high-precision automatic 3D scanning system for industrial parts based on a scanning path planning algorithm" (2022, 37 citations), Zong introduced a novel path planning algorithm that significantly enhances both the speed and accuracy of industrial part digitization. Building on this, his 2024 paper on "Robust Geometry Self-Calibration Based on Differential Kinematics for a Redundant Robotic Inspection System" addresses a fundamental bottleneck in industrial automation: the deviation between simulated and actual viewpoint poses caused by orientation errors in complex robotic systems with external turntables. By leveraging differential kinematics for self-calibration, Zong’s method improves the reliability of automated inspection without requiring external tracking equipment. His contributions are directly applicable to high-stakes manufacturing environments where precision and repeatability are paramount, and his work continues to shape the development of more autonomous, flexible, and accurate industrial inspection systems.