Rentao Xiong

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About

Rentao Xiong is a leading researcher in intelligent robotic manufacturing, with a primary focus on automated deburring processes and the integration of machine vision with robotic systems. His work addresses critical challenges in industrial automation, particularly the precise removal of burrs from metal castings—a common defect that compromises machining accuracy and assembly precision. Xiong’s major contributions include developing an innovative robot offline programming system that integrates visual information to enhance the automatic deburring process, significantly reducing reliance on manual teaching. His 2018 paper on this topic has garnered 18 citations, reflecting its impact on advancing efficient, vision-guided robotic solutions. Additionally, his research on local deformable template matching for robotic deburring (6 citations) tackles the problem of casting deformation, enabling robots to adapt to workpiece variations in real time. By combining computer vision with adaptive control, Xiong’s work improves both the speed and accuracy of industrial deburring, offering practical pathways for smarter, more autonomous manufacturing systems. His contributions are particularly valuable for researchers and engineers seeking to bridge the gap between offline programming and real-world production variability.