Kuan Tan

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About

Kuan Tan’s research lies at the intersection of robotics and machine vision, with a focus on enhancing industrial automation through intelligent, vision-guided systems. Their most cited work, “Integrating Vision System to a Pick and Place Cartesian Robot” (2021), addresses a critical challenge in automated sorting: the failure of robots to accurately identify and handle objects due to limitations in visual feedback. By developing a machine vision algorithm that enables real-time communication between a camera system and a Cartesian robot, Tan’s contribution improves object sorting reliability and efficiency—a key step toward smarter, more adaptive manufacturing processes. With 3 citations, this paper has already influenced early-stage work in vision-based robotics. Tan’s research is particularly valuable for students and engineers exploring how computer vision can be seamlessly integrated into traditional robotic platforms to solve practical, real-world problems. Their work underscores the growing importance of combining perception and actuation in modern automation, offering a foundation for future innovations in pick-and-place systems, quality control, and collaborative robotics.