Supattra Plermkamon

Papers

Total Citations

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About

Supattra Plermkamon’s research lies at the intersection of robotics, computer vision, and intelligent control systems, with a focus on enabling robots to track and grasp dynamic objects in real time. Her major contributions center on developing adaptive linear robot control systems that integrate visual feedback for automated manufacturing tasks. In her most-cited works from 2003, she proposed a system capable of visually tracking and intercepting both stationary and moving objects undergoing arbitrary motion within a robot’s workspace. By generating optimum-tracking trajectories, her approach allows robots to grasp objects on conveyors or in unpredictable paths, significantly enhancing automation in complex industrial environments. Though her citation counts are modest—each paper garnering 2 citations—her work represents foundational steps in adaptive robotic control for dynamic environments. Plermkamon’s research demonstrates the practical integration of vision and control systems, offering a blueprint for intelligent robotic work cells that can adapt to real-world variability. Her contributions are particularly relevant for students and researchers exploring vision-guided robotics, real-time tracking algorithms, and automation in manufacturing.