Thana Radpukdee

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About

Thana Radpukdee is a robotics researcher whose work lies at the intersection of trajectory optimization and metaheuristic algorithms, with a particular focus on six-degree-of-freedom (6-DOF) robotic manipulators. Her most significant contribution, the 2019 paper "Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning" (27 citations), introduces a novel hybrid algorithm that combines a self-adaptive mechanism with differential evolution to solve complex multiobjective path planning problems. This work addresses the critical challenge of simultaneously minimizing travel time and energy consumption in industrial robot motion. In her foundational 2018 study (7 citations), Radpukdee systematically evaluated multiple established metaheuristics for 6-DOF trajectory planning, using fourth-order polynomial functions to represent smooth motion paths. Her research demonstrates how computational intelligence can enhance robotic efficiency, offering practical solutions for manufacturing and automation industries. By developing adaptive optimization frameworks that balance competing objectives, Radpukdee has contributed to making robotic systems more autonomous and energy-efficient, with her citation record reflecting growing interest in self-tuning algorithms for real-world robotic applications.