Max Robotics — Global Service Robot Directory + US Certification

About

Tae-Young Kuc is a leading researcher in robotics and intelligent control systems, with a focus on mobile robot navigation, sensor fusion, and adaptive learning. His pioneering work includes the development of a simple ultrasonic GPS system for indoor mobile robots, which employs Kalman filtering to overcome the limitations of conventional multi-transmitter setups, achieving 16 citations and laying groundwork for efficient localization. Kuc has also advanced iterative learning control by integrating the cerebellar model articulation controller (CMAC) with gradient descent algorithms, enabling robot manipulators to refine torque sequences over repeated tasks—a contribution cited 11 times. More recently, his 2020 study on multi-floor navigation for mobile service robots, cited 10 times, addresses the challenging task of visual detection and recognition of elevator features, facilitating autonomous floor transitions. Additionally, his work on adaptive learning for teleoperated robotic motion, though with 2 citations, explores inverse dynamics to enhance trajectory tracking in bilateral control systems. Kuc’s research, spanning from sensor-based localization to machine learning-driven control, has significantly impacted indoor robotics, offering practical solutions for real-world deployment. His achievements underscore a career dedicated to bridging theoretical control methods with applied robotic systems, making him a notable figure in the field.