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About

Dang Thai Son is a robotics researcher whose work spans indoor navigation, adaptive control, and human-robot interaction. His most-cited paper, “The Practice of Mapping-based Navigation System for Indoor Robot with RPLIDAR and Raspberry Pi” (2021, 10 citations), presents a low-cost, prototyped mapping mobile robot integrating Raspberry Pi, RPLIDAR A1, and SLAM algorithms—a practical contribution to accessible autonomous navigation. In “Adaptive Terminal Sliding Mode Control Using RBF Neural Network for Industrial Robot Manipulators” (2025, 5 citations), he advances robust control theory by combining sliding mode control with neural networks for precise manipulator performance. Earlier work, “Gait of Quadruped Robot and Interaction Based on Gesture Recognition” (2015, 3 citations), explores legged locomotion and intuitive human-robot interfaces. Son’s impact lies in bridging theoretical control methods with real-world, cost-effective implementations, making robotics more accessible for education and industry. His research demonstrates a commitment to solving practical challenges in mapping, manipulation, and interaction, with potential applications in service robots and manufacturing automation.