Yeong-Hun Kong

Papers

Total Citations

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About

Yeong-Hun Kong is a researcher specializing in mobile robotics, reinforcement learning, and human-robot interaction. His work focuses on integrating machine learning techniques with robotic control systems to enhance autonomous navigation and task execution. Kong’s most cited paper, “Dynamic Obstacle Avoidance and Optimal Path Finding Algorithm for Mobile Robot Using Q-learning” (2017, 4 citations), addresses a critical gap in reinforcement learning research by proposing a Q-learning-based method for dynamic obstacle avoidance in real-world environments—moving beyond typical simulation-only studies. This work reflects his interest in applying reinforcement learning, inspired by AlphaGo’s success, to practical robotic challenges. In “Remote Control System for a Mobile Robot with a Robotic Arm” (2016, 2 citations), Kong developed a teleoperation system combining TCP/IP and Bluetooth communication, enabling users to remotely control a mobile robot equipped with a 5-DOF robotic arm via a wireless interface and vision sensors. His contributions advance the integration of learning algorithms and remote control in robotics, offering solutions for dynamic environments and user-friendly operation.