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About

Jeong-Hwan Moon is a robotics researcher whose work focuses on the autonomous navigation, formation control, and manipulation of mobile robots. His most cited paper, "Leader-follower formation control for multiple mobile robots by a designed sliding mode controller based on kinematic control method" (2017, 12 citations), introduces a novel combination of kinematic and dynamic controllers to solve formation control problems for wheeled mobile robots, using LiDAR sensors for relative distance measurement. Moon also explores deep reinforcement learning for autonomous navigation, as seen in his 2018 paper on natural behavior learning using deep Q-networks (DQN), which enables two-wheeled robots to navigate unknown environments. Additionally, his work on robot manipulation in the ROS environment (2017, 3 citations) proposes a dual-arm grasping strategy that adapts to object shapes identified by a pan/tilt camera. With a total of 19 citations across his top papers, Moon’s contributions advance practical, sensor-driven control systems for multi-robot coordination and autonomous operation, making his research valuable for students and engineers working on intelligent robotics systems.