Dong-Eon Kim
Papers
14
Total Citations
139
H-Index
7
About
Dong-Eon Kim is a leading researcher in intelligent robotics and autonomous systems, with a primary focus on robotic manipulation, grasping control, and autonomous navigation. His work integrates advanced artificial intelligence techniques—including deep neural networks, reinforcement learning, and fuzzy logic—to solve critical challenges in real-world robotics. Kim’s most influential contribution is the development of an artificial intelligence-based optimal grasping control system, which introduced a novel tactile sensing module using air pressure sensors to detect contact force and location on robot fingers. This work, cited over 20 times, has advanced the precision and safety of robotic object handling. He also pioneered a deep learning-based smooth driving method for autonomous navigation using LiDAR and Deep Q-Networks, earning 11 citations for enabling more stable mobile robot movement in unknown environments. With over 130 total citations across his top ten papers, Kim has demonstrated sustained impact in areas such as sliding mode control with fuzzy rules, time delay compensation for remote robotic systems, and consensus formation control for multi-robot teams. His research is particularly notable for bridging theoretical control methods with practical, sensor-driven implementations, making his work highly relevant for students and engineers developing next-generation service and industrial robots.
Research Focus
Key Achievements
Top Papers
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- 3Artificial Intelligence-Based Optimal Grasping Control20 citations · 2020
- 4Deep Learning Based on Smooth Driving for Autonomous Navigation11 citations · 2018
- 5Stable Robotic Grasping of Multiple Objects using Deep Neural Networks10 citations · 2020
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- 10Consensus formation control of multiple wheeled mobile robots5 citations · 2017