Dong Hwan Kim
Papers
7
Total Citations
31
H-Index
3
About
Dong Hwan Kim is a robotics researcher whose work spans industrial automation, field robotics, and intelligent perception systems. His core research areas include deep learning-based object recognition, robot kinematics, and autonomous navigation for specialized mobile robots. Kim’s most impactful contribution is a deep learning system for mechanical parts picking that uses YOLOv3 to recognize bolts and nuts and extract their geometric properties—a solution directly applicable to industrial automation (7 citations). He also developed a jumping robot capable of rolling and leaping over obstacles for environmental patrol in harsh terrains (6 citations), and extended inverse kinematic solutions to handle joint angle constraints in robotic manipulators (6 citations). More recently, Kim has focused on solar panel cleaning robots, designing a walking-type mechanism driven by triple driving lines with vacuum pads, and a localization system combining vision processing with extended Kalman filters for precise navigation on panel surfaces (3 citations each). His work on 3D object detection using DBSCAN and YOLOv5, adapted for mobile platforms, and omni-directional robot tracking systems further demonstrates his versatility. With over 28 citations across his top papers, Kim’s research is advancing practical, real-world robotics applications from factory floors to renewable energy maintenance.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Mechanism and control of a jumping robot6 citations · 2007
- 4
- 5
- 6
- 7