Jae Bok Song
Papers
7
Total Citations
160
H-Index
5
About
Jae Bok Song is a robotics researcher whose work spans mobile robot localization, motion planning, and autonomous navigation — areas that sit at the heart of modern robotics. His most recognized contribution, "Mobile Robot Localization Using Optical Flow Sensors" (2004), has garnered 52 citations, establishing him as an early contributor to vision-based localization techniques. Closely behind, his 2007 work on integrating probabilistic roadmap methods with reinforcement learning for manipulator path planning (49 citations) demonstrates a forward-thinking fusion of classical planning algorithms with machine learning — a combination that has since become increasingly mainstream. Song further advanced the field through research on range sensor-based localization and SLAM using topological information, reflecting a sustained commitment to making robots reliably aware of their environments. His work on efficient navigation in human-coexisting environments highlights a practical concern for real-world deployment, where dynamic obstacles pose significant challenges. Later contributions explore redundant robot arm control and novel mechanical designs such as jumping mechanisms, illustrating the breadth of his engineering curiosity. Collectively, Song's research portfolio reflects a career dedicated to bridging theoretical robotics with practical, deployable systems.
Research Focus
Key Achievements
Top Papers
- 1Mobile Robot Localization Using Optical Flow Sensors52 citations · 2004
- 2
- 3SLAM of a Mobile Robot using Thinning-based Topological Information23 citations · 2007
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- 7Development of jumping mechanism for a portable guard robot2 citations · 2008