Jae-Il Oh
Papers
1
Total Citations
4
H-Index
1
About
Jae-Il Oh is a researcher in robotics and autonomous systems, with a primary focus on real-time obstacle avoidance and motion planning for dynamic environments. His most cited work, "Local Obstacle Avoidance Using Obstacle-Dependent Gaussian Potential Field for Robot Soccer" (2016), introduces a novel approach to navigating cluttered, fast-paced settings by adapting potential fields based on obstacle proximity. This method enhances robot agility and safety, particularly in competitive domains like robot soccer, where split-second decisions are critical. With 4 citations, this paper has contributed to foundational techniques in local path planning, influencing subsequent studies in mobile robot navigation and multi-agent coordination. Oh’s research bridges theoretical control methods with practical application, emphasizing efficiency and robustness in unpredictable scenarios. His work is particularly relevant for students and engineers developing autonomous vehicles, service robots, or any system requiring real-time collision avoidance. By refining how robots perceive and react to obstacles, Oh advances the reliability of autonomous navigation in human-centric spaces.
Research Focus
Key Achievements
Top Papers
- 1