Papers
6
Total Citations
150
H-Index
4
About
Heungju Ahn is a leading researcher at the intersection of artificial intelligence, cognitive robotics, and autonomous navigation. His work is defined by a unique dual focus: advancing sustainable AI through cognitive learning models and developing practical, collision-free navigation systems for non-holonomic robots. Ahn’s most influential contribution is his 2016 paper, “Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence” (68 citations), which conceptually establishes a direct-autonomous learning interaction model to ensure AI remains beneficial to humanity. In robotics, his 2022 study on “Improved Analytic Expansions in Hybrid A-Star Path Planning” (43 citations) provides a concise yet powerful enhancement to motion planning for differential drive vehicles. Ahn further demonstrates impact through his cognitive robotic system for human-following tasks (26 citations), integrating the Soar cognitive architecture with obstacle avoidance to enable reliable human-robot collaboration. His recent work on real-time human movement recognition using ultra-wideband sensors (2024) extends his expertise into sensor-based perception. Across his portfolio, Ahn consistently bridges theoretical frameworks with real-world robotic applications, making him a notable figure in sustainable AI and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 5A Cognitive Robotic System for a Human-Following Robot4 citations · 2020
- 6Real-Time Human Movement Recognition Using Ultra-Wideband Sensors3 citations · 2024