Sunghyun Nam
Papers
1
Total Citations
3
H-Index
1
About
Sunghyun Nam is a robotics researcher whose work focuses on improving autonomous navigation in structured indoor environments. His primary research area is robot path planning, with a particular emphasis on developing efficient algorithms for corridor and hallway settings. Nam's major contribution is the introduction of Directionally-Exploring Random Trees (DERT), a novel enhancement to the widely-used Rapidly-exploring Random Tree (RRT) algorithm. He identified that standard RRT methods perform inefficiently in corridor environments, where space is elongated in one direction, leading to excessive sampling and unstable paths biased toward walls. By redefining sampling regions and incorporating directional guidance into random node generation, Nam's approach produces more stable, centered paths with significantly fewer samples. His most-cited paper, "Directionally-Exploring Random Trees for Efficient Robot Path Planning in Corridor Environments" (2022), has garnered 3 citations and represents a targeted solution to a practical problem in mobile robotics. This work demonstrates Nam's ability to identify real-world inefficiencies in established algorithms and propose mathematically grounded improvements, making his research valuable for students and engineers working on autonomous navigation in constrained spaces.
Research Focus
Key Achievements
Top Papers
- 1