Seonil Lee
Papers
2
Total Citations
33
H-Index
2
About
Seonil Lee is a rising roboticist whose research focuses on autonomous navigation and motion planning for mobile robots operating in cluttered, real-world environments. His work addresses two critical challenges: safe exploration for ground robots and stable trajectory generation for aerial robots carrying bulky payloads. In his highly cited 2022 paper, “Autonomous Exploration in a Cluttered Environment for a Mobile Robot With 2D-Map Segmentation and Object Detection” (28 citations), Lee introduced a frontier-based exploration method enhanced by 2D-map segmentation and object detection, enabling robots to navigate unknown spaces while avoiding three-dimensional obstacles and reducing exploration time. Complementing this, his 2022 work on “Trajectory Generation of a Quadrotor Transporting a Bulky Payload in the Cluttered Environments” (5 citations) tackles the wavering effect caused by large payload inertia, proposing a path-planning algorithm that allows aerial robots to safely traverse narrow passages. Together, these contributions demonstrate Lee’s commitment to bridging perception and control for practical robotics. His research is particularly valuable for applications in search-and-rescue, logistics, and industrial inspection, where robots must operate reliably amidst obstacles. With a growing citation record and a focus on solving tangible problems, Seonil Lee is establishing himself as a key contributor to the future of autonomous mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2