Jun Lee
Papers
1
Total Citations
5
H-Index
1
About
Jun Lee is an emerging researcher at the forefront of human-robot collaboration and industrial safety systems. His work centers on the critical challenge of ensuring worker safety in environments where collaborative robots — or cobots — operate alongside humans. His most notable contribution, "Real-Time Digital-Twin-Based Cobot-Worker Collision Risk Prediction Using Unity, ROS, and UWB" (2025), has already garnered 5 citations within its first year of publication, reflecting strong early interest from the robotics and industrial engineering communities. In this work, Lee developed an innovative system that integrates digital twin technology with real-time spatial tracking, leveraging Unity game engine environments, Robot Operating System (ROS), and Ultra-Wideband (UWB) positioning to dynamically predict and prevent cobot-worker collisions. This approach represents a meaningful advance in adaptive safety frameworks for smart manufacturing settings. By bridging simulation, localization, and real-time computation, Lee's research addresses one of Industry 4.0's most pressing human factors challenges. His work is particularly relevant for researchers and engineers designing next-generation collaborative workspaces where human safety and robotic efficiency must coexist seamlessly.
Research Focus
Key Achievements
Top Papers
- 1