Papers
2
Total Citations
188
H-Index
2
About
Dr. Dayeon Kim is a leading researcher in industrial robotics, with a primary focus on enhancing safety and autonomy in human-robot collaborative environments. Her most impactful work, the 2019 paper "Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach," has garnered 180 citations, establishing a novel framework that leverages deep learning for reliable, real-time collision monitoring. This contribution is pivotal for advancing safe human-robot interaction in manufacturing, moving beyond traditional observer-based methods. Dr. Kim also explores practical robotic applications, as demonstrated in her 2017 work on an autonomous table tennis ball collecting robot, which integrates ball detection, navigation, and collection systems. This project highlights her versatility in system design and real-world deployment. Through her research, Dr. Kim is shaping the future of collaborative robotics, making industrial settings safer and more efficient, while her innovative approaches continue to influence both academic and applied robotics fields.
Research Focus
Key Achievements
Top Papers
- 1
- 2System design for autonomous table tennis ball collecting robot8 citations · 2017