Hyuntai Kim
Papers
1
Total Citations
20
H-Index
1
About
Hyuntai Kim has made notable contributions to the field of intelligent robotics, with a primary focus on vision-based robot tracking and autonomous navigation. His key research areas include object detection using convolutional neural networks (CNNs), real-time position estimation, and multi-robot coordination. In his highly cited 2023 work, "Efficient robot tracking system using single-image-based object detection and position estimation," Kim introduced a novel mother-slave robot tracking system that identifies a target robot, predicts its location, and enables seamless tracking using just a single image. By leveraging CNNs for robust object detection and integrating distance and angle estimation, his system achieves efficient, low-latency performance—critical for dynamic environments. With 20 citations, this work has already influenced subsequent research in vision-guided robotics and autonomous systems. Kim’s contributions stand out for their practical impact, offering a scalable solution for real-world applications such as warehouse automation and search-and-rescue missions. His work exemplifies how combining deep learning with classical robotics can yield efficient, cost-effective tracking systems, making him a promising researcher to watch in the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1