Ji-Hun Gwak
Papers
1
Total Citations
2
H-Index
1
About
Dr. Ji-Hun Gwak is a robotics researcher whose work centers on distributed intelligence, multi-robot coordination, and real-time environmental perception. His most notable contribution, the 2024 paper "Distributed Deep Learning for Real-World Implicit Mapping in Multi-Robot Systems," introduces a novel framework that enables teams of robots to collaboratively build environmental maps using only 2D LiDAR data. In this system, each robot independently collects distance measurements to construct a local map, then shares and integrates this information with its peers through wireless communication—allowing for scalable, decentralized navigation without a central server. This approach addresses critical challenges in real-world deployment, such as communication bandwidth limits and dynamic environments. While his citation count is still growing, Dr. Gwak’s work represents a significant step toward practical, distributed autonomy in applications like search-and-rescue, warehouse logistics, and environmental monitoring. His research bridges deep learning, swarm robotics, and sensor fusion, offering a blueprint for how multiple agents can learn and map their surroundings in real time. For students and researchers, his work exemplifies the shift toward resilient, scalable robotic systems that operate without constant human oversight.
Research Focus
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Top Papers
- 1