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About

Jaeho Kim is a robotics researcher whose work spans autonomous navigation, human-robot interaction, and intelligent aerial systems. His most cited paper, “Analysis of dynamic cluster head selection for mission-oriented flying Ad hoc network” (2017, 15 citations), addresses critical challenges in Flying Ad Hoc Networks (FANETs) for unmanned aerial vehicles, proposing dynamic cluster head selection to enhance mission-oriented communication. Kim has also advanced autonomous mobile robotics with an environment-adaptive object detection framework (2022, 5 citations) that leverages deep learning for robust performance across varying conditions. In simultaneous localization and mapping (SLAM), his LiDAR range image descriptor (RID, 2023, 3 citations) enables fast and efficient loop closure detection in indistinguishable environments, a key contribution to reliable robot localization. More recently, Kim has explored intuitive interfaces, including a camera-based virtual drone control system using two-handed gestures (2024, 3 citations) and a context-aware command feedback system for minimizing control errors in human-robot interaction (2025, 1 citation). His work on a meta-human synchronization framework for large-scale digital twins (2023, 2 citations) extends digital twin concepts to human modeling. With over 29 citations across his most influential works, Kim is shaping the future of autonomous systems and seamless human-robot collaboration.