Navid Khazaee Korghond

Papers

Total Citations

H-Index

About

Navid Khazaee Korghond is a robotics researcher whose work centers on human-robot interaction, mobile robot platforms, and attention control systems. His most-cited paper, “Skeleton and visual tracking fusion for human following task of service robots” (12 citations), introduces a novel method that overcomes the limitations of standard skeleton trackers by fusing depth-based skeleton data with visual tracking. This approach was validated on his team’s service robot, Sepanta, and significantly improves a robot’s ability to reliably follow a human in dynamic environments. Khazaee Korghond also made key contributions to accessible robotics hardware with his work on the ReMoRo platform (5 citations), a mobile robot based on distributed I/O modules designed for research and education. Over three generations, this platform evolved to become more compatible and practical for diverse applications. Additionally, his research on top-down attention control (2 citations) addresses the challenge of sensory data overload by implementing a selective attention model within the Device Communication Manager layer of an omnidirectional robot. Together, these contributions highlight Khazaee Korghond’s focus on making service robots more perceptive, responsive, and practical for real-world tasks.