Papers
5
Total Citations
33
H-Index
4
About
Jia Liu is a robotics and autonomous systems researcher whose work spans mobile robot olfaction, path planning, and human-autonomous teaming. Liu's most prominent contributions lie in the challenging domain of robotic odor-source localization, where he has developed and refined algorithms enabling mobile robots to detect, map, and navigate toward multiple odor sources in complex, real-world environments characterized by time-varying airflow and physical obstacles. Central to this body of work is his innovative application of Dempster-Shafer (D-S) inference theory, a probabilistic reasoning framework that Liu has adapted to handle the inherent uncertainty of dynamic airflow conditions — work that has accumulated over 25 citations across multiple publications from 2014 to 2016. His research progressed systematically from foundational path-planning methods to multi-source outdoor mapping experiments, demonstrating both theoretical rigor and practical implementation. More recently, Liu has expanded his research focus into human-autonomous teaming systems, contributing a novel framework for modeling real-time human trust values — a critical challenge in human-robot collaboration that addresses the nuanced problem of trust miscalibration. Together, these contributions position Liu as a versatile researcher bridging autonomous navigation, sensor-based reasoning, and human-machine interaction.
Research Focus
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