Xianting Li
Papers
5
Total Citations
189
H-Index
5
About
Xianting Li is a researcher whose work sits at the innovative intersection of robotics, indoor air quality, and computational optimization. Specializing in **robotic olfaction** and **contaminant source localization**, Li has made significant strides in developing intelligent multi-robot systems capable of identifying pollutant sources within complex, dynamic indoor environments. His research addresses a critical challenge in building science and environmental safety: accurately pinpointing airborne contaminants under varying ventilation conditions, including both natural and mechanical airflow scenarios. Li's most impactful contributions center on applying nature-inspired optimization algorithms — most notably Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm — to guide autonomous robots in real-world olfaction tasks. His experimental rigor is a hallmark of his work, consistently validating computational methods against physical test environments. Collectively, his top five papers have garnered nearly 190 citations, with his most-cited work earning 47 citations since 2019 — a strong indicator of influence within a specialized field. What distinguishes Li's research is its practical ambition: creating systems that could one day enhance emergency response, indoor air quality monitoring, and occupant safety in buildings where invisible contaminants pose serious health risks.
Research Focus
Key Achievements
Top Papers
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