Qilin Feng
Papers
9
Total Citations
344
H-Index
9
About
Qilin Feng is a prominent researcher specializing in robotic olfaction, indoor air quality, and autonomous source localization, with a particular focus on developing intelligent multi-robot systems for detecting and tracing contaminant sources in complex indoor environments. Feng's most significant contributions lie in applying and refining swarm intelligence algorithms — most notably particle swarm optimization (PSO) and whale optimization algorithm (WOA) — to enable robots to efficiently locate time-varying pollutant sources across diverse ventilation conditions, including natural, mechanical, and mixed-mode airflow systems. Through a rigorous combination of experimental validation and numerical modeling, Feng has systematically advanced the field from single-robot methodologies to sophisticated multi-robot collaborative frameworks, demonstrating superior localization performance in realistic three-dimensional indoor settings. His 2017 pioneering work on multi-robot active olfaction garnered 63 citations, establishing a foundational reference point for the field, while subsequent studies on dynamic environments and airflow-independent methods have collectively accumulated over 300 citations, underscoring the breadth and reproducibility of his findings. Feng's body of work offers invaluable practical guidance for indoor safety monitoring, emergency response, and building environmental control — making his research essential reading for engineers and scientists working at the intersection of robotics, indoor air quality, and computational intelligence.
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