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About

Zhilong Chen is a leading researcher in the field of multi-robot active olfaction, with a primary focus on locating time-varying contaminant sources in complex indoor environments. His work bridges robotics, environmental sensing, and ventilation engineering to address critical challenges in indoor air quality and safety. Chen’s major contributions include the development of particle swarm optimization (PSO)-based methods that enable teams of robots to collaboratively track and pinpoint dynamic contaminant sources under both natural and mechanical ventilation conditions. His experimental studies have demonstrated the effectiveness of these approaches in 3D indoor spaces, accounting for the complexities of airflow and source variability. With over 180 citations across his most-cited papers, Chen’s research has had a significant impact on the fields of environmental monitoring and robotic olfaction. His 2017 paper on multi-robot active olfaction for time-varying sources remains a foundational reference, while his 2019 studies on PSO-based methods in dynamic environments have advanced practical applications in emergency response and building safety. Chen’s work is essential reading for researchers interested in autonomous systems, environmental sensing, and indoor air pollution control.