An improved particle swarm optimization method for locating time-varying indoor particle sources

The indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.