Experimental study on 3D source localization in indoor environments with weak airflow based on two bionic swarm intelligence algorithms

Abstract

Existing studies on using robots to locate indoor pollutant sources mainly focused on ventilated indoor environments, and rarely addressed unventilated or poorly ventilated indoor environments. This study aims to locate pollutant sources with unknown heights in real-world indoor environments with weak airflow. For this purpose, a 3D source localization system was first developed, which consists of three robots equipped with gas sensors that can be controlled to move up and down in the height direction. Next, two 3D source localization methods based on bionic swarm intelligence algorithms, namely the 3D whale optimization algorithm (WOA_3D) method and the 3D particle swarm optimization (PSO_3D) method, were validated in an unventilated indoor space with sources at different heights. The experimental results show that both the WOA_3D method and PSO_3D methods were highly adaptable to the change in source height and performed well in terms of success rate and localization efficiency. The comparison between the WOA_3D method and the PSO_3D method shows that the former method has better comprehensive performance. The WOA_3D method was also compared with its 2D simplified version, namely, the WOA_2D method, which further demonstrated the necessity of using the 3D source localization method when the source height is unknown.