An experimental study on locating time-varying pollutant sources in a dynamic indoor environment with mechanical ventilation based on multi-robot cooperation. (15th May 2023)

• Record Type: Journal Article
• Authors: Liao, Yu; Chen, Axiang; Cai, Hao; Lu, Jingyu; Zhang, Wei; Lin, Bencheng; Guo, Xun; Wei, Ling; Tong, Yan
• Is Part Of: Journal of building engineering. Volume 67(2023)
• Publication Date: 2023-05-15
• Subjects: Dynamic indoor environment -- Time-varying source -- Source localization -- Mobile robot olfaction (MRO) -- Multi-robot cooperation; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2023.105962
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Using robots to locate indoor pollutant sources has attracted increasing attention in recent years, which is important for securing people's health and safety indoors. However, few studies have addressed the common scenarios for locating a time-varying source (release rate varies over time) in dynamic indoor environments with mechanical ventilation (condition of supply airflow varies over time). In such scenarios, the coupling effect of indoor dynamic airflow and time-varying source pose great challenges to robots locating the source. To address these challenges, in this study, a mobile robot olfaction system equipped with three robots was developed and 180 experiments in a laboratory were performed. Two methods, namely, the improved particle swarm optimization (IPSO) method and the improved whale swarm algorithm (IWOA) method, were first validated, followed by comparisons with two other methods, the standard particle swarm optimization (SPSO) method and wind utilization II (WUII) method. Both the IPSO and IWOA methods show potential for practical application due to their high success rates (averaging 78.33% and 88.33%, respectively) and localization efficiency, while the success rates of the SPSO and WUII methods (less than 50%) are insufficient for practical application. Highlights: Locating time-varying pollutant sources in dynamic indoor airflow. Multi-robot cooperation was used for source localization. A total of 180 source localization experiments were conducted. Four source localization methods were validated and compared. Periodic and decaying sources were located at different locations.