Experimental study on three single-robot active olfaction algorithms for locating contaminant sources in indoor environments with no strong airflow

Abstract Locating the sources of contaminants or hazardous substances in indoor environments is extremely important for ensuring indoor air quality and indoor environmental safety. Existing source localization studies have focused on environments with strong airflow, and very few studies have addressed the more challenging source localization problems in environments with no strong airflow. In this study, we developed a mobile robot for source localization and used the robot to test and compare three single-robot source localization algorithms (E. coli, spiral and hex-path) that have the potential to be applied to environments with no strong airflow. Each algorithm was tested by 15 independent experiments. Considering both the success rates and average numbers of steps, the performance of the three algorithms from high to low was in the order of the hex-path algorithm, E. coli algorithm, and spiral algorithm, with the success rates of 73.3%, 66.7% and 60.0%, and the average numbers of steps of 51.43, 54.12, and 63.67, respectively. Based on the same comparison criteria, all three algorithms can significantly improve the success rate or reduce the average number of steps compared with a random algorithm that encounters the source solely by chance. This study can provide more choices for developing single-robot source localization methods for environments with no strong airflow and can provide inspiration and guidance for improving existing methods and developing new methods.

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