Configurable simulation strategies for testing pollutant plume source localization algorithms using autonomous multisensor mobile robots. (2nd March 2022)

• Record Type: Journal Article
• Authors: Lewis, Tyrell; Bhaganagar, Kiran
• Is Part Of: International journal of advanced robotic systems. Volume 19:Number 2(2022)
• Publication Date: 2022-03-02
• Subjects: Autonomous plume source localization algorithms -- mobile environmental sensing networks -- unmanned ground vehicles -- atmospheric dispersion modelling applications -- source term estimation -- LES data; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298806221081325
• Languages: English
• ISSNs: 1729-8806

Abstract

In hazardous situations involving the dispersion of chemical, biological, radiological, and nuclear pollutants, timely containment of the emission is critical. A contaminant disperses as a dynamically evolving plume into the atmosphere, introducing complex difficulties in predicting the dispersion trajectory and potential evacuation sites. Strategies for predictive modeling of rapid contaminant dispersion demand localization of the emission source, a task performed effectively via unmanned mobile-sensing platforms. With vast possibilities in sensor configurations and source-seeking algorithms, platform deployment in real-world applications involves much uncertainty alongside opportunity. This work aims to develop a plume source detection simulator to offer a reliable comparison of source-seeking approaches and performance testing of ground-based mobile-sensing platform configurations prior to experimental field testing. Utilizing ROS, Gazebo, MATLAB, and Simulink, a virtual environment is developed for an unmanned ground vehicle with a configurable array of sensors capable of measuring plume dispersion model data mapped into the domain. For selected configurations, gradient-based and adaptive exploration algorithms were tested for source localization using Gaussian dispersion models in addition to large eddy simulation models incorporating the effects of atmospheric turbulence. A unique global search algorithm was developed to locate the true source with overall success allowing for further evaluation in field experiments. From the observations obtained in simulation, it is evident that source-seeking performance can improve drastically by designing algorithms for global exploration while incorporating measurements of meteorological parameters beyond solely concentration (e.g. wind velocity and vorticity) made possible by the inclusion of high-resolution large eddy simulation plume data.