Configurable Simulation Strategies for Testing Pollutant Plume Source Localization Algorithms Using Autonomous Multi-Sensor Mobile Robots

Date
2020
Authors
Lewis, Tyrell
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Abstract

In hazardous situations involving the dispersion of Chemical, Biological, Radiological and Nuclear (CBRN) 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 (SS) 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 SS 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 (UGV) 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 (LES) models incorporating the effects of atmospheric turbulence. The gradient-based algorithm served the purpose of validating the simulator design by demonstrating susceptibility to local maximum convergence in turbulent flows. A unique global-search algorithm was developed to locate the true source with overall success allowing for further evaluation in field experiments. Future directions for improving the simulator are aimed at extending the applications of mobile-sensing robots to include boundary tracking, along with developing hybrid algorithms that incorporate measurements of additional meteorological parameters.

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Keywords
Environmental Sensing, Fluid Dynamics, Robotics, Simulation, Turbulence
Citation
Department
Mechanical Engineering