A Simulated UAV System for Greenhouse Gas Emission Source Localization Using Dispersion Modeling

Abstract

Accurate and efficient localization of greenhouse gas (GHG) emission sources is an important challenge in environmental monitoring. Unmanned aerial vehicles (UAVs) offer a flexible platform for this task, but efficient sampling and localization remain challenging, particularly under resource constraints such as limited flight time and onboard processing capabilities.

This thesis presents a simulated system for UAV-based GHG emission source localization in a two-dimensional, cell-based environment. The system has two main components: an emission source localization component and a path planner. The localization component applies a Gaussian plume dispersion model to estimate source locations from simulated UAV concentration measurements. The path planner, based on Dijkstra’s algorithm, accounts for wind direction in its cost function to produce efficient sampling paths that reduce wind resistance and overall travel cost.

The system is evaluated through a series of simulation experiments. Localization accuracy is assessed under various sampling densities, while the efficiency of the path planner is evaluated by comparing the wind-aware paths to those generated without accounting for wind direction. Results show that the localization method performs reliably with a moderate sampling density, where the success rate of locating a source within a 10 m2 area is 0.74 when samples are 116 m apart. Additionally, the path planner produces slightly more efficient paths, showing a 5% gain when using a cost function as an efficiency metric.