Visual Cooperative Drone Tracking for Open-Path Gas Measurements

Open-path Tunable Diode Laser Absorption Spectroscopy offers an effective method for measuring, mapping, and monitoring gas concentrations, such as leaking CO2 or methane. Compared to spatial sampling of gas distributions using in-situ sensors, open-path sensors in combination with gas tomography algorithms can cover large outdoor environments faster in a non-invasive way. However, the requirement of a dedicated reflection surface for the open-path laser makes automating the spatial sampling process challenging. This publication presents a robotic system for collecting open-path measurements, making use of a sensor mounted on a ground-based pan-tilt unit and a small drone carrying a reflector. By means of a zoom camera, the ground unit visually tracks red LED markers mounted on the drone and aligns the sensor's laser beam with the reflector. Incorporating GNSS position information provided by the drone's flight controller further improves the tracking approach. Outdoor experiments validated the system's performance, demonstrating successful autonomous tracking and valid CO2 measurements at distances up to 60 meters. Furthermore, the system successfully measured a CO2 plume without interference from the drone's propulsion system, demonstrating its superiority compared to flying in-situ sensors.

Gas Source Localization Using physics Guided Neural Networks

This work discusses a novel method for estimating the location of a gas source based on spatially distributed concentration measurements taken, e.g., by a mobile robot or flying platform that follows a predefined trajectory to collect samples. The proposed approach uses a Physics-Guided Neural Network to approximate the gas dispersion with the source location as an additional network input. After an initial offline training phase, the neural network can be used to efficiently solve the inverse problem of localizing the gas source based on measurements. The proposed approach allows avoiding rather costly numerical simulations of gas physics needed for solving inverse problems. Our experiments show that the method localizes the source well, even when dealing with measurements affected by noise.