8 Zerstörungsfreie Prüfung
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Paper des Monats
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Gas Distribution Mapping (GDM) is a valuable tool for monitoring the distribution of gases in a wide range of applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This study aims to address the lack of deep learning approaches in GDM and explore their potential for improved mapping of gas distributions. In this paper, we introduce Gas Distribution Decoder (GDD), a learning-based GDM method. GDD is a deep neural network for spatial interpolation between sparsely distributed sensor measurements that was trained on an extensive data set of realistic-shaped synthetic gas plumes based on actual airflow measurements. As access to ground truth representations of gas distributions remains a challenge in GDM research, we make our data sets, along with our models, publicly available. We test and compare GDD with state-of-the-art models on synthetic and real-world data. Our findings demonstrate that GDD significantly outperforms existing models, demonstrating a 35% improvement in accuracy on synthetic data when measured using the Root Mean Squared Error over the entire distribution map. Notably, GDD appears to have superior capabilities in reconstructing the edges and characteristic shapes of gas plumes compared to traditional models. These potentials offer new possibilities for more accurate and efficient environmental monitoring, and we hope to inspire other researchers to explore learning-based GDM.
We introduce a passive smart dust concept as a novel solution for environmental monitoring. Utilizing chemical reagents like colorimetric indicators and other chemosensors, these particles detect varying environmental conditions. We developed paper-based sensors that are both cost-effective and eco-friendly. In practical tests, these sensors, dispersed over a designated area, successfully identified hazardous substances by changing their color when exposed to acids or bases. This color change was remotely detectable using a drone-mounted color camera. The data thus obtained was processed through specialized software, accurately pinpointing areas of contamination. This method proves the efficacy and scalability of passive smart dust technology for real-time, environmentally sustainable remote sensing of hazardous materials
This paper explores combining a gimbal-mounted tunable diode laser absorption spectroscopy (TDLAS) sensor and a video camera to create a virtual gas camera for methane leak detection. This provides a low-to-zero-cost extension of typical TDLAS gas tomography systems. A prototype setup mounted on a ground robot is evaluated. Results acquired using a simulated methane leak show the feasibility of the virtual gas camera, accurately detecting methane leaks by overlaying concentrations onto a visual image. While the acquisition time is significantly longer than for traditional gas cameras, potential enhancements are discussed. The study concludes that the virtual gas camera is feasible and useful, despite its longer acquisition time. It serves as a valuable software-only addition to typical TDLAS gas tomography systems, offering quickly-available on-site data augmentation for visual leak assessment at low-to-zero cost.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models.
This paper explores combining a gimbal-mounted tunable diode laser absorption spectroscopy (TDLAS) sensor and a video camera to create a virtual gas camera for methane leak detection. This provides a low-to-zero-cost extension of typical TDLAS gas tomography systems. A prototype setup mounted on a ground robot is evaluated. Results acquired using a simulated methane leak show the feasibility of the virtual gas camera, accurately detecting methane leaks by overlaying concentrations onto a visual image. While the acquisition time is significantly longer than for traditional gas cameras, potential enhancements are discussed. The study concludes that the virtual gas camera is feasible and useful, despite its longer acquisition time. It serves as a valuable software-only addition to typical TDLAS gas tomography systems, offering quickly-available on-site data augmentation for visual leak assessment at low-to-zero cost.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Ziel des FuE-Vorhabens ist die Entwicklung, Aufbau und Validierung eines gasto-mographischen Messsystems basierend auf stationärer und mobiler Sensorik zum Emissionsmonitoring in kommunalen Kläranlagen. Die Gastomographie basiert hierbei im Wesentlichen auf, aus unterschiedlichen Sichtwinkeln aufgenommenen, integralen Konzentrationsmessungen, anhand derer eine Rekonstruktion der Gasverteilung erfolgt. Das FuE-Projekt kombiniert hierfür stationäre Sensornetzwerke mit luftgestützten und bodengebundenen Robotersystemen in einem System. Letztere werden mit Reflektoren und/oder Gasdetektoren ausgestattet. Hierfür werden Open-Path-Gasmesstechnik basierend auf Tunable Diode Laser Ab-sorption Spectroscopy (TDLAS, primär CH4 in ppm·m) und In-situ-Gassensorik (CH4, ppm) auf Basis von z.B. Infrarot eingesetzt. Dies sind zwei komplementäre Methoden der Gasmessung: Die In-situ-Gassensorik ermöglicht Punktmessungen, wohingegen TDLAS-Sensoren Gaskonzentrationen integral über offene Messstre-cken von bis zu 60 m gegen Oberflächen messen. Die In-situ-Gassensorik wird für Referenzmessungen und ggf. zur Erhöhung der Rekonstruktionsqualität zum Ein-satz kommen. Die wesentlichen Neuheiten des Projekts sind:
• 2D-Gastomographie auf Basis eines mit TDLAS-basierter Open-Path-Gasmesstechnik und Reflektoren ausgestatteten heterogenen Messsystems.
• CT-basierte Rekonstruktionsalgorithmen, adaptiert an die komplexen Rahmenbedingungen.
• Validierung des Messsystems anhand realer Messungen auf Kläranlagen.
Communities worldwide face significant threats from Explosive Remnants of War (ERW), which endanger lives and restrict land usage. From forest fires due to ERWs or in ERW-contaminated areas (e.g., in Jüterbog, Germany) to broader global challenges (e.g., the Ukrainian conflict), the need for efficient detection and removal of these remnants, especially for humanitarian demining, is paramount. Traditional methods, like manual demining, have severe limitations in safety and efficiency. Here, we introduce an innovative solution to these challenges: “Chemosensing Smart Dust.” This technology uses chemoselective dyes that change their fluorescence properties when exposed to explosives like 2,4,6-trinitrotoluene (TNT). Fluorescence-based detection offers superior sensitivity, reduced likelihood of false positives, and enhanced accuracy of explosive detection. Drones, equipped with excitation lasers or LEDs, deploy the Chemosensing Smart Dust over areas of interest and actively detect the fluorescence changes using high-resolution cameras, offering a rapid, safe, and adaptable detection method. Beyond demining, this innovative approach has potential applications in monitoring polluted areas, homeland security, and emergency response.
Gasquellenlokalisierungen (Gas Source Localization, GSL) tragen dazu bei, die Folgen von Industrieunfällen und Naturkatastrophen zu mildern. Während die GSL, wenn von Menschen durchgeführt, gefährlich und zeitaufwändig ist, können Schwärme von wendigen und kostengünstigen Nanodrohnen die Effizienz und Sicherheit der Suche erhöhen. Da die geringe Nutzlast von Nanodrohnen die Sensor- und Rechenressourcen einschränkt, werden Strategien zur Koordination des Roboterschwarms verwendet, die von biologischen Schwärmen, wie Kolonien sozialer Insekten, inspiriert sind. Die meisten Schwarm-GSL-Strategien verwenden das Maximum der Gaskonzentrationsverteilung zur Schätzung der Gasquellenposition. Experimente legen jedoch nahe, dass die Intermittenz der Gasverteilung vielversprechender ist. In diesem Beitrag wird eine neuartige GSL-Strategie für Schwärme vorgestellt, die auf Pheromonkommunikation und Intermittenz der Gasverteilung basiert. Die Agenten, d.h. die Nanodrohnen, emittieren Pheromonmarker in einer virtuellen Umgebung, wenn sie eine neue Gaswolke feststellen. Die Agenten werden durch virtuelle Kräfte gesteuert und nutzen abwechselnd das Wissen des Schwarms, indem sie dem Pheromongradienten folgen, oder erkunden den Suchraum, indem sie einen Zufallspunkt ansteuern. Zur Kollisionsvermeidung werden die Agenten durchgehend von anderen Agenten und Wänden abgestoßen. Die Strategie wurde auf drei Nanodrohnen implementiert und durch ein Experiment in einem Innenraum mit einer statischen Gasquelle validiert. Die Ergebnisse zeigen eine Verbesserung gegenüber maximabasierten Verfahren und geringe Lokalisierungsfehler in Windrichtung.