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Paper des Monats
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Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment’s characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network’s quality will be investigated.
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.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms.
Verkehrsunfälle unter Beteiligung von Fahrzeugen mit alternativen Kraftstoffen wie LPG („Autogas“, Liquefied Petroleum Gas), CNG (Compressed Natural Gas, komprimiertes Erdgas) oder CGH2 (Compressed Gas Hydrogen, komprimierter Wasserstoff), bergen neue, komplexe Risiken für Passagiere, Einsatzkräfte und deren Umfeld. Im Rahmen des Projektes „CoFi-ABV“ beschäftigt sich die Bundesanstalt für Materialforschung und -prüfung (BAM) unter Berücksichtigung komplexer Brand- und Explosionsszenarien mit einer ganzheitlichen Betrachtung der Auswirkungen des unfallbedingten Versagens von Gasbehältern für alternative Kraftstoffe. Ein wichtiger Teil des Forschungsvorhabens umfasst Entwicklung, Aufbau und Validierung einer unbemannten Flugplattform (Unmanned Aerial Vehicle, UAV) zur Ferndetektion von Gaswolken. Für das sogenannte gassensitive Mini-UAV wird ein robuster Open-Path-Gasdetektor auf einer Sensorbewegungsplattform in Form eines modifizierten Kamera-Gimbals, integriert. Ziel ist es, ein leistungsfähiges und robustes Werkzeug zu entwickeln, welches Einsatzkräften im Falle eines Unfalls als Hilfe zur Abschätzung der Gefahr durch Gaswolken, ihrer Ausbreitung und der notwendigen Absperrbereiche zur Verfügung steht. Dieser Artikel ist der zweite der Reihe und legt, nach Projektvorstellung im ersten Teil, nun den Schwerpunkt auf das gassensitive Mini-UAV.
A 400 m² soil test field with gas injection system was built up for the purpose of large-scale validation, optimization, and characterization of a novel comprehensive monitoring method for underground gas storage areas. The method combines gas sensing technology with linear form factor for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct time-series of gas distribution maps based on samples of orthogonally-aligned linear gas sensors. Several injection experiments with carbon dioxide (CO2) at different days with varying boundary conditions indicates the potential of the method for, e.g., rapid leakage detection with respect to Carbon Capture and Storage (CCS) issues.
In Deutschland gibt es eine große Anzahl von Kraftfahrzeugen, die mit alternativen Antrieben (z. B. LPG (Liquefied Petroleum Gas), CNG (Compressed Natural Gas)) ausgerüstet sind. In zahlreichen Großversuchsserien untersucht die Bundesanstalt für Materialforschung und -prüfung (BAM) im Rahmen des internen Forschungsprojekts CoFi-ABV (Complex Fires – Auswirkungen von Behälterversagen) die Auswirkungen beim Versagen der Treibstoffbehälter dieser alternativen Antriebe. Ziel dieser Untersuchungen ist eine ganzheitliche Auswirkungsbetrachtung, um die Risiken für Feuerwehren und Rettungskräfte besser abschätzen zu können. Im Vorfeld zu den Versuchen für LPG- und CNG-Tanks wurden in einer weiteren Versuchsserie 15handelsübliche 11-kg-Propangasflaschen ohne Sicherheitseinrichtung unterfeuert. Alle 15 Behälter haben – wie zu erwarten war – versagt. Neben den Zustandsdaten des Behälters zum Versagenszeitpunkt (Temperatur des Behältermantels, Innendruck und -temperatur) wurden die Auswirkungen auf die Umgebung hinsichtlich Druckwirkung und Fragmentierung (Anzahl, Masse, Distanz, Richtung) dokumentiert.
A current-independent torque equation for the permanent-magnet synchronous motor (PMSM) aiming at direct-drive servo applications is derived from a first principles model. Instead of measuring currents, all required control parameters are derived from optical incremental encoder measurements. The results are verified on a real system in test series showing the effect of static friction and proving the obtained torque model.
A 400 m2 soil test field with gas injection system was built up, which enables an experimental validation of linear membrane-based gas sensors – a sensor technology which was developed based on the selective permeation of gases through membranes. Several soil watering and injection experiments with carbon dioxide at different days with varying boundary conditions were performed showing that the sensor behaves mostly insensitive to the environmental conditions investigated, i.e., barometric pressure fluctuations, soil temperature, air temperature and humidity, sun duration, and wind speed. Furthermore, depending on water infiltration, a gas phase displacement could be observed in-situ based on the changed measurement signal. The results of the validation experiments highlight the potential of the method for rapid leak detection and localization qualifying the sensor particularly for safety applications, e.g., in underground gas storage areas.
One of the main unsolved issues of CCS is the comprehensive surveillance of CO2 storage areas with reasonable effort and costs. This study presents an approach for distributed subsurface monitoring of gas storage areas. The concept combines different measurement technologies to one multifunctional sensor: membrane based measurement technology for in situ monitoring of gases in soil and fibre optical sensing of temperature and strain (as a measure for structural change). A test field of application-relevant dimensions is built up to validate and optimize the technology.