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The objective of this Ph.D. thesis is the development and validation of a VTOL-based (Vertical Take Off and Landing) micro-drone for the measurement of gas concentrations, to locate gas emission sources, and to build gas distribution maps. Gas distribution mapping and localization of a static gas source are complex tasks due to the turbulent nature of gas transport under natural conditions [1] and becomes even more challenging when airborne. This is especially so, when using a VTOL-based micro-drone that induces disturbances through its rotors, which heavily affects gas distribution. Besides the adaptation of a micro-drone for gas concentration measurements, a novel method for the determination of the wind vector in real-time is presented. The on-board sensors for the flight control of the micro-drone provide a basis for the wind vector calculation. Furthermore, robot operating software for controlling the micro-drone autonomously is developed and used to validate the algorithms developed within this Ph.D. thesis in simulations and real-world experiments. Three biologically inspired algorithms for locating gas sources are adapted and developed for use with the micro-drone: the surge-cast algorithm (a variant of the silkworm moth algorithm) [2], the zigzag / dung beetle algorithm [3], and a newly developed algorithm called “pseudo gradient algorithm”. The latter extracts from two spatially separated measuring positions the information necessary (concentration gradient and mean wind direction) to follow a gas plume to its emission source. The performance of the algorithms is evaluated in simulations and real-world experiments. The distance overhead and the gas source localization success rate are used as main performance criteria for comparing the algorithms. Next, a new method for gas source localization (GSL) based on a particle filter (PF) is presented. Each particle represents a weighted hypothesis of the gas source position. As a first step, the PF-based GSL algorithm uses gas and wind measurements to reason about the trajectory of a gas patch since it was released by the gas source until it reaches the measurement position of the micro-drone. Because of the chaotic nature of wind, an uncertainty about the wind direction has to be considered in the reconstruction process, which extends this trajectory to a patch path envelope (PPE). In general, the PPE describes the envelope of an area which the gas patch has passed with high probability. Then, the weights of the particles are updated based on the PPE. Given a uniform wind field over the search space and a single gas source, the reconstruction of multiple trajectories at different measurement locations using sufficient gas and wind measurements can lead to an accurate estimate of the gas source location, whose distance to the true source location is used as the main performance criterion. Simulations and real-world experiments are used to validate the proposed method. The aspect of environmental monitoring with a micro-drone is also discussed. Two different sampling approaches are suggested in order to address this problem. One method is the use of a predefined sweeping trajectory to explore the target area with the micro-drone in real-world gas distribution mapping experiments. As an alternative sampling approach an adaptive strategy is presented, which suggests next sampling points based on an artificial potential field to direct the micro-drone towards areas of high predictive mean and high predictive variance, while maximizing the coverage area. The purpose of the sensor planning component is to reduce the time that is necessary to converge to the final gas distribution model or to reliably identify important parameters of the distribution such as areas of high concentration. It is demonstrated that gas distribution models can provide an accurate estimate of the location of stationary gas sources. These strategies have been successfully tested in a variety of real-world experiments in different scenarios of gas release using different gas sensors to verify the reproducibility of the experiments. The adaptive strategy was also successfully validated in simulations using predefined sweeping trajectories as reference criteria. The results of this Ph.D. thesis reflect the applicability of gas-sensitive microdrones in a variety of scenarios of gas release. Effective counteractive measures can be set in motion after accidents involving gas emissions with the aid of spatially resolved gas concentration and wind data collected with micro-drones. Monitoring of geochemically active regions, landfills, CO2 storage facilities, and the localization of gas leaks are further areas of application.
Die BAM ist nahezu über die gesamte Wertschöpfungskette hinweg wissenschaftlich tätig. Von der sicheren und effizienten Wasserstofferzeugung (POWER-to-GAS), über die (Zwischen-)Speicherung von Wasserstoff in Druckgasspeichern bis hin zum Transport bspw. mittels Trailerfahrzeug zum Endverbraucher.
Komplettiert werden die Aktivitäten der BAM durch die sicherheitstechnische Beurteilung von wasserstoffhaltigen Gasgemischen, die Verträglichkeitsbewertung von Werkstoffen bis hin zur Detektion von Wasserstoffkonzentrationen über geeignete Sensorik, auch mittels ferngesteuerter Messdrohnen (sog. UAV-Drohnen).
Zudem untersucht die BAM proaktiv Schadensrisiken und Unfallszenarien für die Sicherheitsbetrachtung, um mögliche Schwachstellen aufzeigen und potenzielle Gefährdungen erkennen zu können.
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.
In this paper, we present and validate the concept of an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor with a 3-axis aerial stabilization gimbal for aiming at a versatile octocopter. While the TDLAS sensor provides integral gas concentration measurements, it does not measure the distance traveled by the laser diode’s beam nor the distribution of gas along the optical path. Thus, we complement the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from a set of integral concentration measurements. To allow for a fundamental ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present results showing its performance characteristics and 2D plume reconstruction capabilities under realistic conditions. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO).
Contamination of natural bodies of water or soil with oils and lubricants (or generally, hydrocarbon derivatives such as petrol, fuels, and others) is a commonly found phenomenon around the world due to the extensive production, transfer, and use of fossil fuels. In this work, we develop a simple system for the on-field detection of total petroleum hydrocarbons (TPHs) in water and soil. The test is based on the measurement of the fluorescence signal emitted by the molecular rotor 2-[ethyl[4-[2-(4-nitrophenyl)ethenyl]phenyl]amino]ethanol (4-DNS-OH). This dye is embedded in a hydrophobic polymeric matrix (polyvinylidene fluoride), avoiding interactions with water and providing a robust support for use in a test strip fashion. Together with the strips, an embedded optical system was designed for fluorescence signal read-out, featuring a Bluetooth low-energy connection to a commercial tablet device for data processing and analysis. This system works for the detection and quantification of TPHs in water and soil through a simple extraction protocol using a cycloalkane solvent with a limit of detection of 6 ppm. Assays in surface and sea waters were conclusive, proving the feasibility of the method for in-the-field operation.
Occupational health is an important topic, especially in industry, where workers are exposed to airborne by-products (e.g., dust particles and gases). Therefore, continuous monitoring of the air quality in industrial environments is crucial to meet safety standards. For practical and economic reasons, high-quality, costly measurements are currently only carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only.
The project “Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios” (RASEM) addresses this issue by bringing together the benefits of both – low- and high-cost – measuring technologies enabling cost-efficient long-term air quality monitoring in realtime: A stationary network of low-cost sensors that is augmented by mobile units carrying high-quality sensors. By mapping the distribution of gases and particles in industrial environments with the proposed RASEM system, measures can be identified to improve on-site working conditions much faster than using traditional methods.
In this presentation, we detail the technical aspects of RASEM and introduce the mobile platforms used.
QI-Digital is a joined project aiming at digitalising Quality Infrastructure (QI) processes involving standardization, conformity assessment, accreditation, metrology, and market surveillance [1]. Federal institute of material research and testing (BAM) is working on the creation of a digital calibration certifi-cate (DCC) to achieve digital metrological traceability and conformity assessment. The utilisation of machine readable and executable DCCs in the XML format is demonstrated on an example of a tem-perature measurement at a hydrogen refueling station. The certificates will be retrieved and analysed automatically at a Process Control System or at a Digital Twin.
Gas source localization (GSL) helps mitigate the impact of industrial accidents and natural disasters. While GSL may be dangerous and time-consuming when performed by humans, swarms of agile and inexpensive nano aerial robots may increase the safety and efficiency of gas source localizations. Since the small payloads of nano aerial robots limit the sensing and computing resources, strategies adapted from biological swarms, such as colonies of social insects, are used to coordinate robot swarms. Most swarm GSL strategies are based on the assumption that the maxima of gas concentrations are sufficiently close to the gas sources. However, prior studies have indicated that the occurrence of “bouts”, a metric for the intermittency of gas distributions, may advantageously be used as a more accurate gas source proximity indicator. This paper presents a swarm GSL strategy employing bouts as source proximity indicators and a bio-inspired pheromone system for communication. Nano aerial robots, deployed in this study, act as agents and emit pheromone markers in an artificial environment upon detecting bouts. Leveraging the concept of artificial potential fields, the agents switch between exploiting the knowledge of the swarm by following pheromone gradients and exploring the search space by targeting a random point. The agents are repelled by each other and by walls to avoid collisions. The swarm GSL strategy is implemented into three nano aerial robots and validated in a real-world experiment in an indoor environment with a single gas source. The results indicate that the the swarm GSL strategy presented in this paper is capable of GSL in indoor environments and that the intermittency of gas distributions is a better source proximity indicator than the mean concentration.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano
drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.