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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 permanent-magnet synchronous motor (PMSM) servo drive for lightweight robotic platforms that have a high torque demand at low rotational speeds has been developed. First, a current-independent torque controller is derived and cascaded with a speed and position controller, while merely an encoder is used as a sensor device in combination with a speed estimator. Finally, the speed estimator output is compared to gyroscope measurements and the overall functioning is verified on a real system.
Cable-suspended parallel manipulators have been a topic of research for multiple decades and called special attention in the fields of simulations. However, they are also well-suited for the simple evaluation of aerial-based mobile robot olfaction (MRO) algorithms, such as gas source localization and gas distribution mapping. Based on an open source framework for 3D printers, we designed a low-cost underconstrained, cable-suspended parallel manipulator. Computations are carried out purely on an Atmel ATmega2560 microcontroller.
Cable-suspended parallel manipulators have been a topic of research for multiple decades and called special attention in the fields of simulations. However, they are also well-suited for the simple evaluation of aerial-based mobile robot olfaction (MRO) algorithms, such as gas source localization and gas distribution mapping. Based on an open source framework for 3D printers, we designed a low-cost underconstrained, cable-suspended parallel manipulator. Computations are carried out purely on an Atmel ATmega2560 microcontroller.
A set of equations is derived to estimate the 3D wind vector with a multirotor aircraft using the aircraft itself as a flying anemometer. Since the thrust component is required to compute the wind vector, the PWM signal controlling the motors of the aircraft is measured and a transfer function describing the relation between the PWM signal and the rotational speed of the motors is derived.
An equation for wind vector estimation using a multirotor aircraft as a flying anemometer is shown. To compute the wind vector an estimate of the thrust of the aircraft is required, which is related to the rotational speed of the rotors. Hence, a sensing system for the rotational speed using phototransistors is presented.
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.
In industrial environments, airborne by-products such as dust and (toxic) gases, constitute a major risk for the worker’s health. Major changes in automated processes in the industry lead to an increasing demand for solutions in air quality management. Thus, occupational health experts are highly interested in precise dust and gas distribution models for working environments. For practical and economic reasons, high-quality, costly measurements are often available for short time-intervals only. Therefore, current monitoring procedures are carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only. Real-time knowledge of contaminant distributions inside the working environment would also provide means for better and more economic control of air impurities. For example, the possibility to regulate the workspace’s ventilation exhaust locations can reduce the concentration of airborne contaminants by 50%.
To improve the occupational health and safety of (industrial) workplaces, this work aims for developing a swarm of gas-sensitive aerial nano robots for monitoring indoor air quality and for localizing potential emission sources.
In robotic applications, it is often necessary to orient a sensor quickly. Spherical parallel manipulators (SPM) are well suited for this purpose since they offer superior dynamics and structural stiffness as compared to serial manipulators. To control them, however, the kinematic equations have to be known. In this paper, a SPM with three degrees of freedom and the kinematic equations describing its mechanical properties are presented.
In robotic applications, it is often necessary to orient a sensor quickly. Spherical parallel manipulators (SPM) are well suited for this purpose since they offer superior dynamics and structural stiffness as compared to serial manipulators. To control them, however, the kinematic equations have to be known. In this paper, a SPM with three degrees of freedom and the kinematic equations describing its mechanical properties are presented.
An unmanned micro aircraft (UAV) is equipped with an open-path gas detector to localise gas sources and to generate gas distribution maps. The gas detector is based on the tunable diode laser absorption spectroscopy (TDLAS) and attached to a 3-axis aerial stabilisation gimbal. Sensor validation experiments and first gas tomography reconstructions are shown.
Leaking methane (CH4) from infrastructures, like pipelines, industrial complexes and landfills, or accidental hazardous goods transports is critical for the environment, but can also pose a risk to human life. To enable a fast detection and localisation of these kind of leaks, we developed a novel robotic platform for aerial remote gas sensing. The platform combines an unmanned micro aircraft (UAV), a 3-axis aerial stabilisation gimbal and an open-path gas detector based on the tunable diode laser absorption spectroscopy (TDLAS).
For several applications involving multirotor aircraft, it is crucial to know both the direction and speed of the ambient wind. In this paper, an approach to wind vector estimation based on an equilibrium of the principal forces acting on the aircraft is shown. As the thrust force generated by the rotors depends on their rotational speed, a sensor to measure this quantity is required. Two concepts for such a sensor are presented: One is based on tapping the signal carrying the speed setpoint for the motor controllers, the other one uses phototransistors placed underneath the rotor blades. While some complications were encountered with the first approach, the second yields accurate measurement data. This is shown by an experiment comparing the proposed speed sensor to a commercial non-contact tachometer.
In industrial environments, airborne by-products such as dust and (toxic) gases, constitute a major risk for the worker’s health. Major changes in automated processes in the industry lead to an increasing demand for solutions in air quality management. Thus, occupational health experts are highly interested in precise dust and gas distribution models for working environments. For practical and economic reasons, high-quality, costly measurements are often available for short time-intervals only. Therefore, current monitoring procedures are carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only. Real-time knowledge of contaminant distributions inside the working environment would also provide means for better and more economic control of air impurities. For example, the possibility to regulate the workspace’s ventilation exhaust locations can reduce the concentration of airborne contaminants by 50%.
To improve the occupational health and safety of (industrial) workplaces, this work aims for developing a swarm of gas-sensitive aerial nano robots for monitoring indoor air quality and for localizing potential emission sources.
In this paper, we present 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) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary 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 first results showing the 2D plume reconstruction capabilities of the system under realistic conditions.
An equation for wind vector estimation using a multirotor aircraft as a flying anemometer is shown. To compute the wind vector an estimate of the thrust of the aircraft is required, which is related to the rotational speed of the rotors. Hence, a sensing system for the rotational speed using phototransistors is presented.
KonSens (Kommunizierende Sensorsysteme für die Bauteil- und Umweltüberwachung) - Projektergebnisse
(2019)
Im Projekt KonSens werden für die Anwendungsbeispiele bauteilintegrierte Sensorik für Betonkomponenten und mobile Multigassensorik Sensorsysteme in Form von Funktionsmustern entwickelt, validiert und angewendet. Schwerpunkte liegen einerseits in der Detektion und Bewertung von Korrosionsprozessen in Beton und andererseits in der Detektion und Quantifizierung sehr geringer Konzentrationen toxischer Gase in der Luft. Dabei ist die Adaption der sensorischen Methoden aus dem Labor in reale Messumgebungen inklusive geeigneter Kommunikationstechnik ein wichtiger Aspekt.
Der Beitrag präsentiert die erarbeiteten technischen Richtlinien zur Anwendung und Prüfung von Multigassensoren innerhalb der Richtlinienreihe VDI/VDE 3518 sowie in diesem Zusammenhang die Möglichkeiten der Sensorprüfung im akkreditierten Prüflabor des Fachbereichs 8.1 der BAM. Im Speziellen wird das in Kürze veröffentlichte Blatt 3 der Richtlinienreihe vorgestellt, das Bezug auf Multigassensoren für geruchsbezogene Messungen mit elektronischen Nasen nimmt.
In mobile robot applications, some sensors such as open-path gas detectors or laser rangefinders need to be aimed at specific targets in order to get the desired measurements. To do this in a fast and elegant manner, we present a spherical parallel manipulator with three degrees of freedom. Compared to typical serial manipulators, it offers superior dynamics and structural stiffness, which are important parameters for this type of task. We present the mechanical design and derive kinematic equations both to compute set-points for the desired orientation and to estimate the current state of the system. A PID controller is used to generate control signals.
In this paper, we introduce a nano aerial robot swarm for indoor air quality monitoring applications such as occupational health and safety of (industrial) workplaces. The concept combines a robotic swarm composing of nano Unmanned Aerial Vehicles (nano UAVs), based on the Crazyflie 2.0 quadrocopter, and small lightweight metal oxide gas sensors for measuring the Total Volatile Organic Compound (TVOC) in ppb and estimating the eCO2 (equivalent calculated carbon-dioxide) concentration in ppm. TVOC is a measure for the indoor air quality. An indoor localization and positioning system will be used to estimate the absolute 3D position of the swarm like GPS. Based on this novel indoor air quality monitoring concept, the development and validation of new algorithms in the field of Mobile Robot Olfaction (MRO) are planned, namely gas source localization and gas distribution mapping. A test scenario will be built up to validate and optimize the gas-sensitive nano aerial robot swarm for the intended applications.
Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring.
A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used.
Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms.
For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing.
In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account.
Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model.
Small unmanned aircraft are used increasingly as flying sensor platforms to perform tasks such as gas distribution mapping or environmental monitoring. Sometimes, these applications require knowledge of the ambient wind field.
In general, it would be interesting to avoid any additional hardware, both on ground and on the aircraft. Here, we present an approach to estimate the wind vector just by exploiting the state variables of the aircraft.
This paper introduces a novel robotic platform for aerial remote gas sensing. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer a number of advantages for inspection and surveillance. No direct contact with the target gas is needed and thus the influence of the aerial platform on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of mini-copters. Here, we introduce and present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile hexacopter. 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). It enables tomographic reconstruction of gas plumes and a localization of gas sources. We also present first results showing the gas sensing and aiming capabilities under realistic conditions.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or hardly accessible areas. A challenging task, however, is the generation of threedimensional distribution maps from these gas measurements.
Suitable reconstruction algorithms can be adapted, for instance, from the field of computed tomography (CT), but both their performance and strategies for selecting optimal measuring poses must be evaluated. For this purpose simulations are used, since, in contrast to field tests, they allow repeatable conditions. Although several simulation tools exist, they lack realistic models of remote gas sensors. Recently, we introduced a model for a Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor taking into account the conical shape of its laser beam. However, the novel model has not yet been validated with experiments. In this paper, we compare our model with a real sensor device and show that the assumptions made hold.
Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring. A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used. Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms. For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing. In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account. Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model.
In this paper, we introduce a nano aerial robot swarm for Indoor Air Quality (IAQ) monitoring applications such as occupational health and safety of (industrial) workplaces. The robotic swarm is composed of nano Unmanned Aerial Vehicles (UAVs), based on the Crazyflie 2.0 quadrocopter, and small lightweight Metal Oxide (MOX) gas sensors for measuring the Total Volatile Organic Compound (TVOC), which is a measure for IAQ. An indoor localization and positioning system is used to estimate the absolute 3D position of the swarm similar to GPS. A test scenario was built up to validate and optimize the swarm for the intended applications. Besides calibration of the IAQ sensors, we performed experiments to investigate the influence of the rotor downwash on the gas measurements at different altitudes and compared them with stationary measurements. Moreover, we did a first evaluation of the gas distribution mapping performance. Based on this novel IAQ monitoring concept, new algorithms in the field of Mobile Robot Olfaction (MRO) are planned to be developed exploiting the abilities of an aerial robotic swarm.
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).
This presentation gives an introduction to the gas-sensitive aerial robots developed at BAM, including various application examples in the field of mobile robot olfaction: gas source localization and gas distribution mapping.
The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. In this paper, we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios.
The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. Here we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios.
In this paper, we present 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) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary 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 first results showing the 2D plume reconstruction capabilities of the system under realistic conditions.
Small unmanned aircraft (UA) are used increasingly as flying sensor platforms. Amongst other things they are used for environmental monitoring, for example gas distribution mapping. Sometimes, these applications require knowledge of the ambient wind field, which can be measured by additional devices like anemometers. In general, it would be interesting to avoid such additional apparatuses. One way to achieve this is to estimate the wind vector, that is both the wind direction and speed, from the state variables of the UA.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or poorly accessible areas. A challenging task however, is the generation of three-dimensional distribution maps from these spatially sparse gas measurements. To obtain high-quality reconstructions, the choice of optimal measuring poses is of great importance. Remote gas sensors, that are commonly used in Robot Assisted Gas Tomography (RAGT), require reflecting surfaces within the sensor’s range, limiting the possible sensing geometries, regardless of whether the robots are ground-based or airborne. By combining ground and aerial robots into a heterogeneous swarm whose agents are equipped with reflectors and remote gas sensors, remote inter-robot gas measurements become available, taking RAGT to the next dimension – releasing those constraints. In this paper, we demonstrate the feasibility of drone-to-drone measurements under realistic conditions and highlight the resulting opportunities.
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.