TY - JOUR A1 - Neumann, Patrick P. A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias A1 - Schiller, J.H. T1 - Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms N2 - Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion, the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposes an integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-based plume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitive micro-drone. We compare the performance of the proposed system in simulations and real-world experiments against two commonly used tracking algorithms adapted for aerial exploration missions. KW - Autonomous micro UAV KW - Chemical and wind sensing KW - Gas source localization KW - Particle filter PY - 2013 U6 - https://doi.org/10.1080/01691864.2013.779052 SN - 0169-1864 SP - 725 EP - 738 PB - VNU Sciences Pr. CY - Utrecht AN - OPUS4-28010 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Asadi, S. A1 - Schiller, J.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias T1 - An artificial potential field based sampling strategy for a gas-sensitive micro-drone N2 - This paper presents a sampling strategy for mobile gas sensors. Sampling points are selected using a modified artificial potential field (APF) approach, which balances multiple criteria to direct sensor measurements towards locations of high mean concentration, high concentration variance and areas for which the uncertainty about the gas distribution model is still large. By selecting in each step the most often suggested close-by measurement location, the proposed approach introduces a locality constraint that allows planning suitable paths for mobile gas sensors. Initial results in simulation and in real-world experiments with a gas-sensitive micro-drone demonstrate the suitability of the proposed sampling strategy for gas distribution mapping and its use for gas source localization. T2 - IEEE/RSJ International conference on intelligent robots and systems (IROS '11) / Workshop on robotics for environmental monitoring (WREM2011) CY - San Francisco, CA, USA DA - 25.09.2011 KW - Autonomous UAV KW - Chemical sensing KW - Gas distribution modelling KW - Gas source localization KW - Gas sensors KW - Mobile sensing system KW - Quadrocopter KW - Sensor planning KW - Artificial potential field PY - 2011 SP - 34 EP - 38 AN - OPUS4-24537 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Neumann, Patrick P. A1 - Trincavelli, M. T1 - Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go? N2 - Roboticists often take inspiration from animals for designing sensors, actuators or algorithms that control the behaviour of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odour plume. In particular the task of tracking an odour plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behaviour of moths, dungbeetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is at the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behaviour of animals, can be usefully 'translated' into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically-inspired algorithms. KW - Mobile robotics KW - Mobile robot olfaction KW - Biologically inspired robots KW - Landfill surveillance PY - 2011 UR - http://journal.frontiersin.org/Journal/10.3389/fneng.2011.00020/full U6 - https://doi.org/10.3389/fneng.2011.00020 SN - 1662-6443 SP - 1 EP - 14 PB - Frontiers Research Foundation CY - Lausanne AN - OPUS4-25037 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Neumann, Patrick P. A1 - Asadi, S. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias A1 - Schiller, J.H. T1 - Autonomous gas-sensitive microdrone - wind vector estimation and gas distribution mapping N2 - This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization. KW - Anemometric sensor KW - Autonomous micro UAV KW - Chemical sensing KW - Gas distribution modelling KW - Gas source localization KW - Gas sensors KW - Mobile sensing system KW - Quadrocopter KW - Sensor planning KW - Artificial potential field PY - 2012 U6 - https://doi.org/10.1109/MRA.2012.2184671 SN - 1070-9932 VL - 19 IS - 1 SP - 50 EP - 61 PB - IEEE CY - New York, NY, USA AN - OPUS4-25773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Neumann, Patrick P. A1 - Asadi, S. A1 - Hernandez Bennetts, V. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias T1 - Monitoring of CCS areas using micro unmanned aerial vehicles (MUAVs) N2 - Carbon capture & storage (CCS) is one of the most promising technologies for greenhouse gas (GHG) management. However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leak detection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and to increase the granularity of gas concentration measurements, a quickly deployable, mobile measurement device is needed. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHG emissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, the use of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone. Alternatively, we present an adaptive strategy, which suggests sampling points based on an artificial potential field (APF). The results of real-world experiments demonstrate the feasibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas. T2 - GHGT 11 - Greenhouse gas control technologies conference CY - Kyoto, Japan DA - 18.11.2012 KW - Gas-sensitive micro-drone KW - Gas distribution mapping KW - Sensor planning KW - Artificial potential field KW - CCS PY - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-289119 SN - 1876-6102 VL - 37 SP - 4182 EP - 4190 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-28911 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Schnürmacher, M. A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias A1 - Schiller, J.H. T1 - A probabilistic gas patch path prediction approach for airborne gas ource localization in non-uniform wind fields N2 - 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 for GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms. T2 - ISOEN 2013 - 15th International symposium on olfaction and electronic nose CY - Deagu, South Korea DA - 02.07.2013 KW - Autonomous micro UAV KW - Chemical and wind sensing KW - Gas source localization KW - Particle filter PY - 2013 IS - Symposia / Applications of remote and local gas sensing ... SP - 15 EP - 16 AN - OPUS4-28878 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Matsukura, H. A1 - Neumann, Patrick P. A1 - Schaffernicht, E. A1 - Ishida, H. A1 - Lilienthal, A. J. ED - Rudnitskaya, A. T1 - Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network N2 - Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques. T2 - 19th International Symposium on Olfaction and Electronic Nose CY - Aveiro, Portugal DA - 29.05.2022 KW - Gas Distribution Mapping KW - Spatial Interpolation KW - Deep Learning KW - Super-Resolution KW - Sensor Network PY - 2022 SN - 978-1-6654-5860-3 U6 - https://doi.org/10.1109/isoen54820.2022.9789555 SP - 1 EP - 3 PB - IEEE CY - USA AN - OPUS4-54955 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Kotlyar, O. A1 - Schaffernicht, E. A1 - Fan, H. A1 - Matsukura, H. A1 - Ishida, H. A1 - Neumann, Patrick P. A1 - Lilienthal, A. J. ED - Tardioli, D. ED - Matellán, V. ED - Heredia, G. ED - Silva, M. F. ED - Marques, L. T1 - Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping N2 - To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model. T2 - ROBOT2022: Fifth Iberian Robotics Conference CY - Zaragoza, Spain DA - 22.11.2022 KW - Gas Distribution Mapping KW - Spatial Interpolation KW - Sequential Learning KW - Convolutional LSTM PY - 2023 SN - 978-3-031-21061-7 U6 - https://doi.org/10.1007/978-3-031-21062-4_15 SP - 178 EP - 188 PB - Springer International Publishing AN - OPUS4-56414 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. T1 - Using Redundancy in a Sensor Network to Compensate Sensor Failures N2 - Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks. T2 - IEEE SENSORS 2021 CY - Online meeting DA - 31.10.2021 KW - Environmental monitoring KW - Wireless sensor network KW - Sensor placement KW - Machine learning PY - 2021 SN - 978-1-7281-9501-8 U6 - https://doi.org/10.1109/sensors47087.2021.9639479 SP - 1 EP - 4 PB - IEEE AN - OPUS4-53939 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. A1 - Poikkimäki, M. A1 - Kangas, A. A1 - Säämänen, A. ED - Kourkoulis, S. K. T1 - Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory N2 - The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Poros, Greece DA - 20.09.2022 KW - Low-cost KW - Dust sensor KW - Sensor network KW - Sensor drift PY - 2022 SP - 1 EP - 2 CY - Athens, Greece AN - OPUS4-55909 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Neumann, Patrick P. A1 - Schnürmacher, M. A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias A1 - Schiller, J.H. T1 - A probabilistic gas patch path prediction approach for airborne gas source localization in non-uniform wind fields N2 - 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. KW - Autonomous micro UAV KW - Chemical and wind sensing KW - Gas source localization KW - Particle filter PY - 2014 U6 - https://doi.org/10.1166/sl.2014.3168 SN - 1546-198X SN - 1546-1971 VL - 12 IS - 6/7 SP - 1113 EP - 1118 PB - American Scientific Publishers (ASP) CY - Stevenson Ranch, CA, USA AN - OPUS4-31525 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias T1 - From insects to micro air vehicles - a comparison of reactive plume tracking strategies N2 - Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes, and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state-of-the-art robotics systems. As a step toward practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail toward the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real-world environments. T2 - IAS13 - 13th International conference on intelligent autonomous systems CY - Padova, Italy DA - 2014-07-15 KW - Autonomous micro UAV KW - Mobile robot olfaction KW - Gas source localization KW - Reactive plume tracking KW - Biologically inspired robots PY - 2016 SN - 978-3-319-08338-4; 978-3-319-08337-7 U6 - https://doi.org/10.1007/978-3-319-08338-4_110 SN - 2194-5357 SP - 1533 EP - 1548 PB - Springer Verlag CY - Berlin, Germany AN - OPUS4-31526 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Bennetts, V. H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias T1 - From Insects to Micro Air Vehicles - A Comparison of Reactive Plume Tracking Strategies N2 - Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state of the art robotics systems. As a step towards practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail towards the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real world environments. T2 - 13th International Conference on Intelligent Autonomous Systems (IAS) CY - Padova, Italy DA - 15.07.2014 KW - Autonomous micro UAV KW - Mobile robot olfaction KW - Gas source localization KW - Reactive plume tracking KW - Biologically inspired robots PY - 2014 SP - 1 EP - 12 AN - OPUS4-43920 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Säämänen, A. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. ED - Zemčík, R. ED - Krystek, J. T1 - High-quality meets low-cost: Approaches for hybrid-mobility sensor networks N2 - 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. T2 - 36th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Pilsen, Czech Republic DA - 24.09.2019 KW - Mobile robot olfaction KW - Air quality monitoring KW - Wireless sensor network KW - Gas distribution mapping KW - Occupational health PY - 2020 U6 - https://doi.org/10.1016/j.matpr.2020.05.799 VL - 32 SP - 250 EP - 253 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-51108 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hüllmann, Dino A1 - Neumann, Patrick P. A1 - Lilienthal, A. J. T1 - Gas Dispersion Fluid Mechanics Simulation for Large Outdoor Environments N2 - 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. T2 - 36th Danubia Adria Symposium on Advances in Experimental Mechanics CY - Pilsen, Czech Republic DA - 24.09.2019 KW - Gas dispersion simulation KW - CFD KW - Gas tomography PY - 2019 SN - 978-80-261-0876-4 SP - 49 EP - 50 CY - Pilsen, Czech Republic AN - OPUS4-49224 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hüllmann, Dino A1 - Neumann, Patrick P. A1 - Scheuschner, Nils A1 - Bartholmai, Matthias A1 - Lilienthal, A.J. T1 - Experimental Validation of the Cone-Shaped Remote Gas Sensor Model N2 - 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. T2 - IEEE Sensors 2019 CY - Montreal, Canada DA - 27.10.2019 KW - Remote gas sensor model KW - TDLAS KW - Gas dispersion simulation PY - 2019 SN - 978-1-7281-1634-1 SP - 104 EP - 107 PB - IEEE AN - OPUS4-49548 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Hüllmann, Dino A1 - Kohlhoff, Harald A1 - Bartholmai, Matthias A1 - Bennetts, V. H. A1 - Lilienthal, A. J. T1 - Remote Gas Sensing with Multicopter-Platforms N2 - 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. T2 - Zweites Innovationsforum "Autonome, mobile Dienste; Services für Mobilität" CY - Berlin, Germany DA - 04.06.2019 KW - Tunable Diode Laser Absorption Spectroscopy (TDLAS); UAV-REGAS KW - Localization of gas sources KW - Mobile Robot Olfaction KW - Nano UAV Swarm KW - Tomographic reconstruction of gas plumes PY - 2019 UR - http://modisem.de/files/Ereignisse/2019-06/Innovationsforum_Downloads/IF2_2019_Tagungsband_WEB.pdf SN - 978-3-942709-22-4 N1 - Tagungsband auf Deutsch, Beitrag auf Englisch. VL - 2019 SP - 24 EP - 34 AN - OPUS4-48699 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Neumann, Patrick P. A1 - Kohlhoff, Harald A1 - Hüllmann, Dino A1 - Krentel, Daniel A1 - Kluge, Martin A1 - Dzierliński, M. A1 - Lilienthal, A. J. A1 - Bartholmai, Matthias T1 - Aerial-based gas tomography – from single beams to complex gas distributions N2 - 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). KW - Aerial robot olfaction KW - Mobile robot olfaction KW - Gas tomography KW - TDLAS KW - Plume PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-487843 SP - 1 EP - 16 PB - Taylor & Francis CY - London AN - OPUS4-48784 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hüllmann, Dino A1 - Neumann, Patrick P. A1 - Monroy, J. A1 - Lilienthal, A. J. T1 - A realistic remote gas sensor model for three-dimensional olfaction simulations N2 - 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. T2 - 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) CY - Fukuoka, Japan DA - 26.05.2019 KW - Remote gas sensor KW - Gas detector KW - TDLAS KW - Sensor modelling PY - 2019 SN - 978-1-5386-8327-9 SN - 978-1-5386-8328-6 U6 - https://doi.org/10.1109/ISOEN.2019.8823330 SP - 1 EP - 3 PB - IEEE AN - OPUS4-48919 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Lilienthal, A. J. A1 - Neumann, Patrick P. ED - Zemčík, R. ED - Krystek, J. T1 - Boosting a Low-Cost Sensor Network with Mobile High-Quality Sensors N2 - 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. Recent developments in sensor technology enable cost-efficient gas monitoring in real-time for long-term intervals. This knowledge of contaminant distribution inside the industrial environment would provide means for better and more economic control of air impurities, e.g., the possibility to regulate the workspace’s ventilation exhaust locations, can reduce the concentration of airborne contaminants by 50%. This paper describes a concept proposed in the project “Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios“ (RASEM). RASEM aims to bring together the benefits of both – low- and high-cost – measuring technologies: A stationary network of low-cost sensors shall be augmented by mobile units carrying high-quality sensors. Additionally, RASEM will develop procedures and algorithms to map the distribution of gases and particles in industrial environments. T2 - 36th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Pilsen, Czech Republic DA - 24.09.2019 KW - RASEM KW - Sensor network KW - Low-cost KW - Occupational health KW - Mobile Robot Olfaction PY - 2019 SP - 61 EP - 62 CY - Plzeň AN - OPUS4-49630 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bennetts, V.H. A1 - Kucner, T.P. A1 - Schaffernicht, E. A1 - Neumann, Patrick P. A1 - Fan, H. A1 - Lilienthal, A.J. T1 - Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots N2 - 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. T2 - IEEE International Conference on Robotics and Automation (ICRA) CY - Singapore DA - 29.05.2017 KW - Mapping KW - Field Robots KW - Environment Monitoring and Management KW - Aerial Systems KW - Perception and Autonomy PY - 2017 SN - 978-1-5090-4632-4 SP - 1 EP - 7 PB - IEEE AN - OPUS4-40544 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Kohlhoff, Harald A1 - Hüllmann, Dino A1 - Lilienthal, A.J. A1 - Kluge, Martin T1 - Bringing Mobile Robot Olfaction to the Next Dimension – UAV-based Remote Sensing of Gas Clouds and Source Localization N2 - 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. T2 - IEEE International Conference on Robotics and Automation (ICRA) CY - Singapore DA - 29.05.2017 KW - Tunable Diode Laser Absorption Spectroscopy (TDLAS) KW - 3-axis gimbal KW - Mobile Robot Olfaction KW - Tomographic reconstruction of gas plumes KW - Localization of gas sources KW - Aerial platform PY - 2017 SN - 978-1-5090-4632-4 SP - 3910 EP - 3916 PB - IEEE AN - OPUS4-40545 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bennetts, V.H. A1 - Kucner, T.P. A1 - Schaffernicht, E. A1 - Neumann, Patrick P. A1 - Fan, H. A1 - Lilienthal, A.J. T1 - Probabilistic air flow modelling using turbulent and laminar characteristics for ground and aerial robots N2 - 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. T2 - IEEE International Conference on Robotics and Automation (ICRA) CY - Singapore DA - 29.05.2017 KW - Mapping KW - Field robots KW - Environment monitoring and management KW - Aerial systems KW - Perception and autonomy PY - 2017 U6 - https://doi.org/10.1109/LRA.2017.2661803 SN - 2377-3766 VL - 2 IS - 2 SP - 1117 EP - 1123 PB - IEEE AN - OPUS4-39218 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hüllmann, Dino A1 - Paul, Niels A1 - Kohlhoff, Harald A1 - Neumann, Patrick P. A1 - Lilienthal, A. J. ED - Eberhardsteiner, J. ED - Růžička, M. ED - Cosmi, F. T1 - Measuring rotor speed for wind vector estimation on multirotor aircraft N2 - 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. T2 - 34th Danubia Adria Symposium on Advances in Experimental Mechanics CY - Trieste, Italy DA - 19.09.2017 KW - Wind vector estimation KW - Rotor speed KW - UAV KW - Tachometer PY - 2018 UR - https://www.sciencedirect.com/science/article/pii/S2214785318321114 U6 - https://doi.org/10.1016/j.matpr.2018.08.139 SN - 2214-7853 VL - 5 IS - 13 SP - 26703 EP - 26708 PB - Elsevier CY - Amsterdam, Netherlands AN - OPUS4-47097 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Hüllmann, Dino A1 - Krentel, Daniel A1 - Kluge, Martin A1 - Kohlhoff, Harald A1 - Lilienthal, A. J. T1 - Gas Tomography Up In The Air! N2 - 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. T2 - IEEE Sensors 2018 CY - New Delhi, India DA - 28.10.2018 KW - Aerial robot KW - TDLAS KW - Gas tomography KW - Plume PY - 2018 SN - 978-1-5386-4707-3 SP - 396 EP - 398 PB - IEEE AN - OPUS4-46477 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -