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 T2 - 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Proceedings 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 DO - 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 - Neumann, Patrick P. A1 - Hüllmann, Dino A1 - Winkler, Nicolas P. A1 - Schugardt, Jan ED - Rudnitskaya, A. T1 - Remote Drone-to-Drone Gas Sensing: A Feasibility Study T2 - 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Proceedings N2 - 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. T2 - 19th International Symposium on Olfaction and Electronic Nose CY - Aveiro, Portugal DA - 29.05.2022 KW - Aerial robot KW - TDLAS KW - Inter-robot measurements KW - Gas tomography KW - Plume PY - 2022 SN - 978-1-6654-5860-3 DO - https://doi.org/10.1109/isoen54820.2022.9789627 SP - 1 EP - 3 PB - IEEE CY - USA AN - OPUS4-54926 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 T2 - ROBOT2022: Fifth Iberian Robotics Conference 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 DO - 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 T2 - 2021 IEEE SENSORS 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 DO - 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 - Kohlhoff, Harald A1 - Erdmann, Jessica A1 - Schaffernicht, Erik A1 - Lilienthal, Achim J. T1 - Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring T2 - SMSI 2021 Proceedings N2 - A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown. T2 - SMSI 2021 Conference: Sensor and Measurement Science International CY - Online meeting DA - 03.05.2021 KW - Wireless sensing node KW - Air pollution KW - Sensor network KW - Environmental monitoring PY - 2021 DO - https://doi.org/10.5162/SMSI2021/D3.5 VL - 2021 SP - 260 EP - 261 AN - OPUS4-52607 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 T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics - Extended abstracts 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 - CONF A1 - Neumann, Patrick P. A1 - Kohlhoff, Harald A1 - Johann, Sergej A1 - Erdmann, Jessica A1 - Winkler, Nicolas P. ED - Kourkoulis, S. K. T1 - The RASEM System: A Technical Overview T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics - Extended abstracts 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. 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 costefficient 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 paper, we detail the technical aspects of RASEM and introduce the mobile platforms used. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Poros, Greece DA - 20.09.2022 KW - RASEM KW - Dust sensor KW - Low-cost KW - Sensor network KW - Ground Robot KW - Aerial Robot PY - 2022 SP - 1 EP - 2 CY - Athens, Greece AN - OPUS4-55915 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 JF - Materials Today: Proceedings 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 DO - 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 - 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 T2 - Tagungsband des zweiten Innovationsforums: Autonome, mobile Dienste – Services für Mobilität 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 - 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 T2 - 36th Danubia - Adria Symposium on Advances in Experimental Mechanics - Extended abstracts 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 -