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 - Haratsu, T. A1 - Sakaue, M. A1 - Matsukura, H. A1 - Neumann, Patrick P. A1 - Ishida, H. ED - Tardioli, D. ED - Matellán, V. ED - Heredia, G. ED - Silva, M. F. ED - Marques, L. T1 - Simulating a Gas Source Localization Algorithm with Gas Dispersion Produced by Recorded Outdoor Wind T2 - Proceedings of the Fifth Iberian Robotics Conference (ROBOT2022) N2 - This paper reports the use of the first gas dispersion simulator capable of introducing large wind fluctuations into simulations. The proposed simulator enables testing of a modification made to a gas source localization algorithm in a realistic scenario in order to study how the change affects it. Gas source localization in an outdoor environment is a challenging task mainly due to the complexity of the gas spread caused by the unpredictable nature of constantly changing wind. Therefore, a novel use of outdoor wind in developing a gas source localization system by simulation is presented in this paper. To consider the characteristic of sudden but large and unpredictable changes in wind direction, we propose to use recorded outdoor wind to simulate a realistic outdoor gas dispersion which has been done for the first time to the best of our knowledge. With the use of this simulator, we have tested a modification to a mobile robot-based gas source localization algorithm. Multiple simulations of the modified and the original particle filter-based algorithm have been done to study the effect of the tested modification. The results showed that a small difference in the algorithm can greatly impact the results. From this study, we show that the use of simulation consisting of the necessary traits to evaluate outdoor gas source localization, has the potential to accelerate the development of a reliable localization system. T2 - ROBOT2022: Fifth Iberian Robotics Conference CY - Zaragoza, Spain DA - 23.11.2022 KW - Gas Dispersion Simulator KW - Gas Source Localization KW - Mobile Robot Olfaction KW - Particle Filter PY - 2023 SN - 978-3-031-21061-7 DO - https://doi.org/10.1007/978-3-031-21062-4_9 SP - 105 EP - 116 PB - Springer AN - OPUS4-56416 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Neumann, Patrick P. A1 - Hirschberger, Paul A1 - Bartholmai, Matthias ED - Holl, H. T1 - Flying ant robot for chemical trail detection and localization JF - Materials Today: Proceedings N2 - This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize a 5 cm wide chemical trail, a detection criterion was developed that uses only relative changes in the transient phase of the sensor response. The reduction in signal strength dependence improves the robustness of its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot can correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future. T2 - 37th Danubia - Adria Symposium on Advances in Experimental Mechanics CY - Linz, Austria DA - 21.09.2021 KW - Aerial robot KW - Trail following KW - Trail detection KW - Localization PY - 2022 DO - https://doi.org/10.1016/j.matpr.2022.02.594 SN - 2214-7853 VL - 62 SP - 2462 EP - 2465 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-54507 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Hirschberger, Paul A1 - Bartholmai, Matthias T1 - Flying Ant Robot – Aerial Chemical Trail Detection and Localization T2 - Proceedings of the IEEE Sensors 2021 N2 - This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize the chemical trail, a novel detection criterion was developed that uses only relative changes in the transient phase of the sensor response, making it more robust in its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot is able to correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future. T2 - IEEE Sensors 2021 CY - Online meeting DA - 31.10.2021 KW - Nano aerial robot KW - Trail following KW - Trail detection KW - Localization PY - 2021 DO - https://doi.org/10.1109/sensors47087.2021.9639857 SP - 1 EP - 4 PB - IEEE AN - OPUS4-53933 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 - JOUR A1 - Neumann, Patrick P. A1 - Hüllmann, Dino A1 - Bartholmai, Matthias ED - Pastramă, Ş. D. ED - Constantinescu, D. M. T1 - Concept of a gas-sensitive nano aerial robot swarm for indoor air quality monitoring JF - Materials Today: Proceedings N2 - 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. T2 - 35th Danubia Adria Symposium on Advances in Experimental Mechanics CY - Sinaia, Romania DA - 25.09.2018 KW - Nano aerial robot KW - UAV KW - Swarm KW - Indoor air quality KW - Monitoring KW - Concept PY - 2019 DO - https://doi.org/10.1016/j.matpr.2019.03.151 SN - 2214-7853 VL - 12 IS - 2 SP - 470 EP - 473 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-48055 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 - JOUR A1 - Neumann, Patrick P. A1 - Hirschberger, Paul A1 - Bartholmai, Matthias ED - Zemčík, R. ED - Krystek, J. T1 - Influence of rotor downwash on vertically displaced nanobots in flight JF - Materials Today: Proceedings N2 - One challenge associated with navigating a nano aerial robot swarm indoors in unstructured environments is, i.a., the limited air space. To avoid an over-regulation of the available indoor air space (e.g., prohibit copters to fly above each other), a safety region around each copter must be defined. In this paper, we investigate the impact of the downwash of a nano-drone on the stability of another nearby nano-drone. In the experiments, we found out that this downwash has a negative influence of a second nano-drone in a distance of around 1 m vertically and 0.2 m horizontally. Based on the obtained data, we developed a model describing the above-mentioned safety region to enable a safe operation of the swarm in these environments with fewer constraints. T2 - 36th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Pilsen, Czech Republic DA - 24.09.2019 KW - Mobile Robot Olfaction KW - Nano aerial robot KW - Swarm KW - Collision-free navigation KW - Safety region model PY - 2020 DO - https://doi.org/10.1016/j.matpr.2020.03.047 VL - 32 IS - 2 SP - 108 EP - 111 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-50673 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -