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. 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 - Sensor network KW - Dust sensor KW - Sensor drift PY - 2022 AN - OPUS4-55908 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 - Localization of gas sources KW - Mobile Robot Olfaction KW - Nano UAV Swarm KW - Tomographic reconstruction of gas plumes KW - Tunable Diode Laser Absorption Spectroscopy (TDLAS) KW - UAV-REGAS PY - 2019 AN - OPUS4-48167 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. T1 - Mapping the Distribution of Air Pollutants with Mobile Robots and Stationary Sensors N2 - Precise knowledge about the distribution of air pollutants is necessary to develop plausible occupational health measures. Combinatory systems, consisting of mobile robots and stationary sensors, can be effective solutions for the coverage of large measurement areas. However, further research is needed to fully understand their performance in comparison to traditional sensing strategies. Therefore, multiple sensor networks layouts will be set up in a simulation environment as well as in real industrial environments. Models for distribution mapping will be developed and evaluated to investigate the performance and opportunities of hybrid-mobility sensor networks for the task of distribution mapping. T2 - AASS Seminar CY - Online meeting DA - 24.09.2020 KW - Mobile Robot Olfaction KW - Aerial Robot Olfaction KW - Wireless Sensor Network KW - Occupational Health PY - 2020 AN - OPUS4-51399 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. T1 - Breaking the wall of air pollution monitoring N2 - Air pollution in industrial environments is a major risk. Precise knowledge about the distribution of air pollutants is necessary to develop plausible occupational health measures. Combinatory systems, consisting of mobile robots and stationary sensors, can be effective solutions for the coverage of large measurement areas. T2 - Falling Walls Adlershof CY - Online meeting DA - 02.10.2020 KW - Mobile Robot Olfaction KW - Aerial Robot Olfaction KW - Occupational Health KW - Wireless Sensor Network PY - 2020 AN - OPUS4-51400 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 - Bartholmai, Matthias A1 - Lilienthal, A. J. T1 - Boosting a Low-Cost Sensor Network with Mobile High-Quality Sensors N2 - The Motivation of RASEM (Robot-assisted Environmental Monitoring): Monitoring of the air quality in industrial environments is inevitable to meet safety Standards. Because of economic and practical reasons, measurements are carried out sparsely in terms of time and space. Newest developments on the field of low cost sensor technology enable cost efficient long term monitoring of gases and dust 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 AN - OPUS4-49632 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Schaffernicht, Erik A1 - Lilienthal, Achim J. T1 - Gas Distribution Mapping With Radius-Based, Bi-directional Graph Neural Networks (RABI-GNN) N2 - Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models. T2 - International Symposium on Olfaction and Electronic Nose (ISOEN) CY - Grapevine, TX, USA DA - 12.05.2024 KW - Gas distribution mapping KW - Spatial interpolation KW - Graph neural networks KW - Mobile robot olfaction PY - 2024 AN - OPUS4-60106 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Lilienthal, A. J. A1 - Schaffernicht, E. A1 - Neumann, Patrick P. T1 - DeepGDM: Deep Learning for Gas Distribution Mapping N2 - Gas distribution mapping (GDM) is a valuable tool for monitoring the distribution of gases in various applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This presentation gives an overview of the different approaches to GDM and motivate the use of a deep-neural network-based approach. As access to ground truth representations of gas distributions remains a challenge in GDM research, an approach for the simulation of realistic-shaped synthetic gas plumes is described, which was used for training Gas Distribution Decoder, a deep neural network for spatial interpolation of spatially sparse gas measurements. T2 - AASS Seminar CY - Örebro, Sweden DA - 08.06.2023 KW - Deep learning KW - Mobile robot olfaction KW - Environmental monitoring PY - 2023 AN - OPUS4-57635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Johann, Sergej A1 - Kohlhoff, Harald A1 - Neumann, Patrick P. T1 - Revisiting Environmental Sensing Nodes: Lessons Learned and Way Forward N2 - Setting up sensors for the purpose of environmental monitoring should be a matter of days, but often drags over weeks or even months, preventing scientists from doing real research. Additionally, the newly developed hardware and software solutions are often reinventing existing wheels. In this short paper, we revisit the design of our environmental sensing node that has been monitoring industrial areas over a span of two years. We share our findings and lessons learned. Based on this, we outline how a new generation of sensing node(s) can look like. T2 - SMSI 2023 Conference Sensor and Measurement Science International CY - Nuremberg, Germany DA - 08.05.2023 KW - Sensing node KW - Sensor network KW - Environmental monitoring KW - Low-cost KW - LoRaWAN PY - 2023 AN - OPUS4-57455 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -