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 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 - 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 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 - CONF A1 - Lohrke, Heiko A1 - Kohlhoff, Harald A1 - Erdmann, Jessica A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. T1 - Combined GNSS/Visual Inter-Robot Tracking Method for Open-Path Gas Tomography N2 - Methane emissions are a significant environmental and safety concern, yet many gas tomography systems intended to measure them require manual alignment, reliable high-bandwidth links, or fiducials. We present a lightweight, fully autonomous framework enabling line-of-sight inter-robot Tunable Diode Laser Absorption Spectroscopy (TDLAS) measurements without such constraints. A sensor robot equipped with a gimbal-mounted TDLAS unit tracks a reflector robot bearing an illuminated, color-controllable target. Coarse localization is achieved via RTK-GNSS, with vision-based fine tracking and passive time synchronization handled onboard. The system, based on off-the-shelf Pixhawk controllers and ArduPilot firmware, was validated in a 15m × 7m outdoor trial. Despite GNSS inaccuracies and deliberate occlusion by a methane-filled bag, the system retained lock, recovered from visual loss in under one second, and captured a 2800 ppm·m plume signature. These results demonstrate robust, scalable methane sensing for mobile gas tomography or standalone leak detection. Core components are released open-source to support future deployment. T2 - IEEE SENSORS 2025 CY - Vancouver, BC, Kanada DA - 19.10.2025 KW - Gas tomography KW - Tracking KW - Robot KW - Methane KW - TDLAS PY - 2025 SN - 979-8-3315-4467-6 DO - https://doi.org/10.1109/SENSORS59705.2025.11330519 SP - 1 EP - 4 PB - IEEE CY - Piscataway, NJ, USA AN - OPUS4-65615 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -