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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.
An unmanned micro aircraft (UAV) is equipped with an open-path gas detector to localise gas sources and to generate gas distribution maps. The gas detector is based on the tunable diode laser absorption spectroscopy (TDLAS) and attached to a 3-axis aerial stabilisation gimbal. Sensor validation experiments and first gas tomography reconstructions are shown.
Leaking methane (CH4) from infrastructures, like pipelines, industrial complexes and landfills, or accidental hazardous goods transports is critical for the environment, but can also pose a risk to human life. To enable a fast detection and localisation of these kind of leaks, we developed a novel robotic platform for aerial remote gas sensing. The platform combines an unmanned micro aircraft (UAV), a 3-axis aerial stabilisation gimbal and an open-path gas detector based on the tunable diode laser absorption spectroscopy (TDLAS).
In industrial environments, airborne by-products such as dust and (toxic) gases, constitute a major risk for the worker’s health. Major changes in automated processes in the industry lead to an increasing demand for solutions in air quality management. Thus, occupational health experts are highly interested in precise dust and gas distribution models for working environments. For practical and economic reasons, high-quality, costly measurements are often available for short time-intervals only. Therefore, current monitoring procedures are carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only. Real-time knowledge of contaminant distributions inside the working environment would also provide means for better and more economic control of air impurities. For example, the possibility to regulate the workspace’s ventilation exhaust locations can reduce the concentration of airborne contaminants by 50%.
To improve the occupational health and safety of (industrial) workplaces, this work aims for developing a swarm of gas-sensitive aerial nano robots for monitoring indoor air quality and for localizing potential emission sources.
In industrial environments, airborne by-products such as dust and (toxic) gases, constitute a major risk for the worker’s health. Major changes in automated processes in the industry lead to an increasing demand for solutions in air quality management. Thus, occupational health experts are highly interested in precise dust and gas distribution models for working environments. For practical and economic reasons, high-quality, costly measurements are often available for short time-intervals only. Therefore, current monitoring procedures are carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only. Real-time knowledge of contaminant distributions inside the working environment would also provide means for better and more economic control of air impurities. For example, the possibility to regulate the workspace’s ventilation exhaust locations can reduce the concentration of airborne contaminants by 50%.
To improve the occupational health and safety of (industrial) workplaces, this work aims for developing a swarm of gas-sensitive aerial nano robots for monitoring indoor air quality and for localizing potential emission sources.