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Organisationseinheit der BAM
Small unmanned aircraft are used increasingly as flying sensor platforms to perform tasks such as gas distribution mapping or environmental monitoring. Sometimes, these applications require knowledge of the ambient wind field.
In general, it would be interesting to avoid any additional hardware, both on ground and on the aircraft. Here, we present an approach to estimate the wind vector just by exploiting the state variables of the aircraft.
The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. Here we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios.
An equation for wind vector estimation using a multirotor aircraft as a flying anemometer is shown. To compute the wind vector an estimate of the thrust of the aircraft is required, which is related to the rotational speed of the rotors. Hence, a sensing system for the rotational speed using phototransistors is presented.
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 this paper, we present an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS) combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present first results showing the 2D plume reconstruction capabilities of the system under realistic conditions.