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Leaking methane (CH4) from infrastructures, such as pipelines and landfills, is critical for the environment but can also pose a safety risk. To enable a fast detection and localization of These kind of leaks, we developed a novel robotic platform for aerial remote gas sensing - the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS).
This paper presents a sampling strategy for mobile gas sensors. Sampling points are selected using a modified artificial potential field (APF) approach, which balances multiple criteria to direct sensor measurements towards locations of high mean concentration, high concentration variance and areas for which the uncertainty about the gas distribution model is still large. By selecting in each step the most often suggested close-by measurement location, the proposed approach introduces a locality constraint that allows planning suitable paths for mobile gas sensors. Initial results in simulation and in real-world experiments with a gas-sensitive micro-drone demonstrate the suitability of the proposed sampling strategy for gas distribution mapping and its use for gas source localization.
This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization.
Geogenic gases from natural sources, carbon dioxide (CO2) from a geological repository (carbon capture and storage - CCS) or a leaking gas pipeline can present serious risks in industrial and urban areas. To extend the lead time for risk treatment in such critical regions, reliable detection of gases within the shallow subsurface is required to observe critical gas accumulations before degassing into the atmosphere.
A near real-time monitoring approach is introduced to determine the volumetric expansion of a leakinggas in the subsurface. Considering the pressure relaxation with the ambient air pressure, the approach enables the forecasting of the final size of a pressurized gas body in terms of characteristic lengths. According to theoretical basics, such a characteristic length, which enables us to perform a gas (safety) measurement based on a purely geometrical measure, behaves independently of subsurface properties,i.e., it enables a reliable quantification of the escaping gas irrespective of its heterogeneous or changingflow path distribution. A field test for a 10 l/min pinhole leakage injected into a 10 m long, 0.4 m wide, 0.95 m deep soil-filled trench that was equipped with linear sensors shows the lateral-vertical volumetric gas expansion along these sensors, and demonstrates the applicability of the characteristic length approach.
Geogenic gases such as CH4 or CO2 from natural sources, gases (CCS-CO2, H2, Natural
gas, City gas …) from a geological repository, or a leaking gas pipeline can present serious
risks in industrial and urban areas where the density of infrastructural elements increases as
well as above and below ground. To extend the lead time for risk treatment in such critical
regions, reliable detection of gases within the shallow subsurface is required to observe
critical gas accumulations before degassing into the atmosphere.
A near real-time monitoring approach is introduced to determine the volumetric expansion of
such a gas escaping from a leak in the subsurface. Considering the pressure relaxation with
the ambient air pressure, the approach enables the forecasting of the final size of a
pressurized gas body in terms of characteristic lengths. According to theoretical basics, such
a characteristic length, which allows to perform a gas (safety) measurement based on a
purely geometrical measure, behaves independently of subsurface properties, i.e., it enables
a reliable quantification of the escaping gas, irrespective of its heterogeneous flow path
distribution. A field test for a 10 l/min pinhole leakage of CO2 injected in an unsaturated Chernozemic soil (agricultural test field Bad Lauchstädt)that was equipped with linear gas sensors demonstrates the lateral-vertical volumetric gas expansion along the environment of these gas sensors, and confirms the applicability of the new characteristic length approach.
Concept for investigating mechanical and thermal impacts on distributed subsurface gas monitoring
(2016)
A multifunctional sensor in line shape was developed and introduced in previous work for measuring of gas concentrations, temperature change, and strain. A current field study focuses on a spatially distributed monitoring of subsurface CO 2 gas storage sites in near real time.
Mechanical impacts, e.g., caused by construction work, denudation, and seismic activity, can affect the integrity of underground gas storage sites. Thermal or moisture impacts, e.g., caused by weather conditions, can influence the gas Distribution behavior. In this paper, we briefly describe the setup of a CO 2 injection soil test field. This setup contains actuating elements for the investigation of mechanical and thermal impacts on distributed subsurface gas monitoring. A concept is given for evaluating these impacts and first experimental results are presented.
A multifunctional sensor in line shape was developed and introduced in previous work for measuring of gas concentrations, temperature change, and strain. A current field study focuses on a spatially distributed monitoring of subsurface CO2 gas storage sites in near real time.
Mechanical impacts, e.g., caused by construction work, denudation, and seismic activity, can affect the integrity of underground gas storage sites. Thermal or moisture impacts, e.g., caused by weather conditions, can influence the gas distribution behavior. In this paper, we briefly describe the setup of a CO2 injection soil test field. This setup contains actuating elements for the investigation of mechanical and thermal impacts on distributed subsurface gas monitoring. A concept is given for evaluating these impacts and first experimental results are presented.
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.
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.