Exploration and localization of a gas source with MOX gas sensors on a mobile robot — A Gaussian regression bout amplitude approach

Vuka, Mikel; Schaffernicht, Erik; Schmuker, Michael; Bennetts, Victor Hernandez; Amigoni, Francesco; Lilienthal, Achim J.

doi:10.1109/isoen.2017.7968898

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…In research presented by Vuka and coworkers [ 164 ], the six metal oxide sensors and an ultrasonic anemometer wind sensor were employed to find the origin of hexane gas. The sensors were integrated in a mobile robot in a Gaussian regression domain approach with an active search algorithm (gas source localization, GSL) learned to find the gas-polluting gas source.…”

Section: Application Of Moxs E-noses For Environmental Monitoringmentioning

confidence: 99%

“…However, some researchers have estimated LOD in MOXs sensors using alternative approaches [ 174 ]. In [ 164 ], ten MOX sensors (FIS SB-500-12 and FIGARO TGS 3870-A04, five of each type) were used to detect CO in varying air humidity (20–80% RH) at a constant airflow of 1 l/min. It was shown that at low concentrations (less than 50 ppm), the MOX signals could be fitted with linear models and that the homoscedasticity condition was met; moreover, cross-sensitivity to moisture was the major factor in reducing the LOD of MOX sensors.…”

Section: Application Of Moxs E-noses For Environmental Monitoringmentioning

confidence: 99%

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Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors

Khorramifar,

Karami,

Lvova

et al. 2023

Sensors

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Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

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Section: Application Of Moxs E-noses For Environmental Monitoringmentioning

confidence: 99%

Section: Application Of Moxs E-noses For Environmental Monitoringmentioning

confidence: 99%

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors

Khorramifar,

Karami,

Lvova

et al. 2023

Sensors

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“…Simulated experiments were conducted to find the optimal values of the parameters, b and g. The environments are shown in Figure 18. The geometric maps used for the experiments are of real-world environments in which we carried out different mobile robot olfaction experiments in our previous work: (1) a campus corridor with stairs and sitting places (Arain et al, 2015a(Arain et al, , 2016Polvara et al, 2018;Vuka et al, 2017); (2) an outdoor forest area (Arain et al, 2015a;Hernandez Bennetts et al, 2013; (3) an industrial foundry where bronze bearings are produced (Schaffernicht et al, 2017); and (4) an industrial foundry where high-volume products are being cast (Hernandez Bennetts et al, 2016).…”

Section: Parameter Selection For Multi-criteria Weightsmentioning

confidence: 99%

Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot

Arain

Bennetts

Schaffernicht

et al. 2020

The International Journal of Robotics Research

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Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our “Autonomous Remote Methane Explorer” ([Formula: see text]) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated an [Formula: see text] prototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route.

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“…Metal oxide semiconductor (MOX) gas sensors are characterized by high sensitivity, fast response, and low cost. Those characteristics make their application very promising in different fields, such as agri-food quality and safety [1][2][3][4][5], environmental monitoring [6][7][8], home security [9][10][11], and human health [12][13][14] among the most investigated. Although these features make MOX sensors one of the most promising technologies of recent years, their diffusion is limited due to some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

k-NN and k-NN-ANN Combined Classifier to Assess MOX Gas Sensors Performances Affected by Drift Caused by Early Life Aging

et al. 2020

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The drift of metal oxide semiconductor (MOX) chemical sensors is one of the most important topics in this field. The work aims to test the performance of MOX gas sensors over the aging process. Firstly, sensors were tested with ethanol to understand their behavior and response changes. In parallel, beers with different alcoholic content were analyzed to assess what happened in a real application scenario. With ethanol analysis, it was possible to quantify drift of the baseline of the sensors and changes that could affect their responses over time (from day 1 to day 51). Conversely, the beer dataset has been exploited to evaluate how two different classifiers perform the classification task based on the alcohol content of the samples. A hybrid k-nearest neighbors artificial neural network (k-NN-ANN) approach and “standard” k-NN were used to evaluate to distinguish among the samples when the measures were affected by drift. To achieve this goal, data acquired from day one to day six were used as training to predict data collected up to day 51. Overall, performances of the two methods were similar, even if the best result in terms of accuracy is reached by k-NN-ANN (96.51%).

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Exploration and localization of a gas source with MOX gas sensors on a mobile robot — A Gaussian regression bout amplitude approach

Cited by 12 publications

References 7 publications

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors

Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot

k-NN and k-NN-ANN Combined Classifier to Assess MOX Gas Sensors Performances Affected by Drift Caused by Early Life Aging

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