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Arain, M. A., Cirillo, M., Hernandez Bennetts, V., Schaffernicht, E., Trincavelli, M. & Lilienthal, A. J. (2015). Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots. In: 2015 IEEE International Conference on Robotics and Automation (ICRA): . Paper presented at 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington, USA, May 26-30, 2015 (pp. 3428-3434). Washington, USA: IEEE Computer Society
Open this publication in new window or tab >>Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots
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2015 (English)In: 2015 IEEE International Conference on Robotics and Automation (ICRA), Washington, USA: IEEE Computer Society, 2015, p. 3428-3434Conference paper, Published paper (Refereed)
Abstract [en]

The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

Place, publisher, year, edition, pages
Washington, USA: IEEE Computer Society, 2015
Keyword
Sensor planning, mobile robot olfaction, remote gas sensing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-46796 (URN)10.1109/ICRA.2015.7139673 (DOI)000370974903063 ()978-1-4799-6923-4 (ISBN)
Conference
2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington, USA, May 26-30, 2015
Available from: 2015-11-25 Created: 2015-11-25 Last updated: 2018-01-10Bibliographically approved
Arain, M. A., Trincavelli, M., Cirillo, M., Schaffernicht, E. & Lilienthal, A. J. (2015). Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor. Sensors, 15(3), 6845-6871
Open this publication in new window or tab >>Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor
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2015 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 3, p. 6845-6871Article in journal (Refereed) Published
Abstract [en]

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2015
Keyword
Coverage planning; Mobile robot olfaction; Remote gas detection; Sensor planning; Surveillance robots
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-44407 (URN)10.3390/s150306845 (DOI)000354160900112 ()25803707 (PubMedID)2-s2.0-84928681961 (Scopus ID)
Available from: 2015-04-22 Created: 2015-04-22 Last updated: 2018-01-11Bibliographically approved
Hernandez Bennetts, V., Schaffernicht, E., Pomadera Sese, V., Lilienthal, A. J. & Trincavelli, M. (2014). A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems. In: Proceedings of the IEEE Sensors Conference 2014: . Paper presented at IEEE Sensors 2014. November 2-5, Valencia, Spain. IEEE conference proceedings
Open this publication in new window or tab >>A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems
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2014 (English)In: Proceedings of the IEEE Sensors Conference 2014, IEEE conference proceedings, 2014, p. -2049Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-40889 (URN)10.1109/ICSENS.2014.6985437 (DOI)
Conference
IEEE Sensors 2014. November 2-5, Valencia, Spain
Available from: 2015-01-12 Created: 2015-01-12 Last updated: 2018-01-11Bibliographically approved
Di Lello, E., Trincavelli, M., Bruyninckx, H. & De laet, T. (2014). Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System. Sensors, 14(7), 12533-12559
Open this publication in new window or tab >>Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System
2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 7, p. 12533-12559Article in journal (Refereed) Published
Abstract [en]

In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector.

Keyword
metal oxide semiconductor sensor, gas sensing, Bayesian inference
National Category
Chemical Sciences
Research subject
Chemistry
Identifiers
urn:nbn:se:oru:diva-36458 (URN)10.3390/s140712533 (DOI)000340035700069 ()2-s2.0-84904178639 (Scopus ID)
Note

Funding Agency:

KU Leuven OT project

Available from: 2014-09-05 Created: 2014-09-05 Last updated: 2017-12-05Bibliographically approved
Schaffernicht, E., Trincavelli, M. & Lilienthal, A. J. (2014). Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events. Sensor Letters, 12(6-7), 1142-1146
Open this publication in new window or tab >>Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events
2014 (English)In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6-7, p. 1142-1146Article in journal (Refereed) Published
Abstract [en]

In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of a quasi-continuous gas concentration, models the spatial distribution of gas events, for example detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian Inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting, a Bayesian approach is used with a beta distribution prior for the parameter μ that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e., will be a beta distribution as well, enabling a simple approach for sequential learning. To learn a map composed of beta distributions, we discretize the inspection area into a grid and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for MOX sensors and a photo ionization detector mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors.

Place, publisher, year, edition, pages
Valencia, California, US: American Scientific Publishers, 2014
Keyword
BERNOULLI DISTRIBUTION; BETA DISTRIBUTION; GAS DISTRIBUTION MAPPING; STATISTICAL MODELING
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-41205 (URN)10.1166/sl.2014.3189 (DOI)2-s2.0-84911444121 (Scopus ID)
Available from: 2015-01-13 Created: 2015-01-13 Last updated: 2017-10-03Bibliographically approved
Fonollosa, J., Rodriguez-Lujan, I., Trincavelli, M., Vergara, A. & Huerta, R. (2014). Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry. Sensors, 14(10), 19336-19353
Open this publication in new window or tab >>Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry
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2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 10, p. 19336-19353Article in journal (Refereed) Published
Abstract [en]

Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

Keyword
chemical sensors, open sampling systems, gas turbulence, dynamic chemical mixture, inhibitory support vector machine, gas chromatography
National Category
Chemical Sciences
Research subject
Chemistry
Identifiers
urn:nbn:se:oru:diva-39812 (URN)10.3390/s141019336 (DOI)000344455700076 ()25325339 (PubMedID)2-s2.0-84908530056 (Scopus ID)
Note

Funding Agencies:

U.S. Office of Naval Research (ONR) N00014-13-1-0205

California Institute for Telecommunications and Information Technology (CALIT2) 2014CSRO 136

Available from: 2014-12-16 Created: 2014-12-16 Last updated: 2017-12-05Bibliographically approved
Hernandez Bennetts, V., Schaffernicht, E., Pomadera Sese, V., Lilienthal, A. J., Marco, S. & Trincavelli, M. (2014). Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds. Sensors, 14(9), 17331-17352
Open this publication in new window or tab >>Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds
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2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 9, p. 17331-17352Article in journal (Refereed) Published
Abstract [en]

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

Place, publisher, year, edition, pages
MDPI AG, 2014
Keyword
environmental monitoring; gas discrimination; gas distribution mapping; service robots; open sampling systems; PID, metal oxide sensors
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-37008 (URN)10.3390/s140917331 (DOI)000343106600091 ()25232911 (PubMedID)2-s2.0-84928689795 (Scopus ID)
Projects
Gasbot
Note

Funding Agencies:

Gasbot project 8140

Spanish project: "Signal Processing for Ion Mobility Spectrometry: Analysis of Biomedical fluids and detection of toxic chemicals" TEC2011-26143

Departament d'Universitats, Recerca i Societat de la Informacio de la Generalitat de Catalunya SGR 1445

Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya and the European Social Fund (ESF)

SUR

Department d'Economia i Coneixement

Available from: 2014-09-18 Created: 2014-09-18 Last updated: 2018-01-11Bibliographically approved
Hernandez Bennetts, V., Trincavelli, M., Lilienthal, A. J. & Schaffernicht, E. (2014). Online parameter selection for gas distribution mapping. Sensor Letters, 12(6-7), 1147-1151
Open this publication in new window or tab >>Online parameter selection for gas distribution mapping
2014 (English)In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6-7, p. 1147-1151Article in journal (Refereed) Published
Abstract [en]

The ability to produce truthful maps of the distribution of one or more gases is beneficial for applications ranging from environmental monitoring to mines and industrial plants surveillance. Realistic environments are often too complicated for applying analytical gas plume models or performing reliable CFD simulations, making data-driven statistical gas distribution models the most attractive alternative. However, statistical models for gas distribution modelling, often rely on a set of meta-parameters that need to be learned from the data through Cross Validation (CV) techniques. CV techniques are computationally expensive and therefore need to be computed offline. As a faster alternative, we propose a parameter selection method based on Virtual Leave-One-Out Cross Validation (VLOOCV) that enables online learning of meta-parameters. In particular, we consider the Kernel DM+V, one of the most well studied algorithms for statistical gas distribution mapping, which relies on a meta-parameter, the kernel bandwidth. We validate the proposed VLOOCV method on a set of indoor and outdoor experiments where a mobile robot with a Photo Ionization Detector (PID) was collecting gas measurements. The approximation provided by the proposed VLOOCV method achieves very similar results to plain Cross Validation at a fraction of the computational cost. This is an important step in the development of on-line statistical gas distribution modelling algorithms.

Place, publisher, year, edition, pages
American Scientific Publishers, 2014
Keyword
BANDWIDTH SELECTION; GAS DISTRIBUTION MAPPING; VIRTUAL LEAVE-ONE-OUT CROSS VALIDATION
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-32669 (URN)10.1166/sl.2014.3191 (DOI)2-s2.0-84911378963 (Scopus ID)
Projects
GASBOT
Available from: 2013-12-06 Created: 2013-12-06 Last updated: 2018-01-11Bibliographically approved
Hernandez Bennetts, V., Schaffernicht, E., Stoyanov, T., Lilienthal, A. J. & Trincavelli, M. (2014). Robot assisted gas tomography: an alternative approach for the detection of fugitive methane emissions. In: Workshop on Robot Monitoring: . Paper presented at Workshop on Robotic Monitoring at the Robotics Science and Systems (RSS) 2014. July 13th, Berkeley Ca., USA.
Open this publication in new window or tab >>Robot assisted gas tomography: an alternative approach for the detection of fugitive methane emissions
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2014 (English)In: Workshop on Robot Monitoring, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements.

National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-37064 (URN)
Conference
Workshop on Robotic Monitoring at the Robotics Science and Systems (RSS) 2014. July 13th, Berkeley Ca., USA
Projects
Gasbot
Available from: 2014-09-19 Created: 2014-09-19 Last updated: 2018-01-11Bibliographically approved
Pashami, S., Lilienthal, A. J., Schaffernicht, E. & Trincavelli, M. (2014). rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors. Sensor Letters, 12(6/7), 1123-1127
Open this publication in new window or tab >>rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors
2014 (English)In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6/7, p. 1123-1127Article in journal (Refereed) Published
Abstract [en]

 The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms.

Place, publisher, year, edition, pages
American Scientific Publishers, 2014
Keyword
MOX Sensor, Open Sampling System, Change Point Detection, Reweighted Norm Minimization
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-38056 (URN)10.1166/sl.2014.3170 (DOI)2-s2.0-84911386978 (Scopus ID)
Available from: 2014-10-24 Created: 2014-10-24 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0195-2102

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