oru.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (7 of 7) Show all publications
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2018). A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments. Sensors and actuators. B, Chemical, 259, 183-203
Open this publication in new window or tab >>A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
2018 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed) Published
Abstract [en]

Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

Place, publisher, year, edition, pages
Amsterda, Netherlands: Elsevier, 2018
Keywords
Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-63468 (URN)10.1016/j.snb.2017.10.063 (DOI)000424877600023 ()2-s2.0-85038032167 (Scopus ID)
Projects
SmokBot
Funder
EU, Horizon 2020, 645101
Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2018-09-17Bibliographically approved
Monroy, J., Hernandez Bennetts, V., Fan, H., Lilienthal, A. & Gonzalez-Jimenez, J. (2017). GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments. Sensors, 17(7), 1479-1494
Open this publication in new window or tab >>GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments
Show others...
2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, p. 1479-1494Article in journal (Refereed) Published
Abstract [en]

This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment.

Place, publisher, year, edition, pages
Basel, Switzerland: MPDI AG, 2017
Keywords
Gas dispersal, robotics olfaction, gas sensing, mobile robotics, Robot Operating System (ROS)
National Category
Robotics Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-64761 (URN)10.3390/s17071479 (DOI)000407517600018 ()2-s2.0-85021432580 (Scopus ID)
Projects
SmokeBot
Funder
EU, European Research Council, 645101Swedish Research CouncilKnowledge Foundation, 20130196
Note

Funding Agencies:

Spanish Goverment

Andalucia Goverment

Available from: 2018-02-01 Created: 2018-02-01 Last updated: 2018-09-06Bibliographically approved
Fan, H., Arain, M. A., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2017). Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robotcreatedoccupancy Maps And Remote Gas Sensors In The Simulation Loop. In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings: . Paper presented at 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 28-31 May 2017 Montreal, QC, Canada. IEEE conference proceedings, Article ID 17013581.
Open this publication in new window or tab >>Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robotcreatedoccupancy Maps And Remote Gas Sensors In The Simulation Loop
Show others...
2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE conference proceedings, 2017, article id 17013581Conference paper, Published paper (Refereed)
Abstract [en]

Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2017
National Category
Computer Sciences Robotics
Identifiers
urn:nbn:se:oru:diva-60633 (URN)10.1109/ISOEN.2017.7968874 (DOI)978-1-5090-2392-9 (ISBN)978-1-5090-2393-6 (ISBN)
Conference
2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 28-31 May 2017 Montreal, QC, Canada
Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2018-02-01Bibliographically approved
Arain, M. A., Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2017). Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments. In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings: . Paper presented at 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 28-31 May 2017 Montreal QC, Canada. , Article ID 7968895.
Open this publication in new window or tab >>Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments
Show others...
2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, 2017, article id 7968895Conference paper, Published paper (Refereed)
Abstract [en]

An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.

National Category
Computer Sciences Robotics
Identifiers
urn:nbn:se:oru:diva-60646 (URN)10.1109/ISOEN.2017.7968895 (DOI)978-1-5090-2392-9 (ISBN)978-1-5090-2393-6 (ISBN)
Conference
2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 28-31 May 2017 Montreal QC, Canada
Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2018-01-13Bibliographically approved
Xing, Y., Vincent, T. A., Cole, M., Gardner, J. W., Fan, H., Hernandez Bennetts, V., . . . Lilienthal, A. (2017). Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments. In: IEEE SENSORS 2017: Conference Proceedings. Paper presented at 16th IEEE Sensors Conference, Glasgow, Scotland, UK, October 29 - November 1, 2017 (pp. 1691-1693). New York: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments
Show others...
2017 (English)In: IEEE SENSORS 2017: Conference Proceedings, New York: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1691-1693Conference paper, Published paper (Refereed)
Abstract [en]

In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%).

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Proceedings of IEEE Sensors, ISSN 1930-0395
Keywords
Gas sensor, mobile robot, MOX, open sampling system, gas discrimination
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-64463 (URN)10.1109/ICSENS.2017.8234440 (DOI)000427677500564 ()2-s2.0-85044276510 (Scopus ID)978-1-5090-1012-7 (ISBN)978-1-5090-1013-4 (ISBN)
Conference
16th IEEE Sensors Conference, Glasgow, Scotland, UK, October 29 - November 1, 2017
Projects
SmokeBot
Funder
EU, Horizon 2020, 645101
Available from: 2018-01-23 Created: 2018-01-23 Last updated: 2018-04-25Bibliographically approved
Asadi, S., Fan, H., Hernandez Bennetts, V. & Lilienthal, A. (2017). Time-dependent gas distribution modelling. Robotics and Autonomous Systems, 96, 157-170
Open this publication in new window or tab >>Time-dependent gas distribution modelling
2017 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 96, p. 157-170Article in journal (Refereed) Published
Abstract [en]

Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Mobile robot olfaction, Statistical gas distribution modelling, Temporal sub-sampling, Time-dependent gas distribution modelling
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-62783 (URN)10.1016/j.robot.2017.05.012 (DOI)000413881900014 ()
Note

Funding Agency:

EC  FP7-224318-DIADEM

Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2018-01-13Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2016). Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks. In: 2016 IEEE SENSORS: . Paper presented at 15th IEEE Sensors Conference (SENSORS 2016), Orlando, USA, October 30 - November 2, 2016. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks
2016 (English)In: 2016 IEEE SENSORS, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper, Published paper (Refereed)
Abstract [en]

Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
Proceedings of IEEE Sensors, ISSN 1930-0395
Keywords
gas discrimination, Open Sampling Systems, metal oxide sensors, unsupervised learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-54024 (URN)10.1109/ICSENS.2016.7808903 (DOI)000399395700485 ()2-s2.0-85010987762 (Scopus ID)978-1-4799-8287-5 (ISBN)
Conference
15th IEEE Sensors Conference (SENSORS 2016), Orlando, USA, October 30 - November 2, 2016
Projects
Mobile Robots with Novel Environmental Sensors for Inspection of Disaster Sites with Low Visibility
Note

Funding Agency:

ICT by the European Commission  645101

Available from: 2016-12-16 Created: 2016-12-16 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1662-0960

Search in DiVA

Show all publications