To Örebro University

oru.seÖrebro University Publications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 14) Show all publications
Fan, H., Schaffernicht, E. & Lilienthal, A. (2022). Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications. Frontiers in Chemistry, 10, Article ID 863838.
Open this publication in new window or tab >>Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
2022 (English)In: Frontiers in Chemistry, E-ISSN 2296-2646, Vol. 10, article id 863838Article in journal (Refereed) Published
Abstract [en]

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning-based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022
Keywords
electronic nose, metal oxide semiconductor sensor, gas detection, gas sensing, open sampling systems, ensemble learning, robotic olfaction
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-98781 (URN)10.3389/fchem.2022.863838 (DOI)000795874900001 ()35572118 (PubMedID)2-s2.0-85130296086 (Scopus ID)
Projects
SmokeBot
Funder
EU, Horizon 2020, 645101
Available from: 2022-04-29 Created: 2022-04-29 Last updated: 2024-01-03Bibliographically approved
Winkler, N. P., Kotlyar, O., Schaffernicht, E., Fan, H., Matsukura, H., Ishida, H., . . . Lilienthal, A. (2022). Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping. In: Danilo Tardioli; Vicente Matellán; Guillermo Heredia; Manuel F. Silva; Lino Marques (Ed.), ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2. Paper presented at ROBOT2022: Fifth Iberian Robotics Conference, Zaragoza, Spain, November 23-25, 2022 (pp. 178-188). Springer, 590
Open this publication in new window or tab >>Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping
Show others...
2022 (English)In: ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2 / [ed] Danilo Tardioli; Vicente Matellán; Guillermo Heredia; Manuel F. Silva; Lino Marques, Springer, 2022, Vol. 590, p. 178-188Conference paper, Published paper (Refereed)
Abstract [en]

To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.

Place, publisher, year, edition, pages
Springer, 2022
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 590
Keywords
Convolutional LSTM, Gas Distribution Mapping, Sequential Learning, Spatial Interpolation
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-102769 (URN)10.1007/978-3-031-21062-4_15 (DOI)000906176800015 ()2-s2.0-85145267880 (Scopus ID)9783031210617 (ISBN)9783031210624 (ISBN)
Conference
ROBOT2022: Fifth Iberian Robotics Conference, Zaragoza, Spain, November 23-25, 2022
Note

Funding agency:

Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)

Japan Society for the Promotion of Science 22H04952

Available from: 2022-12-19 Created: 2022-12-19 Last updated: 2024-01-03Bibliographically approved
Fan, H. (2022). Robot-aided Gas Sensing for Emergency Responses. (Doctoral dissertation). Örebro: Örebro University
Open this publication in new window or tab >>Robot-aided Gas Sensing for Emergency Responses
2022 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Emergency response personnel can be exposed to various extreme hazards during the response to natural and human-made disasters. In many of the scenarios, one of the risk factors is the presence of hazardous airborne chemicals. Addressing this risk factor requires typical tiring, taxing and toxic operations that are suitable to be aided by Mobile Robot Olfaction (MRO) techniques. MRO is the research domain combining intelligent mobile robots with an artificial sense of smell. It presents the prospect of practical applications for emergency response as it allows to convey useful information on-site and online without risking the safety of human responders. However, standard gas sampling procedures for laboratory use are not directly applicable to MRO due to the complexity of uncontrolled environments and the need for fast deployment and analysis. Besides, state-of-the-art gas sensing approaches have difficulties handling A Priori Unknown Gases (APUG). In APUG situations, the number or/and identities of the present chemicals are unknown, posing challenges in recognizing the underlying risks with conventional solutions such as supervised learning-based electronic noses or dedicated gas sensors targeting known analytes.

This dissertation focuses on contributions toward real-world applications of robot-aided gas sensing with an APUG problem setup. The dissertation starts with a requirement analysis of Gas Sensing for Emergency Response (GSER) to identify the key tasks in ad hoc applications. Considering that not all analytes of interest in a field application may be known in advance, a pipeline incorporating non-supervised detection and discrimination of multiple chemicals and consequent distribution modelling is found to be important for GSER. The remainder of the thesis fills this pipeline with three steps: 1) An ensemble learning-based gas detection approach is proposed to recognize significant changes from sensor signals as well as model the baseline response pattern. 2) A clustering analysis-based gas discrimination approach is developed to perform online analysis that automatically learns the number of different chemical compounds from the acquired measurements and provides a probabilistic representation of their class labels. 3) The integration of the proposed non-supervised gas detection and gas discrimination approaches with gas distribution modelling allows prototyping of a GSER system, which can enhance emergency responders’ situational awareness in the target environment. This GSER system demonstrates the concept of discriminating and mapping multiple unknown chemical compounds in uncontrolled environments with validation and evaluation using real-world data sets.

During the research on the GSER system, gas dispersal simulation is also investigated to facilitate MRO algorithm development and validation in general. In-field experiments of MRO algorithms are often time-consuming, expensive, cumber some, and lack repeatability, while most of the available simulation tools are limited to insitu gas sensors and simple environments. These issues were addressed by improving a simulation framework to replicate geometrical representations of actual real-world environments and support a variety of gas sensor models. The potential applicability of the resulting work is demonstrated by simulating a gas emission monitoring task and facilitating the development process of a state-of-the-art time-dependent gas distribution modelling algorithm.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2022. p. 176
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 95
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-101509 (URN)9789175294643 (ISBN)
Public defence
2022-11-18, Örebro universitet, Långhuset, Hörsal L2, Fakultetsgatan 1, Örebro, 13:00 (English)
Opponent
Supervisors
Available from: 2022-09-28 Created: 2022-09-28 Last updated: 2024-01-03Bibliographically approved
Fan, H., Jonsson, D., Schaffernicht, E. & Lilienthal, A. (2022). Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning. In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN): Proceedings. Paper presented at 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2022), Aveiro, Portugal, May 29 - June 1, 2022. IEEE
Open this publication in new window or tab >>Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning
2022 (English)In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN): Proceedings, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Gas identification using an electronic nose (e-nose) typically relies on a multi-class classifier trained with extensive data of a limited set of target analytes. Usually, classification performance degrades in the presence of mixtures that include interferents not represented in the training data. This issue limits the applicability of e-noses in real-world scenarios where interferents are a priori unknown. This paper investigates the feasibility of tackling this particular gas identification problem using one-class learning. We propose several training strategies for a one-class support vector machine to deal with gas mixtures composed of a target analyte and an interferent at different concentration levels. Our evaluation indicates that accurate identification of the presence of a target analyte is achievable if it is dominant in a mixture. For interferent-dominant mixtures, extensive training is required, which implies that an improvement in the generalization ability of the one-class model is needed.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
gas identification, gas mixture, unknown interferent, one-class learning, electronic nose
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-101289 (URN)10.1109/ISOEN54820.2022.9789607 (DOI)000852626300015 ()2-s2.0-85133180752 (Scopus ID)9781665458610 (ISBN)9781665458603 (ISBN)
Conference
2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2022), Aveiro, Portugal, May 29 - June 1, 2022
Available from: 2022-09-18 Created: 2022-09-18 Last updated: 2024-01-03Bibliographically approved
Xing, Y., Vincent, T. A., Fan, H., Schaffernicht, E., Hernandez Bennetts, V., Lilienthal, A. J., . . . Gardner, J. W. (2019). FireNose on Mobile Robot in Harsh Environments. IEEE Sensors Journal, 19(24), 12418-12431
Open this publication in new window or tab >>FireNose on Mobile Robot in Harsh Environments
Show others...
2019 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 19, no 24, p. 12418-12431Article in journal (Refereed) Published
Abstract [en]

In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
FireNose, mobile robot, MOX sensor, gas map, harsh environments
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-77784 (URN)10.1109/JSEN.2019.2939039 (DOI)000506895500081 ()2-s2.0-85076340302 (Scopus ID)
Funder
EU, Horizon 2020
Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2024-01-03Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2019). Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers. In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN): . Paper presented at 2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019. IEEE, Article ID 151773.
Open this publication in new window or tab >>Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
2019 (English)In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE, 2019, article id 151773Conference paper, Published paper (Refereed)
Abstract [en]

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Metal oxide semiconductor sensor, electronic nose, gas detection, gas sensing, open sampling systems
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-77210 (URN)10.1109/ISOEN.2019.8823148 (DOI)2-s2.0-85072989108 (Scopus ID)
Conference
2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019
Projects
SmokeBot
Available from: 2019-10-13 Created: 2019-10-13 Last updated: 2024-01-03Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2019). Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose. Sensors, 19(3), Article ID E685.
Open this publication in new window or tab >>Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
2019 (English)In: Sensors, E-ISSN 1424-8220, Vol. 19, no 3, article id E685Article in journal (Refereed) Published
Abstract [en]

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-72366 (URN)10.3390/s19030685 (DOI)000459941200248 ()30736489 (PubMedID)2-s2.0-85061226919 (Scopus ID)
Note

Funding Agency:

European Commission  645101

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2024-01-03Bibliographically approved
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: 2024-01-03Bibliographically 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, 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: 2022-02-10Bibliographically approved
Fan, H., Arain, M. A., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2017). Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robot-Created Occupancy 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 Robot-Created Occupancy 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: 2024-01-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1662-0960

Search in DiVA

Show all publications