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Gas discrimination for mobile robots
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-0195-2102
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The problem addressed in this thesis is discrimination of gases with an array of partially selective gas sensors. Metal oxide gas sensors are the most common gas sensing technology since they have, compared to other gas sensing technologies, a high sensitivity to the target compounds, a fast response time,they show a good stability of the response over time and they are commercially available. One of the most severe limitation of metal oxide gas sensors is the scarce selectivity, that means that they do not respond only to the compound for which they are optimized but also to other compounds. One way to enhance the selectivity of metal oxide gas sensors is to build an array of sensorswith different, and partially overlapping, selectivities and then analyze the response of the array with a pattern recognition algorithm. The concept of anarray of partially selective gas sensors used together with a pattern recognition algorithm is known as an electronic nose (e-nose).In this thesis the attention is focused on e-nose applications related mobile robotics. A mobile robot equipped with an e-nose can address tasks like environmental monitoring, search and rescue operations or exploration of hazardous areas. In e-noses mounted on mobile robots the sensing array is most often directly exposed to the environment without the use of a sensing chamber.This choice is often made because of constraints in weight, costs and because the dynamic response obtained by the direct interaction of the sensors with the gas plume contains valuable information. However, this setup introduces additional challenges due to the gas dispersion that characterize natural environments.Turbulent and chaotic gas dispersal causes the array of sensors to be exposed to rapid changes in concentration that cause the sensor response to behighly dynamic and to seldom reach a steady state. Therefore the discriminationof gases has to be performed on features extracted from the dynamics of the signal. The problem is further complicated by variations in temperature and humidity, physical variables to which metal oxide gas sensors are crossensitive.For these reasons the problem of discrimination of gases when an array of sensors is directly exposed to the environment is different from when the array of sensors is in a controlled chamber.

This thesis is a compilation of papers whose contributions are two folded.On one side new algorithms for discrimination of gases with an array of sensors directly exposed to the environment are presented. On the other side, innovative experimental setups are proposed. These experimental setups enable the collection of high quality data that allow a better insight in the problem of discrimination of gases with mobile robots equipped with an e-nose. The algorithmic contributions start with the design and validation of a gas discrimination algorithm for gas sensors array directly exposed to the environment. The algorithmis then further developed in order to be able to run online on a robot, thereby enabling the possibility of creating an olfactory driven path-planning strategy. Additional contributions aim at maximizing the generalization capabilitiesof the gas discrimination algorithm with respect to variations in the environmental conditions. First an approach in which the odor discrimination is performed by an ensemble of linear classifiers is considered. Then a feature selection method that aims at finding a feature set that is insensitive to variations in environmental conditions is designed. Finally, a further contribution in this thesis is the design of a pattern recognition algorithm for identification of bacteria from blood vials. In this case the array of gas sensors was deployed ina controlled sensing chamber.

Place, publisher, year, edition, pages
Örebro: Örebro universitet , 2010. , 69 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 41
National Category
Engineering and Technology Computer and Information Science Computer Science
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-11901ISBN: 978-91-7668-762-8 (print)OAI: oai:DiVA.org:oru-11901DiVA: diva2:353116
Public defence
2010-12-03, Hörsal Teknik (HST), Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2010-09-24 Created: 2010-09-24 Last updated: 2017-10-17Bibliographically approved
List of papers
1. Towards environmental monitoring with mobile robots
Open this publication in new window or tab >>Towards environmental monitoring with mobile robots
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2008 (English)In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, New York: IEEE , 2008, 2210-2215 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an “electronic nose” and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties:

an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed.

Place, publisher, year, edition, pages
New York: IEEE, 2008
Keyword
mobile, robot, olfaction
National Category
Computer Science Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-4619 (URN)10.1109/IROS.2008.4650755 (DOI)978-1-4244-2057-5 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, 22-26 Sept, Nice
Available from: 2008-10-02 Created: 2008-10-02 Last updated: 2017-10-18Bibliographically approved
2. Classification of odours with mobile robots based on transient response
Open this publication in new window or tab >>Classification of odours with mobile robots based on transient response
2008 (English)In: IEEE/RSJ international conference on intelligent robots and systems, IROS 2008, New York: IEEE , 2008, 4110-4115 p.Conference paper, Published paper (Refereed)
Abstract [en]

Classification of odours with an array of gas sensors mounted on a mobile robot is a challenging and still relatively unexplored topic. Mobile robots able to classify an odour could navigate to a specific source or isolate high concentration areas in applications such as environmental monitoring. A key aspect to classification is to be able to process the data collected while moving the robot and using a simple and compact sensor system. In order to achieve this, we present a classification algorithm that is based in the transient response from the sensors. An analysis of how classification results vary with regards to the movement of the robot is provided and subsequently the experimental validations show that the classification performance depends more on how

the robot traverses the odour plume and the quality of the transient than on the distance from the source location. The experimental validation has been done in a large unmodified indoor environment.

Place, publisher, year, edition, pages
New York: IEEE, 2008
Keyword
mobile, robot, olfaction
National Category
Computer Science Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-4618 (URN)10.1109/IROS.2008.4650713 (DOI)978-1-4244-2057-5 (ISBN)
Conference
IEEE/RSJ international conference on intelligent robots and systems, IROS 2008, 22-26 Sept, Nice
Available from: 2008-10-02 Created: 2008-10-02 Last updated: 2017-10-18Bibliographically approved
3. Online classification of gases for environmental exploration
Open this publication in new window or tab >>Online classification of gases for environmental exploration
2009 (English)In: IEEE/RSJ international conference on intelligent robots and systems, IROS 2009, New York: IEEE, 2009, 3311-3316 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we investigate how a mobile robot equipped with tin dioxide gas sensors and an anemometer can use an online classification algorithm in order to improve the exploration strategy. The purpose of the platform is to establish the character of a gas source with accuracy while minimizing the time required for exploration. For this to be possible, the output of the classification algorithm is probabilistic, feeding in a sequence of posterior probabilities to a path planner. To further assist path planning, a 3d-ultrasonic anemometer is available which give indication on the average wind speed and direction. In addition to evaluating different olfaction driven path planning strategies, experimental validations also evaluate the classification algorithms and its application to different environments with varying characteristics.

Place, publisher, year, edition, pages
New York: IEEE, 2009
Series
IEEE Conference Publications, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Engineering and Technology Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-7853 (URN)10.1109/IROS.2009.5354635 (DOI)000285372901226 ()2-s2.0-76249096898 (Scopus ID)978-1-4244-3803-7 (ISBN)
Conference
IEEE/RSJ international conference on intelligent robots and systems, IROS 2009, 10-15 Oct, St. Louis, MO, USA
Available from: 2009-09-08 Created: 2009-09-08 Last updated: 2017-10-18Bibliographically approved
4. Classification of odours for mobile robots using an ensemble of linear classifiers
Open this publication in new window or tab >>Classification of odours for mobile robots using an ensemble of linear classifiers
2009 (English)In: Olfaction and electronic nose: proceedings of the 13th international symposium on olfaction and electronic nose / [ed] Matteo Pardo, Giorgio Sberveglieri, American Institute of Physics (AIP), 2009, 475-478 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the classification of odours using an electronic nose mounted on a mobile robot. The samples are collected as the robot explores the environment. Under such conditions, the sensor response differs from typical three phase sampling processes. In this paper, we focus particularly on the classification problem and how it is influenced by the movement of the robot. To cope with these influences, an algorithm consisting of an ensemble of classifiers is resented. Experimental results show that this algorithm increases classification performance compared to other traditional classification methods.

Place, publisher, year, edition, pages
American Institute of Physics (AIP), 2009
Series
AIP conference proceedings, ISSN 0094-243X ; 1137
Keyword
Odour Classification; Mobile Robotics
National Category
Computer Science Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-7851 (URN)10.1063/1.3156587 (DOI)000268929400118 ()2-s2.0-70450140369 (Scopus ID)978-0-7354-0674-2 (ISBN)
Conference
13th international symposium on olfaction and electronic nose, Brescia, Italy, April 15–17, 2009
Available from: 2009-09-08 Created: 2009-09-08 Last updated: 2017-10-18Bibliographically approved
5. Odour classification system for continuous monitoring applications
Open this publication in new window or tab >>Odour classification system for continuous monitoring applications
2009 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 58, no 2, 265-273 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we investigate the classification performance of an electronic nose system, based on tin dioxide gas sensors. In contrast to previous studies, the electronic nose is mounted on a mobile platform and samples are analyzed using only transient information in the signals. The motivation behind this work is to explore the feasibility of using electronic nose devices for odour classification in a number of future application domains which require fast and possibly real-time odour identification. To perform transient based analysis of the signals, a comparative study of different methods for feature extraction was performed. Additionally, the application of a relevance vector machine classifier is explored to further analyze the classification performance based on quality of the obtained samples. The results presented in this study can be used for the development of electronic nose devices particularly suitable for environmental monitoring applications.

Place, publisher, year, edition, pages
Elsevier, 2009
Keyword
Chemical sensors array, Odour classification, Mobile olfaction, Relevance vector machines
National Category
Computer Science Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-7838 (URN)10.1016/j.snb.2009.03.018 (DOI)000267159700002 ()2-s2.0-66349087011 (Scopus ID)
Available from: 2009-09-08 Created: 2009-09-08 Last updated: 2017-12-13Bibliographically approved
6. A statistical approach to gas distribution modelling with mobile robots: the Kernel DM+V algorithm
Open this publication in new window or tab >>A statistical approach to gas distribution modelling with mobile robots: the Kernel DM+V algorithm
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2009 (English)In: IEEE/RSJ international conference on intelligent robots and systems: IROS 2009, IEEE conference proceedings, 2009, 570-576 p.Conference paper, Published paper (Refereed)
Abstract [en]

Gas distribution modelling constitutes an ideal application area for mobile robots, which – as intelligent mobile gas sensors – offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2009
Series
IEEE Conference Publications, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Engineering and Technology Other Computer and Information Science
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-8435 (URN)10.1109/IROS.2009.5354304 (DOI)000285372900101 ()2-s2.0-76249127720 (Scopus ID)978-1-4244-3803-7 (ISBN)
Conference
IEEE/RSJ international conference on intelligent robots and systems, IROS 2009. 10-15 Oct, St. Louis, MO.
Available from: 2009-11-08 Created: 2009-11-02 Last updated: 2017-10-18Bibliographically approved
7. Feature selection for gas identification with a mobile robot
Open this publication in new window or tab >>Feature selection for gas identification with a mobile robot
2010 (English)In: 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2010, 2852-2857 p.Conference paper, Published paper (Other academic)
Abstract [en]

In this paper we analyze the problem of discrimination of gases with mobile robots. Previously, it has been shown that the conditions in which data is collected heavily influence the characteristics of the signal to be identified. As a result, the already difficult task of selecting features which characterize a gas is made more challenging by the absence of a steady state response. This is often due to the movement of the robot, and/or the physical properties of the environment, e. g., turbulent airflow creating patches and eddies in the plume. In this work we compare two approaches for feature selection which are able to consider explicitly the information on the experimental setup and optimize the subset of features used in the recognition process. The approaches are tested on a large data set collected with a mobile robot moving in different environments (outdoors and indoors). The results show that the classification performance is improved resulting in a higher average accuracy and lower variance in the accuracy across the different experimental setups.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2010
National Category
Computer Science Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-13995 (URN)10.1109/ROBOT.2010.5509617 (DOI)000284150003046 ()978-1-4244-5038-1 (ISBN)
Conference
2010 IEEE International Conference on Robotics and Automation (ICRA)
Available from: 2011-01-17 Created: 2011-01-17 Last updated: 2017-10-18Bibliographically approved
8. An inspection of signal dynamics using an open sampling system for gas identification
Open this publication in new window or tab >>An inspection of signal dynamics using an open sampling system for gas identification
2010 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Odour discrimination with a sensor array directly exposed to the environment is of great interest in many applications ranging from environmental monitoring to search and rescue and exploration of hazardous areas. Metal oxide based sensors are typically used in such applications for gas detection as they are compact, low costing and exhibit fast response time to an analyte. Given the characteristics of the metal oxide sensors as well as the chaotic nature of the airflow in a natural environment, it has been assumed that a reliable odour discrimination system working in this condition has to work with features that capture the transient characteristics of the signal. In this work we present an experimental setup that enables a deeper insight into transient-based analysis for open sampling systems. Observations on the  properties of the signal collected under a controlled experimental condition using an open sampling system are presented. These observations suggest that in a scenario in which the sensors are exposed to different compounds without the possibility to recover the baseline value in between a gas identification, a model of the dynamics of the system is needed and a notion of the sensor state should be maintained that captures the past history of the sensor response.

National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-14498 (URN)
Conference
ICRA Workshop on Networked and Mobile Robot Olfaction in Natural, Dynamic Environments, 2010
Available from: 2011-02-07 Created: 2011-02-07 Last updated: 2017-10-17Bibliographically approved
9. Direct identification of bacteria in blood culture samples using an electronic nose
Open this publication in new window or tab >>Direct identification of bacteria in blood culture samples using an electronic nose
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2010 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 57, no 12, 2884-2890 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we introduce a method for identification of bacteria in human blood culture samples using an electronic nose. The method uses features, which capture the static (steady state) and dynamic (transient) properties of the signal from the gas sensor array and proposes a means to ensemble results from consecutive samples. The underlying mechanism for ensembling is based on an estimation of posterior probability, which is extracted from a support vector machine classifier. A large dataset representing ten different bacteria cultures has been used to validate the presented methods. The results detail the performance of the proposed algorithm and show that through ensembling decisions on consecutive samples, significant reliability in classification accuracy can be achieved.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2010
Keyword
Bacteria identification, electronic nose, sepsis
National Category
Computer Science Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-12831 (URN)10.1109/TBME.2010.2049492 (DOI)000284360100011 ()20460199 (PubMedID)2-s2.0-78649274345 (Scopus ID)
Available from: 2011-01-11 Created: 2011-01-03 Last updated: 2017-12-11Bibliographically approved
10. Collecting a database for studying gas distribution mapping and gas source localization with mobile robots
Open this publication in new window or tab >>Collecting a database for studying gas distribution mapping and gas source localization with mobile robots
2010 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

In this paper, we present our initial experiments to collect a database for studying mobile robot olfaction. Mobile robots with olfactory sensing capabilities are expected to be used in various applications including gas distribution mapping and gas source localization. Owing to the turbulent nature of the airflow field and the gas distribution, these robots must be equipped with algorithms that can cope with chaotic environments. Since it is important to check the applicability of such algorithms in a diversity of environments, we propose to build a database with which the users can test the performances of their own algorithms in various environments. The database collected so far consists of two parts: a basic data set collected in a well-characterized controlled indoor environment and applied data sets collected in uncontrolled indoor and outdoor environments. A result of applying the database for testing a gas-source localization algorithm are shown as an example. We believe that such database will accelerate the research advancements on mobile robot olfaction.

National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-14499 (URN)
Conference
International Conference on Advanced Mechatronics, Osaka, Japan, October 4-6, 2010
Available from: 2011-02-07 Created: 2011-02-07 Last updated: 2017-10-17Bibliographically approved

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