oru.sePublications
Change search
Refine search result
1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Stachniss, Cyrill
    University of Freiburg, Freiburg, Germany.
    Plagemann, Christian
    Stanford University, Stanford CA, USA.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Statistical gas distribution modeling using kernel methods2011In: Intelligent systems for machine olfaction: tools and methodologies / [ed] E. L. Hines and M. S. Leeson, IGI Global, 2011, 1, p. 153-179Chapter in book (Refereed)
    Abstract [en]

    Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methodsthat statistically model gas distribution. Gas measurements are treated as randomvariables, and the gas distribution is predicted at unseen locations either using akernel density estimation or a kernel regression approach. The resulting statistical 

    apmodelsdo not make strong assumptions about the functional form of the gas distribution,such as the number or locations of gas sources, for example. The majorfocus of this chapter is on two-dimensional models that provide estimates for themeans and predictive variances of the distribution. Furthermore, three extensionsto the presented kernel density estimation algorithm are described, which allow toinclude wind information, to extend the model to three dimensions, and to reflecttime-dependent changes of the random process that generates the gas distributionmeasurements. All methods are discussed based on experimental validation usingreal sensor data.

  • 2.
    Di Rocco, Maurizio
    et al.
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Gas source localization in indoor environments using multiple inexpensive robots and stigmergy2011In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2011, p. 5007-5014Conference paper (Refereed)
    Abstract [en]

    Environmental monitoring is a rather new field in robotics. One of the main appealing tasks is gas mapping, i.e., the characterization of the chemical properties (concentration, dispersion, etc.) of the air within an environment. Current approaches rely on a robot using standard localization and mapping techniques to fuse gas measures with spatial features. These approaches require sophisticated sensors and/or high computational resources. We propose a minimalistic approach, in which one or multiple low-cost robots exploit the ability to store information in the environment, or “stigmergy”, to effectively compute an artificial potential leading toward the likely location of the gas source, as indicated by a highest gas concentration or fluctuation. The potential is computed and stored directly on an array of RFID tags buried under the floor. Our approach has been validated in extensive experiments performed on real robots in a domestic environment.

  • 3.
    Ferri, Gabriele
    et al.
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Mondini, Alessio
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Manzi, Alessandro
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Mazzolai, Barbara
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Laschi, Cecilia
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Mattoli, Virgilio
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Lettere, Marco
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Dario, Paolo.
    Scuola Superiore Sant'Anna, Pisa, Italy.
    DustCart, a Mobile Robot for Urban Environments: Experiments of Pollution Monitoring and Mapping during Autonomous Navigation in Urban Scenarios2010In: Proceedings of ICRA Workshop on Networked and Mobile Robot Olfaction in Natural, Dynamic Environments, 2010Conference paper (Refereed)
    Abstract [en]

    In the framework of DustBot European project, aimed at developing a new multi-robot system for urban hygiene management, we have developed a twowheeled robot: DustCart. DustCart aims at providing a solution to door-to-door garbage collection: the robot, called by a user, navigates autonomously to his/her house; collects the garbage from the user and discharges it in an apposite area. An additional feature of DustCart is the capability to monitor the air pollution by means of an on board Air Monitoring Module (AMM). The AMM integrates sensors to monitor several atmospheric pollutants, such as carbon monoxide (CO), particular matter (PM10), nitrogen dioxide (NO2), ozone (O3) plus temperature (T) and relative humidity (rHu). An Ambient Intelligence platform (AmI) manages the robots’ operations through a wireless connection. AmI is able to collect measurements taken by different robots and to process them to create a pollution distribution map. In this paper we describe the DustCart robot system, focusing on the AMM and on the process of creating the pollutant distribution maps. We report results of experiments of one DustCart robot moving in urban scenarios and producing gas distribution maps using the Kernel DM+V algorithm. These experiments can be considered as one of the first attempts to use robots as mobile monitoring devices that can complement the traditional fixed stations.

  • 4. Franke, Andre
    et al.
    McGovern, Dermot P B
    Barrett, Jeffrey C
    Wang, Kai
    Radford-Smith, Graham L
    Ahmad, Tariq
    Lees, Charlie W
    Balschun, Tobias
    Lee, James
    Roberts, Rebecca
    Anderson, Carl A
    Bis, Joshua C
    Bumpstead, Suzanne
    Ellinghaus, David
    Festen, Eleonora M
    Georges, Michel
    Green, Todd
    Haritunians, Talin
    Jostins, Luke
    Latiano, Anna
    Mathew, Christopher G
    Montgomery, Grant W
    Prescott, Natalie J
    Raychaudhuri, Soumya
    Rotter, Jerome I
    Schumm, Philip
    Sharma, Yashoda
    Simms, Lisa A
    Taylor, Kent D
    Whiteman, David
    Wijmenga, Cisca
    Baldassano, Robert N
    Barclay, Murray
    Bayless, Theodore M
    Brand, Stephan
    Büning, Carsten
    Cohen, Albert
    Colombel, Jean-Frederick
    Cottone, Mario
    Stronati, Laura
    Denson, Ted
    De Vos, Martine
    D'Inca, Renata
    Dubinsky, Marla
    Edwards, Cathryn
    Florin, Tim
    Franchimont, Denis
    Gearry, Richard
    Glas, Jürgen
    Van Gossum, Andre
    Guthery, Stephen L
    Halfvarson, Jonas
    Örebro University, School of Health and Medical Sciences.
    Verspaget, Hein W
    Hugot, Jean-Pierre
    Karban, Amir
    Laukens, Debby
    Lawrance, Ian
    Lemann, Marc
    Levine, Arie
    Libioulle, Cecile
    Louis, Edouard
    Mowat, Craig
    Newman, William
    Panés, Julián
    Phillips, Anne
    Proctor, Deborah D
    Regueiro, Miguel
    Russell, Richard
    Rutgeerts, Paul
    Sanderson, Jeremy
    Sans, Miquel
    Seibold, Frank
    Steinhart, A Hillary
    Stokkers, Pieter C F
    Torkvist, Leif
    Kullak-Ublick, Gerd
    Wilson, David
    Walters, Thomas
    Targan, Stephan R
    Brant, Steven R
    Rioux, John D
    D'Amato, Mauro
    Weersma, Rinse K
    Kugathasan, Subra
    Griffiths, Anne M
    Mansfield, John C
    Vermeire, Severine
    Duerr, Richard H
    Silverberg, Mark S
    Satsangi, Jack
    Schreiber, Stefan
    Cho, Judy H
    Annese, Vito
    Hakonarson, Hakon
    Daly, Mark J
    Parkes, Miles
    Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci2010In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 42, no 12, p. 1118-1125Article in journal (Refereed)
    Abstract [en]

    We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10⁻⁸). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.

  • 5.
    Lilienthal, Achim J.
    et al.
    Örebro University, School of Science and Technology.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Estimating predictive variance for statistical gas distribution modelling2009In: Olfaction and electronic nose: proceedings / [ed] Matteo Pardo, Giorgio Sberveglieri, Melville, USA: American Institute of Physics (AIP), 2009, p. 65-68Conference paper (Refereed)
    Abstract [en]

    Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance 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. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

    Download full text (pdf)
    FULLTEXT01
  • 6.
    Lilienthal, Achim J.
    et al.
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Blanco, Jose Luis
    Dept. of System Engineering and Automation, University of Malaga.
    Gonzalez, Javier
    Örebro University, School of Science and Technology.
    A statistical approach to gas distribution modelling with mobile robots: the Kernel DM+V algorithm2009In: IEEE/RSJ international conference on intelligent robots and systems: IROS 2009, IEEE conference proceedings, 2009, p. 570-576Conference 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.

    Download full text (pdf)
    FULLTEXT01
  • 7.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Statistical gas distribution modelling for mobile robot applications2014Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In this dissertation, we present and evaluate algorithms for statistical gas distribution modelling in mobile robot applications. We derive a representation of the gas distribution in natural environments using gas measurements collected with mobile robots. The algorithms fuse different sensors readings (gas, wind and location) to create 2D or 3D maps.

    Throughout this thesis, the Kernel DM+V algorithm plays a central role in modelling the gas distribution. The key idea is the spatial extrapolation of the gas measurement using a Gaussian kernel. The algorithm produces four maps: the weight map shows the density of the measurements; the confidence map shows areas in which the model is considered being trustful; the mean map represents the modelled gas distribution; the variance map represents the spatial structure of the variance of the mean estimate.

    The Kernel DM+V/W algorithm incorporates wind measurements in the computation of the models by modifying the shape of the Gaussian kernel according to the local wind direction and magnitude.

    The Kernel 3D-DM+V/W algorithm extends the previous algorithm to the third dimension using a tri-variate Gaussian kernel.

    Ground-truth evaluation is a critical issue for gas distribution modelling with mobile platforms. We propose two methods to evaluate gas distribution models. Firstly, we create a ground-truth gas distribution using a simulation environment, and we compare the models with this ground-truth gas distribution. Secondly, considering that a good model should explain the measurements and accurately predicts new ones, we evaluate the models according to their ability in inferring unseen gas concentrations.

    We evaluate the algorithms carrying out experiments in different environments. We start with a simulated environment and we end in urban applications, in which we integrated gas sensors on robots designed for urban hygiene. We found that typically the models that comprise wind information outperform the models that do not include the wind data.

    Download full text (pdf)
    Avhandling
    Download (pdf)
    Cover
    Download (pdf)
    Spikblad
  • 8.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Statistical evaluation of the kernel DM+V/W algorithm for building gas distribution maps in uncontrolled environments2009In: Proceedings of Eurosensors XXIII conference / [ed] Juergen Brugger, Danick Briand, Elsevier, 2009, Vol. 1, p. 481-484Conference paper (Refereed)
    Abstract [en]

    In this paper we present a statistical evaluation of the Kernel DM+V/W algorithm to build two-dimensional gas distribution maps with a mobile robot. In addition to gas sensor measurements from an "e-nose" the Kernel DM+V/W algorithm also takes into account wind information received from an ultrasonic anemometer. We evaluate the method based on real measurements in three uncontrolled environments with very different properties. As a measure for the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. A paired Wilcoxon signed rank test shows a significant improvement (at a confidence level of 95%) of the model quality when using wind information.

    Download full text (pdf)
    FULLTEXT01
  • 9.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The 3D-kernel DM+V/W algorithm: using wind information in three dimensional gas distribution modelling with a mobile robot2010In: 2010 IEEE SENSORS, 2010, p. 999-1004Conference paper (Other academic)
    Abstract [en]

    In this paper we present a statistical method to build three-dimensional gas distribution maps from gas sensor and wind measurements obtained with a mobile robot in uncontrolled environments. The particular contribution of this paper is to introduce and evaluate an algorithm for 3D statistical gas distribution mapping, that takes into account airflow information. 3D-Kernel DM+V/W algorithm uses a multivariate Gaussian weighting function to model the information provided by the gas sensors and an ultrasonic anemometer. The proposed algorithm is evaluated with respect to the ability of the obtained models to predict unseen measurements. The results based on 15 trials with a mobile robot in an indoor environment show improvements in the model performance when using the 3D kernel DM+V/W algorithm. Moreover the model is able to adapt to the dynamical changes of the environment learning the hyper-parameter from the sensors readings.

  • 10.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Three-dimensional statistical gas distribution mapping in an uncontrolled indoor environment2009In: Olfaction and electronic nose / [ed] Matteo Pardo, Giorgio Sberveglieri, 2009, p. 109-112Conference paper (Refereed)
    Abstract [en]

    In this paper we present a statistical method to build three-dimensional gas distribution maps (3D-DM). The proposed mapping technique uses kernel extrapolation with a tri-variate Gaussian kernel that models the likelihood that a reading represents the concentration distribution at a distant location in the three dimensions. The method is evaluated using a mobile robot equipped with three "e-noses" mounted at different heights. Initial experiments in an uncontrolled indoor environment are presented and evaluated with respect to the ability of the 3D map, computed from the lower and upper nose, to predict the map from the middle nose.

    Download full text (pdf)
    FULLTEXT01
  • 11.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using local wind information for gas distribution mapping in outdoor environments with a mobile robot2009In: IEEE sensors, vols 1-3, New York: IEEE conference proceedings, 2009, p. 1637-1642Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a statistical method tobuild two-dimensional gas distribution maps (Kernel DM+V/Walgorithm). In addition to gas sensor measurements, the proposedmethod also takes into account wind information by modelingthe information content of the gas sensor measurements as abivariate Gaussian kernel whose shape depends on the measuredwind vector. We evaluate the method based on real measurementsin an outdoor environment obtained with a mobile robot thatwas equipped with gas sensors and an ultrasonic anemometerfor wind measurements. As a measure of the model quality wecompute how well unseen measurements are predicted in termsof the data likelihood. The initial results are encouraging andshow a clear improvement of the proposed method compared tothe case where wind is not considered.

    Download full text (pdf)
    FULLTEXT01
  • 12.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using local wind information for gas distribution mapping in outdoor environments with a mobile robot2009In: 2009 IEEE SENSORS, VOLS 1-3, NEW YORK: IEEE conference proceedings, 2009, p. 1715-1720Chapter in book (Other academic)
    Abstract [en]

    In this paper we introduce a statistical method to build two-dimensional gas distribution maps (Kernel DM+V/W algorithm). In addition to gas sensor measurements, the proposed method also takes into account wind information by modeling the information content of the gas sensor measurements as a bivariate Gaussian kernel whose shape depends on the measured wind vector. We evaluate the method based on real measurements in an outdoor environment obtained with a mobile robot that was equipped with gas sensors and an ultrasonic anemometer for wind measurements. As a measure of the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. The initial results are encouraging and show a clear improvement of the proposed method compared to the case where wind is not considered.

  • 13.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Mondini, Alessio
    CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Ferri, Gabriele
    CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Mazzolai, Barbara
    Centre in MicroBioRobotics IIT at SSSA, Italian Institute of Technology, Pisa, Italy .
    Manzi, Alessandro
    Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Gabelletti, Matteo
    Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Dario, Paolo
    CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The DustBot System: Using Mobile Robots to Monitor Pollution in Pedestrian Area2010In: Chemical Engineering Transactions, ISSN 1974-9791, E-ISSN 2283-9216, Vol. 23, p. 273-278Article in journal (Refereed)
    Abstract [en]

    The EU project DustBot addresses urban hydeience. Two types of robots were designed, the DustClean robot to autonomously clean pedestrian areas, and the DustCart robot for door-to-door garbage collection. Three prototype robots were built and equipped with electronic noses so as to enable them to collect environmental data while performing their urban hygiene tasks. Essentially, the robots act as a mobile, wirless node in a sensor network. In this paper we give an overview of the DusBot platform focusig on the Air Monitoring Module (AMM). We descibe the data flow between the robots throught the ubiquitous network to a gas distribution modelling server, where a gas deisribution model is computed. We descibe the Kernel DM+V algorithn, an approach to create statistical gas disdtribution models in the form of predictive mean and variance discrtized onto a grid map. Finally we present and discuss results obtained with the DustBot AMM during experimental trails performex in outdoor public places; a courtyard in Pontedera, Italy and a pedestrian square in Örebro, Sweden.

  • 14.
    Trincavelli, Marco
    et al.
    Örebro University, Department of Technology.
    Reggente, Matteo
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Ishida, Hiroshi
    Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Towards environmental monitoring with mobile robots2008In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, New York, NY, USA: IEEE, 2008, p. 2210-2215, article id 4650755Conference 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.

    Download full text (pdf)
    Towards Environmental Monitoring with Mobile Robots
1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf