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Kucner, Tomasz PiotrORCID iD iconorcid.org/0000-0002-9503-0602
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Publications (10 of 11) Show all publications
Fan, H., Kucner, T. P., Magnusson, M., Li, T. & Lilienthal, A. (2018). A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment. IEEE transactions on intelligent transportation systems (Print), 19(9), 2977-2993
Open this publication in new window or tab >>A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment
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2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 9, p. 2977-2993Article in journal (Refereed) Published
Abstract [en]

Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Mobile robot, occupancy filtering, PHD filter, BOF, particle filter, random finite set
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-63981 (URN)10.1109/TITS.2017.2770152 (DOI)000444611400021 ()2-s2.0-85038368968 (Scopus ID)
Note

Funding Agencies:

EU Project SPENCER  600877 

Marie Sklodowska-Curie Individual Fellowship  709267 

National Twelfth Five-Year Plan for Science and Technology Support of China  2014BAK12B03 

Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-09-28Bibliographically approved
Swaminathan, C. S., Kucner, T. P., Magnusson, M., Palmieri, L. & Lilienthal, A. (2018). Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 31st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018 (pp. 7403-7409). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty
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2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 7403-7409Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Trajectory, Robots, Planning, Cost function, Uncertainty, Veichle dynamics, Aerospace electronics
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-70143 (URN)10.1109/IROS.2018.8593905 (DOI)
Conference
31st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018
Projects
ILIAD
Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2019-01-08Bibliographically approved
Almqvist, H., Magnusson, M., Kucner, T. P. & Lilienthal, A. (2018). Learning to detect misaligned point clouds. Journal of Field Robotics, 35(5), 662-677
Open this publication in new window or tab >>Learning to detect misaligned point clouds
2018 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed) Published
Abstract [en]

Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
perception, mapping, position estimation
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62985 (URN)10.1002/rob.21768 (DOI)000437836900002 ()2-s2.0-85037622789 (Scopus ID)
Projects
ILIADALLO
Funder
EU, Horizon 2020, 732737Knowledge Foundation, 20110214
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2018-07-27Bibliographically approved
Kucner, T. P. (2018). Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots. (Doctoral dissertation). Örebro: Örebro University
Open this publication in new window or tab >>Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots
2018 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

To bring robots closer to real-world autonomy, it is necessary to equip them with tools allowing them to perceive, model and behave adequately to dynamic changes in the environment. The idea of incorporating information about dynamics not only in the robots reactive behaviours but also in global planning process stems from the fact that dynamic changes are typically not completely random and follow spatiotemporal patterns. The overarching idea behind the work presented in this thesis is to investigate methods allowing to represent the variety of the real-world spatial motion patterns in a compact, yet expressive way. The primary focus of the presented work is on building maps capturing the motion patterns of dynamic objects and/or the flow of continuous media.

The contribution of this thesis is twofold. First, I introduce Conditional-Transition Map: a representation for modelling motion patterns of dynamic objects as a multimodal flow of occupancy over a grid map. Furthermore, in this thesis I also propose an extension (Temporal Conditional-Transition Map), which models the speed of said flow. The proposed representations connect the changes of occupancy among adjacent cells. Namely, they build conditional models of the direction to where occupancy is heading given the direction from which the occupancy arrived. Previously, all of the representations modelling dynamics in grid maps assumed cell independence. The representations assuming cell independence are substantially less expressive and store only information about the observed levels of dynamics (i.e. how frequent changes are at a certain location). In contrast, the proposed representations also encode information about the direction of motion. Furthermore, the multimodal and conditional character of the representations allows to distinguish and correctly model intersecting flows. The capabilities of the introduced grid-based representations are demonstrated with experiments performed on real-world data sets.

In the second part of this thesis, I introduce Circular Linear Flow Field map modelling flow of continuous media and discrete objects. This representation, in contrast to the work presented in the first part of this thesis, does not model occupancy changes directly. Instead, it employs a field of Gaussian Mixture Models, whose local elements are probability distributions of (instantaneous) velocities, to describe motion patterns. Since it assumes only velocity measurements, the proposed representation have been used to model a broad spectrum of dynamics including motion patterns of people and airflow. Using a Gaussian Mixture Model allows to capture the multimodal character of real-world dynamics (e.g. intersecting flows) and also to account for flow variability. In addition to the basic learning algorithms, I present solutions (sampling-based and kernel-based approach) for the problem of building a dense Circular Linear Flow Field map using spatially sparse but temporally dense sets of measurements. In the end, I present how to use the Circular Linear Flow Field map in motion planning to achieve flow compliant trajectories. The capabilities of Circular Linear Flow Field maps are presented and evaluated using simulated and real-world datasets.

The spectrum of applications for the representations and approaches presented in this thesis is very broad. Among others, the results of this thesis can be used by service robots providing help for passengers in crowded airports or drones surveying landfills to detect leakages of greenhouse gases. In the case of a service robot interacting with passengers in a populated airport, the information about the flow of passengers allows to build not only the shortest path between points “A” and “B” but also enables the robot to behave seamlessly, unobtrusively and safely. In the case of a drone patrolling a landfill the impact of airflow, is equally significant. In this scenario, information about airflow allows harnessing the energy of airstreams to lower the energy consumption of a drone. Another way to utilise information about the wind flow is to use it to improve localisation of sources of gas leakage.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2018. p. 136
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 80
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-68024 (URN)978-91-7529-255-7 (ISBN)
Public defence
2018-09-20, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2018-07-23 Created: 2018-07-23 Last updated: 2018-08-31Bibliographically approved
Kucner, T. P., Magnusson, M., Schaffernicht, E., Hernandez Bennetts, V. M. & Lilienthal, A. (2017). Enabling Flow Awareness for Mobile Robots in Partially Observable Environments. IEEE Robotics and Automation Letters, 2(2), 1093-1100
Open this publication in new window or tab >>Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
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2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed) Published
Abstract [en]

Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Field robots; mapping; social human-robot interaction
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-55174 (URN)10.1109/LRA.2017.2660060 (DOI)000413736600094 ()
Projects
ILIAD
Funder
Knowledge Foundation, 20140220 20130196
Note

Funding Agencies:

EU project SPENCER  ICT-2011-600877 

H2020-ICT project SmokeBot  645101 

H2020-ICT project ILIAD  732737 

Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2017-11-23Bibliographically approved
Palmieri, L., Kucner, T., Magnusson, M., Lilienthal, A. & Arras, K. (2017). Kinodynamic Motion Planning on Gaussian Mixture Fields. In: IEEE International Conference on Robotics and Automation (ICRA 2017): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore, May 29 - June 03, 2017.
Open this publication in new window or tab >>Kinodynamic Motion Planning on Gaussian Mixture Fields
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2017 (English)In: IEEE International Conference on Robotics and Automation (ICRA 2017), 2017Conference paper, Published paper (Refereed)
Abstract [en]

We present a mobile robot motion planning ap-proach under kinodynamic constraints that exploits learnedperception priors in the form of continuous Gaussian mixturefields. Our Gaussian mixture fields are statistical multi-modalmotion models of discrete objects or continuous media in theenvironment that encode e.g. the dynamics of air or pedestrianflows. We approach this task using a recently proposed circularlinear flow field map based on semi-wrapped GMMs whosemixture components guide sampling and rewiring in an RRT*algorithm using a steer function for non-holonomic mobilerobots. In our experiments with three alternative baselines,we show that this combination allows the planner to veryefficiently generate high-quality solutions in terms of pathsmoothness, path length as well as natural yet minimum controleffort motions through multi-modal representations of Gaussianmixture fields.

National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-55177 (URN)
Conference
IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore, May 29 - June 03, 2017
Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2018-07-30Bibliographically approved
Magnusson, M., Kucner, T. P., Gholami Shahbandi, S., Andreasson, H. & Lilienthal, A. (2017). Semi-Supervised 3D Place Categorisation by Descriptor Clustering. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) Vancouver, Canada, September 24–28, 2017 (pp. 620-625). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Semi-Supervised 3D Place Categorisation by Descriptor Clustering
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2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 620-625Conference paper, Published paper (Refereed)
Abstract [en]

Place categorisation; i. e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data.

This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold.

This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-61903 (URN)10.1109/IROS.2017.8202216 (DOI)000426978201006 ()2-s2.0-85041949592 (Scopus ID)978-1-5386-2682-5 (ISBN)978-1-5386-2683-2 (ISBN)
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) Vancouver, Canada, September 24–28, 2017
Projects
ILIAD
Funder
EU, Horizon 2020, 732737
Note

Iliad Project: http://iliad-project.eu

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2018-04-09Bibliographically approved
Triebel, R., Arras, K., Alami, R., Beyer, L., Breuers, S., Chatila, R., . . . Zhang, L. (2016). SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports. In: David S. Wettergreen, Timothy D. Barfoot (Ed.), Field and Service Robotics: Results of the 10th International Conference. Paper presented at 10th International Conference on Field and Service Robotics (FSR), Toronto, Canada, June 23-26, 2015 (pp. 607-622). Springer
Open this publication in new window or tab >>SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports
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2016 (English)In: Field and Service Robotics: Results of the 10th International Conference / [ed] David S. Wettergreen, Timothy D. Barfoot, Springer, 2016, p. 607-622Conference paper, Published paper (Refereed)
Abstract [en]

We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

Place, publisher, year, edition, pages
Springer, 2016
Series
Springer Tracts in Advanced Robotics, ISSN 1610-7438 ; 113
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-51410 (URN)10.1007/978-3-319-27702-8_40 (DOI)000377201600040 ()2-s2.0-84961262357 (Scopus ID)978-3-319-27702-8 (ISBN)978-3-319-27700-4 (ISBN)
Conference
10th International Conference on Field and Service Robotics (FSR), Toronto, Canada, June 23-26, 2015
Available from: 2016-07-27 Created: 2016-07-19 Last updated: 2018-07-16Bibliographically approved
Magnusson, M., Kucner, T. & Lilienthal, A. J. (2015). Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling. In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE): . Paper presented at IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, August 24-28, 2015 (pp. 450-455). New York: IEEE conference proceedings
Open this publication in new window or tab >>Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling
2015 (English)In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), New York: IEEE conference proceedings , 2015, p. 450-455Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous handling of piled materials is an emerging topic in automation science and engineering. A central question for material rehandling tasks (transporting materials that have been assembled in piles) is “where to dig, in order to optimise performance”? In particular, we are interested in the application of autonomous wheel loaders to handle piles of gravel. Still, the methodology proposed in this paper relates to granular materials in other applications too. Although initial work on suggesting strategies for where to dig has been done by a few other groups, there has been a lack of structured evaluation of the usefulness of the proposed strategies. In an attempt to further the field, we present a quantitative evaluation of loading strategies; both coarse ones, aiming to maintain a good pile shape over long-term operation; and refined ones, aiming to detect the locally best attack pose for acquiring a good fill grade in the bucket. Using real-world data from a semi-automated test platform, we present an assessment of how previously proposed pile shape measures can be mapped to the amount of material in the bucket after loading. We also present experimental data for long-term strategies, using simulations based on real-world 3D scan data from a production site.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2015
Series
IEEE International Conference on Automation Science and Engineering (CASE), ISSN 2161-8070
Keywords
Emerging Topics in Automation, Automation for Machine Tools, Sustainable Production
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-45677 (URN)10.1109/CoASE.2015.7294120 (DOI)000380453000073 ()2-s2.0-84952765805 (Scopus ID)978-1-4673-8183-3 (ISBN)
Conference
IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, August 24-28, 2015
Projects
ALLO
Funder
Knowledge Foundation, 20110214
Available from: 2015-08-28 Created: 2015-08-28 Last updated: 2018-01-11Bibliographically approved
Kucner, T. P., Magnusson, M. & Lilienthal, A. J. (2015). Where am I?: An NDT-based prior for MCL. In: 2015 European Conference on Mobile Robots (ECMR): . Paper presented at 7th European Conference on Mobile Robots, Lincoln, England, September 2-4, 2015. New York: IEEE conference proceedings
Open this publication in new window or tab >>Where am I?: An NDT-based prior for MCL
2015 (English)In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Conference paper, Published paper (Refereed)
Abstract [en]

One of the key requirements of autonomous mobile robots is a robust and accurate localisation system. Recent advances in the development of Monte Carlo Localisation (MCL) algorithms, especially the Normal Distribution Transform Monte Carlo Localisation (NDT-MCL), provides memory-efficient reliable localisation with industry-grade precision. We propose an approach for building an informed prior for NDT-MCL (in fact for any MCL algorithm) using an initial observation of the environment and its map. Leveraging on the NDT map representation, we build a set of poses using partial observations. After that we construct a Gaussian Mixture Model (GMM) over it. Next we obtain scores for each distribution in GMM. In this way we obtain in an efficient way a prior for NDT-MCL. Our approach provides a more focused then uniform initial distribution, concentrated in states where the robot is more likely to be, by building a Gaussian mixture model over potential poses. We present evaluations and quantitative results using real-world data from an indoor environment. Our experiments show that, compared to a uniform prior, the proposed method significantly increases the number of successful initialisations of NDT-MCL and reduces the time until convergence, at a negligible initial cost for computing the prior.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2015
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-51942 (URN)10.1109/ECMR.2015.7324175 (DOI)000380213600010 ()2-s2.0-84962243582 (Scopus ID)978-1-4673-9163-4 (ISBN)
Conference
7th European Conference on Mobile Robots, Lincoln, England, September 2-4, 2015
Projects
SPENCER
Funder
EU, FP7, Seventh Framework Programme, ICT-2011- 600877
Available from: 2016-09-02 Created: 2016-09-02 Last updated: 2018-01-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9503-0602

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