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Wiedemann, T., Lilienthal, A. & Shutin, D. (2019). Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge. Sensors, 19(3), Article ID 520.
Open this publication in new window or tab >>Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id 520Article in journal (Refereed) Published
Abstract [en]

In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019
Keywords
Robotic exploration, gas source localization, mobile robot olfaction, sparse Bayesian learning, multi-agent system, advection-diffusion model
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71964 (URN)10.3390/s19030520 (DOI)000459941200083 ()30691174 (PubMedID)2-s2.0-85060572534 (Scopus ID)
Projects
SmokeBot (EC H2020, 645101)
Note

Funding Agencies:

European Commission  645101 

Valles Marineris Explorer initiative of DLR (German Aerospace Center) Space Administration 

Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-06-19Bibliographically approved
Schindler, M. & Lilienthal, A. J. (2019). Domain-specific interpretation of eye tracking data: towards a refined use of the eye-mind hypothesis for the field of geometry. Educational Studies in Mathematics, 101(1), 123-139
Open this publication in new window or tab >>Domain-specific interpretation of eye tracking data: towards a refined use of the eye-mind hypothesis for the field of geometry
2019 (English)In: Educational Studies in Mathematics, ISSN 0013-1954, E-ISSN 1573-0816, Vol. 101, no 1, p. 123-139Article in journal (Refereed) Published
Abstract [en]

Eye tracking is getting increasingly popular in mathematics education research. Studies predominantly rely on the so-called eye-mind hypothesis (EMH), which posits that what persons fixate on closely relates to what they process. Given that the EMH was developed in reading research, we see the risk that implicit assumptions are tacitly adopted in mathematics even though they may not apply in this domain. This article investigates to what extent the EMH applies in mathematics - geometry in particular - and aims to lift the discussion of what inferences can be validly made from eye-tracking data. We use a case study to investigate the need for a refinement of the use of the EMH. In a stimulated recall interview, a student described his original thoughts perusing a gaze-overlaid video recorded when he was working on a geometry problem. Our findings contribute to better a understanding of when and how the EMH applies in the subdomain of geometry. In particular, we identify patterns of eye movements that provide valuable information on students' geometry problem solving: certain patterns where the eye fixates on what the student is processing and others where the EMH does not hold. Identifying such patterns may contribute to an interpretation theory for students' eye movements in geometry - exemplifying a domain-specific theory that may reduce the inherent ambiguity and uncertainty that eye tracking data analysis has.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Eye tracking, Eye movements, Eye-mind hypothesis, Geometry
National Category
Educational Sciences
Identifiers
urn:nbn:se:oru:diva-73868 (URN)10.1007/s10649-019-9878-z (DOI)000463669800009 ()2-s2.0-85061182709 (Scopus ID)
Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-04-23Bibliographically approved
Wiedemann, T., Shutin, D. & Lilienthal, A. (2019). Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties. Robotics and Autonomous Systems, 118, 66-79
Open this publication in new window or tab >>Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties
2019 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 118, p. 66-79Article in journal (Refereed) Published
Abstract [en]

Sampling gas distributions by robotic platforms in order to find gas sources is an appealing approach to alleviate threats for a human operator. Different sampling strategies for robotic gas exploration exist. In this paper we investigate the benefit that could be obtained by incorporating physical knowledge about the gas dispersion. By exploring a gas diffusion process using a multi-robot system. The physical behavior of the diffusion process is modeled using a Partial Differential Equation (PDE) which is integrated into the exploration strategy. It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The objective of the exploration strategy is to guide the robots to informative measurement locations and by means of concentration measurements estimate the source parameters, in particular, their number, locations and magnitudes. To this end we propose a probabilistic approach towards PDE identification under sparsity constraints using factor graphs and a message passing algorithm. Moreover, message passing schemes permit efficient distributed implementation of the algorithm, which makes it suitable for a multi-robot system. We designed an experimental setup that allows us to evaluate the performance of the exploration strategy in hardware-in-the-loop experiments as well as in experiments with real ethanol gas under laboratory conditions. The results indicate that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling. (C) 2019 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Robotic exploration, Gas source localization, Multi-agent-system, Partial differential equation, Mobile robot olfaction, Sparse Bayesian learning, Factor graph, Message passing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-75365 (URN)10.1016/j.robot.2019.03.014 (DOI)000474324100006 ()2-s2.0-85065544153 (Scopus ID)
Funder
EU, European Research Council, 645101
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-29Bibliographically approved
Hernandez Bennetts, V., Kamarudin, K., Wiedemann, T., Kucner, T. P., Somisetty, S. L. & Lilienthal, A. (2019). Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning. Sensors, 19(5), Article ID E1119.
Open this publication in new window or tab >>Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning
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2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 5, article id E1119Article in journal (Refereed) Published
Abstract [en]

Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization-measuring and modelling air flow at micro-scales-that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed ("ventilation maps", spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Airflow modeling, environmental monitoring, machine learning, mobile robotics, static sensor networks, ventilation
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-73199 (URN)10.3390/s19051119 (DOI)000462540400138 ()30841615 (PubMedID)2-s2.0-85062613532 (Scopus ID)
Funder
Vinnova, 2017-05468
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-06-19Bibliographically approved
Burgués, J., Hernandez Bennetts, V., Lilienthal, A. & Marco, S. (2019). Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping. Sensors, 19(3), Article ID 478.
Open this publication in new window or tab >>Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id 478Article in journal (Refereed) Published
Abstract [en]

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019
Keywords
Robotics, signal processing, electronics, gas source localization, gas distribution mapping; gas sensors, drone, UAV, MOX sensor, quadcopter
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71963 (URN)10.3390/s19030478 (DOI)000459941200041 ()30682827 (PubMedID)2-s2.0-85060510907 (Scopus ID)
Note

Funding Agency:

Spanish MINECO  BES-2015-071698  TEC2014-59229-R

Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-06-19Bibliographically approved
Mielle, M., Magnusson, M. & Lilienthal, A. (2019). The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM. Robotics, 8(2), Article ID 40.
Open this publication in new window or tab >>The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM
2019 (English)In: Robotics, E-ISSN 2218-6581, Vol. 8, no 2, article id 40Article in journal (Refereed) Published
Abstract [en]

Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map's errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
SLAM, prior map, emergency map, layout map, graph-based SLAM, navigation, search and rescue
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-75742 (URN)10.3390/robotics8020040 (DOI)000475325600017 ()2-s2.0-85069926702 (Scopus ID)
Funder
Knowledge Foundation, 20140220
Note

Funding Agency:

EU  ICT-26-2016 732737  ICT-23-2014 645101

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (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, ISSN 1424-8220, 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: 2019-06-19Bibliographically approved
Mielle, M., Magnusson, M. & Lilienthal, A. (2019). URSIM: Unique Regions for Sketch Map Interpretation and Matching. Robotics, 8(2), Article ID 43.
Open this publication in new window or tab >>URSIM: Unique Regions for Sketch Map Interpretation and Matching
2019 (English)In: Robotics, E-ISSN 2218-6581, Vol. 8, no 2, article id 43Article in journal (Refereed) Published
Abstract [en]

We present a method for matching sketch maps to a corresponding metric map, with the aim of later using the sketch as an intuitive interface for human-robot interactions. While sketch maps are not metrically accurate and many details, which are deemed unnecessary, are omitted, they represent the topology of the environment well and are typically accurate at key locations. Thus, for sketch map interpretation and matching, one cannot only rely on metric information. Our matching method first finds the most distinguishable, or unique, regions of two maps. The topology of the maps, the positions of the unique regions, and the size of all regions are used to build region descriptors. Finally, a sequential graph matching algorithm uses the region descriptors to find correspondences between regions of the sketch and metric maps. Our method obtained higher accuracy than both a state-of-the-art matching method for inaccurate map matching, and our previous work on the subject. The state of the art was unable to match sketch maps while our method performed only 10% worse than a human expert.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
map matching, sketch, human-robot interaction, interface, graph matching, segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-75741 (URN)10.3390/robotics8020043 (DOI)000475325600020 ()2-s2.0-85069975721 (Scopus ID)
Funder
Knowledge Foundation, 20140220
Note

Funding Agency:

EU  ICT-26-2016 732737

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved
Fan, H., Lu, D., Kucner, T. P., Magnusson, M. & Lilienthal, A. (2018). 2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map. In: Proceedings of 21st International Conference on Information Fusion (FUSION): . Paper presented at 21st International Conference on Information Fusion (FUSION), Cambridge, UK, July 10 - 13, 2018 (pp. 2400-2406).
Open this publication in new window or tab >>2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map
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2018 (English)In: Proceedings of 21st International Conference on Information Fusion (FUSION), 2018, p. 2400-2406Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a novel sub-pixel motionextraction method, called as Two Dimensional Spatial KeystoneTransform (2DS-KST), for the motion detection and estimationfrom successive noisy Occupancy Grid Maps (OGMs). It extendsthe KST in radar imaging or motion compensation to 2Dreal spatial case, based on multiple hypotheses about possibledirections of moving obstacles. Simulation results show that 2DSKSThas a good performance on the extraction of sub-pixelmotions in very noisy environment, especially for those slowlymoving obstacles.

Keywords
robotics, occupancy grid map, motion extraction, keystone transform, 2DS-KST, sub-pixel
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71953 (URN)10.23919/ICIF.2018.8455274 (DOI)978-0-9964527-6-2 (ISBN)978-1-5386-4330-3 (ISBN)
Conference
21st International Conference on Information Fusion (FUSION), Cambridge, UK, July 10 - 13, 2018
Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-02-01Bibliographically approved
Burgués, J., Hernandez Bennetts, V., Lilienthal, A. & Marco, S. (2018). 3D Gas Distribution with and without Artificial Airflow: An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors. Proceedings, 2(13), Article ID 911.
Open this publication in new window or tab >>3D Gas Distribution with and without Artificial Airflow: An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors
2018 (English)In: Proceedings, E-ISSN 2504-3900, Vol. 2, no 13, article id 911Article in journal (Refereed) Published
Abstract [en]

Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2018
Keywords
MOX, metal oxide, flow visualization, gas sensors, gas distribution mapping, sensor grid, 3D, gas source localization, indoor
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71962 (URN)10.3390/proceedings2130911 (DOI)
Projects
SmokeBot (EC H2020, 645101)
Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-02-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0217-9326

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