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  • 1.
    Arain, Muhammad Asif
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Cirillo, Marcello
    Örebro universitet, Institutionen för naturvetenskap och teknik. Scania AB, Granparksvagen 10, SE-15187 Södertälje, Sweden.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots2015Ingår i: 2015 IEEE International Conference on Robotics and Automation (ICRA), Washington, USA: IEEE Computer Society, 2015, 3428-3434 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

  • 2.
    Arain, Muhammad Asif
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Fan, Han
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments2017Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, 2017, 7968895Konferensbidrag (Refereegranskat)
    Abstract [en]

    An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.

  • 3.
    Arain, Muhammad Asif
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    The Right Direction to Smell: Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography2016Ingår i: 2016 IEEE International Conference on Robotics and Automation (ICRA), New York, USA: IEEE Robotics and Automation Society, 2016, 4275-4281 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

  • 4.
    Arain, Muhammad Asif
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Cirillo, Marcello
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor2015Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, nr 3, 6845-6871 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

  • 5.
    Bennetts, Victor Hernandez
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Stoyanov, Todor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments2014Ingår i: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE conference proceedings, 2014, 6362-6367 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.

  • 6.
    Fan, Han
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks2016Ingår i: 2016 IEEE SENSORS, Institute of Electrical and Electronics Engineers (IEEE), 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm. 

  • 7.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Fan, Han
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots2017Ingår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, nr 2, 1117-1123 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling<?brk?> algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback&ndash;Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.

  • 8.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Ferrari, Silvia
    Sibley School of Mechanical and Aerospace Engineering, Cornell University, NY, USA.
    Albertson, John
    School of Civil and Environmental Engineering, Cornell University, NY, USA.
    Integrated Simulation of Gas Dispersion and Mobile Sensing Systems2015Ingår i: Workshop on Realistic, Rapid and Repeatable Robot Simulation, 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    Accidental or intentional releases of contaminants into the atmosphere pose risks to human health, the environment, the economy, and national security. In some cases there may be a single release from an unknown source, while in other cases there are fugitive emissions from multiple sources. The need to locate and characterize the sources efficiently - whether it be the urgent need to evacuate or the systematic need to cover broad geographical regions with limited resources - is shared among all cases. Efforts have begun to identify leaks with gas analyzers mounted on Mobile Robot Olfaction (MRO) systems, road vehicles, and networks of fixed sensors, such as may be based in urban environments. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper, we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing technologies using ROS. This framework is also key to developing and testing optimization and planning algorithms for determining sensor placement and sensor motion, as well as for fusing and connecting the sensor measurements to the leak locations.

  • 9.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Fan, Han
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Andersson, Lena
    Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden.
    Johansson, Anders
    Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden.
    Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes2016Ingår i: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, 131-136 s., 7759045Konferensbidrag (Refereegranskat)
    Abstract [en]

    In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities.

  • 10.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Marco, Santiago
    Signal and Information Processing for Sensing Systema, Institute for Bioengineering of Catalonia, Barcelona, Spain; Departament d’Electrònica, Universitat de Barcelona, Barcelona, Spain.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds2014Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, nr 9, 17331-17352 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

  • 11.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Spain.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems2014Ingår i: Proceedings of the IEEE Sensors Conference 2014, IEEE conference proceedings, 2014, -2049 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters.

  • 12.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Stoyanov, Todor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Robot assisted gas tomography: an alternative approach for the detection of fugitive methane emissions2014Ingår i: Workshop on Robot Monitoring, 2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements.

  • 13.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Stoyanov, Todor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Robot assisted gas tomography: localizing methane leaks in outdoor environments2014Ingår i: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.

  • 14.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Spain.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Online parameter selection for gas distribution mapping2013Ingår i: Proceedings of the ISOEN conference 2013, 2013Konferensbidrag (Refereegranskat)
  • 15.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Online parameter selection for gas distribution mapping2014Ingår i: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, nr 6-7, 1147-1151 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The ability to produce truthful maps of the distribution of one or more gases is beneficial for applications ranging from environmental monitoring to mines and industrial plants surveillance. Realistic environments are often too complicated for applying analytical gas plume models or performing reliable CFD simulations, making data-driven statistical gas distribution models the most attractive alternative. However, statistical models for gas distribution modelling, often rely on a set of meta-parameters that need to be learned from the data through Cross Validation (CV) techniques. CV techniques are computationally expensive and therefore need to be computed offline. As a faster alternative, we propose a parameter selection method based on Virtual Leave-One-Out Cross Validation (VLOOCV) that enables online learning of meta-parameters. In particular, we consider the Kernel DM+V, one of the most well studied algorithms for statistical gas distribution mapping, which relies on a meta-parameter, the kernel bandwidth. We validate the proposed VLOOCV method on a set of indoor and outdoor experiments where a mobile robot with a Photo Ionization Detector (PID) was collecting gas measurements. The approximation provided by the proposed VLOOCV method achieves very similar results to plain Cross Validation at a fraction of the computational cost. This is an important step in the development of on-line statistical gas distribution modelling algorithms.

  • 16.
    Ishida, Hiroshi
    et al.
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Matsukura, Haruka
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Using Chemical Sensors as 'Noses' for Mobile Robots2016Ingår i: Essentials of Machine Olfaction and Taste / [ed] Takamichi Nakamoto, Singapore: John Wiley & Sons, 2016, 219-246 s.Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Gas sensors detect the presence of gaseous chemical compounds in air. They are often used in the form of gas alarms for detecting dangerous or hazardous gases. However, a limited number of stationary gas alarms may not be always sufficient to cover a large industrial facility. Human workers having a portable gas detector in their hand needs to be sent to thoroughly check gas leaks in the areas not covered by stationary gas alarms. However, making repetitive measurements with a gas detector at a number of different locations is laborious. Moreover, the places where the gas concentration level needs to be checked are often potentially dangerous for human workers. If a portable gas detector is mounted on a mobile robot, the task of patrolling in an industrial facility for checking a gas leak can be automated. Robots are good at doing repetitive tasks, and can be sent into harsh environments.

  • 17.
    Kaltenhaeuser, Robert
    et al.
    Ilmenau University of Technology, Germany.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Steege, Frank-Florian
    Ilmenau University of Technology, Germany.
    Gross, Horst-Michael
    Ilmenau University of Technology, Germany.
    Evolutionary computation based system decomposition with neural networks2013Ingår i: ESANN 2013 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligenceand Machine Learning, Louvain-La-Neuve, 2013, 191-196 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present an evolutionary approach to divide a complex control system into smaller sub-systems with the help of neural networks.Thereto, measured channels are partitioned into several disjunct sets, rep-resenting possible sub-problems, while the networks are used to assessthe quality of the resulting decomposition. We show that this approach iswell suited to calculate correct decompositions of complex control systems.Furthermore, the obtained neural networks are used to predict importantprocess factors with considerable better approximation quality than mono-lithic approaches that have to deal with all input channels in parallel.

  • 18.
    Khaliq, Ali
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Pashami, Sepideh
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bringing Artificial Olfaction and Mobile Robotics Closer Together: An Integrated 3D Gas Dispersion Simulator in ROS2015Ingår i: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses, 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework.

  • 19.
    Kucner, Tomasz
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Tell me about dynamics!: Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models2016Ingår i: Robotics: Science and Systems Conference (RSS 2016), 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures.

  • 20.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor Manuel
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017Ingår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, nr 2, 1093-1100 s.Artikel i tidskrift (Refereegranskat)
    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.

  • 21.
    Lilienthal, Achim J.
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    It's always smelly around here! Modeling the Spatial Distribution of Gas Detection Events with BASED Grid Maps2013Ingår i: Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013), 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of the gas concentration, models the spatial distribution of events of detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting a Bayesian approach is used with a beta distribution prior for the parameter u that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e. will be a beta distribution, enabling a simple approach for sequential learning. To learn a field of beta distributions, we discretize the inspection area into a grid map and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for different sensors mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors.

  • 22.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bouguerra, Abdelbaki
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks2014Ingår i: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE Robotics and Automation Society, 2014, 5685-5690 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations.

  • 23.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bouguerra, Abdelbaki
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Support relation analysis and decision making for safe robotic manipulation tasks2015Ingår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 71, nr SI, 99-117 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this article, we describe an approach to address the issue of automatically building and using high-level symbolic representations that capture physical interactions between objects in static configurations. Our work targets robotic manipulation systems where objects need to be safely removed from piles that come in random configurations. We assume that a 3D visual perception module exists so that objects in the piles can be completely or partially detected. Depending on the outcome of the perception, we divide the issue into two sub-issues: 1) all objects in the configuration are detected; 2) only a subset of objects are correctly detected. For the first case, we use notions from geometry and static equilibrium in classical mechanics to automatically analyze and extract act and support relations between pairs of objects. For the second case, we use machine learning techniques to estimate the probability of objects supporting each other. Having the support relations extracted, a decision making process is used to identify which object to remove from the configuration so that an expected minimum cost is optimized. The proposed methods have been extensively tested and validated on data sets generated in simulation and from real world configurations for the scenario of unloading goods from shipping containers.

  • 24.
    Mosberger, Rafael
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Andreasson, Henrik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Inferring human body posture information from reflective patterns of protective work garments2016Ingår i: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, 4131-4136 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    We address the problem of extracting human body posture labels, upper body orientation and the spatial location of individual body parts from near-infrared (NIR) images depicting patterns of retro-reflective markers. The analyzed patterns originate from the observation of humans equipped with protective high-visibility garments that represent common safety equipment in the industrial sector. Exploiting the shape of the observed reflectors we adopt shape matching based on the chamfer distance and infer one of seven discrete body posture labels as well as the approximate upper body orientation with respect to the camera. We then proceed to analyze the NIR images on a pixel scale and estimate a figure-ground segmentation together with human body part labels using classification of densely extracted local image patches. Our results indicate a body posture classification accuracy of 80% and figure-ground segmentations with 87% accuracy.

  • 25.
    Pashami, Sepideh
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors2014Ingår i: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, nr 6/7, 1123-1127 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

     The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms.

  • 26.
    Pashami, Sepideh
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    TREFEX: trend estimation and change detection in the response of mox gas sensors2013Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, nr 6, 7323-7344 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Many applications of metal oxide gas sensors can benefit from reliable algorithmsto detect significant changes in the sensor response. Significant changes indicate a changein the emission modality of a distant gas source and occur due to a sudden change ofconcentration or exposure to a different compound. As a consequence of turbulent gastransport and the relatively slow response and recovery times of metal oxide sensors,their response in open sampling configuration exhibits strong fluctuations that interferewith the changes of interest. In this paper we introduce TREFEX, a novel change pointdetection algorithm, especially designed for metal oxide gas sensors in an open samplingsystem. TREFEX models the response of MOX sensors as a piecewise exponentialsignal and considers the junctions between consecutive exponentials as change points. Weformulate non-linear trend filtering and change point detection as a parameter-free convexoptimization problem for single sensors and sensor arrays. We evaluate the performanceof the TREFEX algorithm experimentally for different metal oxide sensors and severalgas emission profiles. A comparison with the previously proposed GLR method shows aclearly superior performance of the TREFEX algorithm both in detection performance andin estimating the change time.

  • 27.
    Schaffernicht, Erik
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Trincavelli, Marco
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events2014Ingår i: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, nr 6-7, 1142-1146 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of a quasi-continuous gas concentration, models the spatial distribution of gas events, for example detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian Inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting, a Bayesian approach is used with a beta distribution prior for the parameter μ that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e., will be a beta distribution as well, enabling a simple approach for sequential learning. To learn a map composed of beta distributions, we discretize the inspection area into a grid and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for MOX sensors and a photo ionization detector mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors.

  • 28.
    Vuka, Mikel
    et al.
    Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schmuker, Michael
    University of Hertfordshire, School of Computer Science, College Lane, Hatfield, Herts, United Kingdom.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Amigoni, Francesco
    Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
    Lilienthal, Achim J
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot: A Gaussian Regression Bout Amplitude Approach2017Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, 164-166 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mobile robot olfaction systems combine gas sensorswith mobility provided by robots. They relief humansof dull, dirty and dangerous tasks in applications such assearch & rescue or environmental monitoring. We address gassource localization and especially the problem of minimizingexploration time of the robot, which is a key issue due toenergy constraints. We propose an active search approach forrobots equipped with MOX gas sensors and an anemometer,given an occupancy map. Events of rapid change in the MOXsensor signal (“bouts”) are used to estimate the distance to agas source. The wind direction guides a Gaussian regression,which interpolates distance estimates. The contributions of thispaper are two-fold. First, we extend previous work on gassource distance estimation with MOX sensors and propose amodification to cope better with turbulent conditions. Second,we introduce a novel active search gas source localizationalgorithm and validate it in a real-world environment.

  • 29.
    Wiedemann, Thomas
    et al.
    Institute of Communications and Navigation German Aerospace Center (DLR), Wessling, Germany.
    Shutin, Dmitri
    Institute of Communications and Navigation German Aerospace Center (DLR), Wessling, Germany.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bayesian Gas Source Localization and Explorationwith a Multi-Robot System Using PartialDifferential Equation Based Modeling2017Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, 2017, 122-124 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time.

  • 30.
    Zhang, Ye
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Gulliksson, Mårten
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Reconstructing gas distribution maps via an adaptive sparse regularization algorithm2016Ingår i: Inverse Problems in Science and Engineering, ISSN 1741-5977, E-ISSN 1741-5985, Vol. 24, nr 7, 1186-1204 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we present an algorithm to be used by an inspectionrobot to produce a gas distribution map and localize gas sources ina large complex environment. The robot, equipped with a remotegas sensor, measures the total absorption of a tuned laser beam andreturns integral gas concentrations. A mathematical formulation ofsuch measurement facility is a sequence of Radon transforms,which isa typical ill-posed problem. To tackle the ill-posedness, we developa new regularization method based on the sparse representationproperty of gas sources and the adaptive finite-element method. Inpractice, only a discrete model can be applied, and the quality ofthe gas distributionmap depends on a detailed 3-D world model thatallows us to accurately localize the robot and estimate the paths of thelaser beam. In this work, using the positivity ofmeasurements and theprocess of concentration, we estimate the lower and upper boundsof measurements and the exact continuous model (mapping fromgas distribution to measurements), and then create a more accuratediscrete model of the continuous tomography problem. Based onadaptive sparse regularization, we introduce a new algorithm thatgives us not only a solution map but also a mesh map. The solutionmap more accurately locates gas sources, and the mesh map providesthe real gas distribution map. Moreover, the error estimation of theproposed model is discussed. Numerical tests for both the syntheticproblem and practical problem are given to show the efficiency andfeasibility of the proposed algorithm.

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