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  • 1.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology. Scania AB, Södertälje, Sweden.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots2015In: 2015 IEEE International Conference on Robotics and Automation (ICRA), Washington, USA: IEEE, 2015, p. 3428-3434Conference paper (Refereed)
    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.

    Download full text (pdf)
    fulltext
  • 2.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments2017In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE, 2017, article id 7968895Conference paper (Refereed)
    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 University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Mobile Robotics and Olfaction (MRO) Lab, Center for Applied Autonomous Sensor Systems (AASS), School of Science and Technology, Örebro University, Örebro, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot2021In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 40, no 4-5, p. 782-814Article in journal (Refereed)
    Abstract [en]

    Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our ‘‘Autonomous Remote Methane Explorer’’ (ARMEx) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated anARMExprototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route.

  • 4.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The Right Direction to Smell: Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography2016In: 2016 IEEE International Conference on Robotics and Automation (ICRA), New York, USA: IEEE Robotics and Automation Society, 2016, p. 4275-4281Conference paper (Refereed)
    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.

    Download full text (pdf)
    Arain_etal_ICRA-2016
  • 5.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor2015In: Sensors, E-ISSN 1424-8220, Vol. 15, no 3, p. 6845-6871Article in journal (Refereed)
    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.

    Download full text (pdf)
    Arain-Sensors2015
  • 6.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data2018In: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 63, p. 691-742Article in journal (Refereed)
    Abstract [en]

    There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore,  comparisons are made with descriptions derived on alternative approaches for generating semantic models.

    Download full text (pdf)
    Data Driven Conceptual Spaces, Banaee et. al, JAIR 2018
  • 7.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Davison, Andrew J.
    Department of Computing, Imperial College London, London, United Kingdom.
    Compressed Voxel-Based Mapping Using Unsupervised Learning2017In: Robotics, E-ISSN 2218-6581, Vol. 6, no 3, article id 15Article in journal (Refereed)
    Abstract [en]

    In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.

  • 8.
    Dominguez, David Caceres
    et al.
    Örebro University, School of Science and Technology.
    Iannotta, Marco
    Örebro University, School of Science and Technology.
    Stork, Johannes Andreas
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    A Stack-of-Tasks Approach Combined With Behavior Trees: A New Framework for Robot Control2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 4, p. 12110-12117Article in journal (Refereed)
    Abstract [en]

    Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this approach solves a sequence of Quadratic Programs (QP) at each time-step, without taking into account any temporal state evolution, it is suitable for dealing with local disturbances. However, its limitation lies in the handling of situations that require non-quadratic objectives to achieve a specific goal, as well as situations where countering the control disturbance would require a locally suboptimal action. Recent works address this shortcoming by exploiting Finite State Machines (FSMs) to compose the tasks in such a way that the robot does not get stuck in local minima. Nevertheless, the intrinsic trade-off between reactivity and modularity that characterizes FSMs makes them impractical for defining reactive behaviors in dynamic environments. In this letter, we combine the SoT control strategy with Behavior Trees (BTs), a task switching structure that addresses some of the limitations of the FSMs in terms of reactivity, modularity and re-usability. Experimental results on a Franka Emika Panda 7-DOF manipulator show the robustness of our framework, that allows the robot to benefit from the reactivity of both SoT and BTs.

  • 9.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Arain, Muhammad Asif
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop2017In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE conference proceedings, 2017, article id 17013581Conference paper (Refereed)
    Abstract [en]

    Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.

  • 10.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments2018In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed)
    Abstract [en]

    Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

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    A Cluster Analysis Approach Based on Exploiting Density Peaks for Gas Discrimination with Electronic Noses in Open Environments
  • 11.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers2019In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE, 2019, article id 151773Conference paper (Refereed)
    Abstract [en]

    Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.

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    Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
  • 12.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 3, article id E685Article in journal (Refereed)
    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.

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    Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
  • 13.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks2016In: 2016 IEEE SENSORS, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper (Refereed)
    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. 

  • 14.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Jonsson, Daniel
    Örebro University, Örebro, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning2022In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN): Proceedings, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    Gas identification using an electronic nose (e-nose) typically relies on a multi-class classifier trained with extensive data of a limited set of target analytes. Usually, classification performance degrades in the presence of mixtures that include interferents not represented in the training data. This issue limits the applicability of e-noses in real-world scenarios where interferents are a priori unknown. This paper investigates the feasibility of tackling this particular gas identification problem using one-class learning. We propose several training strategies for a one-class support vector machine to deal with gas mixtures composed of a target analyte and an interferent at different concentration levels. Our evaluation indicates that accurate identification of the presence of a target analyte is achievable if it is dominant in a mixture. For interferent-dominant mixtures, extensive training is required, which implies that an improvement in the generalization ability of the one-class model is needed.

  • 15.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology. Mobile Robotics & Olfaction Lab.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology. Mobile Robotics & Olfaction Lab.
    Lilienthal, Achim
    Örebro University, School of Science and Technology. Mobile Robotics & Olfaction Lab.
    Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications2022In: Frontiers in Chemistry, E-ISSN 2296-2646, Vol. 10, article id 863838Article in journal (Refereed)
    Abstract [en]

    Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning-based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.

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    Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
  • 16.
    Gugliermo, Simona
    et al.
    Örebro University, School of Science and Technology. Intelligent Transport Systems, Scania CV AB, Södertälje, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Koniaris, Christos
    Intelligent Transport Systems, Scania CV AB, Södertälje, Sweden.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization2023In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3534-3541Article in journal (Refereed)
    Abstract [en]

    The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.

    The full text will be freely available from 2025-06-01 00:00
  • 17.
    Gugliermo, Simona
    et al.
    Örebro University, School of Science and Technology. Intelligent Transport Systems, Scania CV AB, Södertälje, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Koniaris, Christos
    Intelligent Transport Systems, Scania CV AB, Södertälje, Sweden.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Extracting Planning Domains from Execution Traces: a Progress Report2023Conference paper (Refereed)
    Abstract [en]

    One of the difficulties of using AI planners in industrial applications pertains to the complexity of writing planning domain models. These models are typically constructed by domain planning experts and can become increasingly difficult to codify for large applications. In this paper, we describe our ongoing research on a novel approach to automatically learn planning domains from previously executed traces using Behavior Trees as an intermediate human-readable structure. By involving human planning experts in the learning phase, our approach can benefit from their validation. This paper outlines the initial steps we have taken in this research, and presents the challenges we face in the future.

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    Extracting Planning Domains from Execution Traces: a Progress Report
  • 18.
    Gutiérrez Maestro, Eduardo
    et al.
    Örebro University, School of Science and Technology.
    Almeida, Tiago Rodrigues de
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Martinez Mozos, Oscar
    Örebro University, School of Science and Technology.
    Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities2023In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 9, article id 5637Article in journal (Refereed)
    Abstract [en]

    We present a system designed to monitor the well-being of older adults during their daily activities. To automatically detect and classify their emotional state, we collect physiological data through a wearable medical sensor. Ground truth data are obtained using a simple smartphone app that provides ecological momentary assessment (EMA), a method for repeatedly sampling people's current experiences in real time in their natural environments. We are making the resulting dataset publicly available as a benchmark for future comparisons and methods. We are evaluating two feature selection methods to improve classification performance and proposing a feature set that augments and contrasts domain expert knowledge based on time-analysis features. The results demonstrate an improvement in classification accuracy when using the proposed feature selection methods. Furthermore, the feature set we present is better suited for predicting emotional states in a leave-one-day-out experimental setup, as it identifies more patterns.

  • 19.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Ferrari, Silvia
    Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca NY, USA.
    Albertson, John
    School of Civil and Environmental Engineering, Cornell University, Ithaca NY, USA.
    Integrated Simulation of Gas Dispersion and Mobile Sensing Systems2015In: Workshop on Realistic, Rapid and Repeatable Robot Simulation, 2015Conference paper (Refereed)
    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.

  • 20.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    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 University, School of Science and Technology.
    Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 9, p. 17331-17352Article in journal (Refereed)
    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.

  • 21.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems2014In: Proceedings of the IEEE Sensors Conference 2014, IEEE conference proceedings, 2014, p. -2049Conference paper (Refereed)
    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.

  • 22.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Robot assisted gas tomography: an alternative approach for the detection of fugitive methane emissions2014In: Workshop on Robot Monitoring, 2014Conference paper (Refereed)
    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.

  • 23.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Online parameter selection for gas distribution mapping2013In: Proceedings of the ISOEN conference 2013, 2013Conference paper (Refereed)
  • 24.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Online parameter selection for gas distribution mapping2014In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6-7, p. 1147-1151Article in journal (Refereed)
    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.

  • 25.
    Iannotta, Marco
    et al.
    Örebro University, School of Science and Technology.
    Dominguez, David Caceres
    Örebro University, School of Science and Technology.
    Stork, Johannes Andreas
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees2022In: IROS 2022 Workshop on Mobile Manipulation and Embodied Intelligence (MOMA): Challenges and  Opportunities, 2022Conference paper (Refereed)
    Abstract [en]

    Integrating the heterogeneous controllers of a complex mechanical system, such as a mobile manipulator, within the same structure and in a modular way is still challenging. In this work we extend our framework based on Behavior Trees for the control of a redundant mechanical system to the problem of commanding more complex systems that involve multiple low-level controllers. This allows the integrated systems to achieve non-trivial goals that require coordination among the sub-systems.

    Download full text (pdf)
    Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees
  • 26.
    Ishida, Hiroshi
    et al.
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Matsukura, Haruka
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Using Chemical Sensors as 'Noses' for Mobile Robots2016In: Essentials of Machine Olfaction and Taste / [ed] Takamichi Nakamoto, Singapore: John Wiley & Sons, 2016, p. 219-246Chapter in book (Refereed)
    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.

  • 27.
    Kaltenhaeuser, Robert
    et al.
    Ilmenau University of Technology, Ilmenau, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Steege, Frank-Florian
    Ilmenau University of Technology, Ilmenau, Germany.
    Gross, Horst-Michael
    Ilmenau University of Technology, Ilmenau, Germany.
    Evolutionary computation based system decomposition with neural networks2013In: ESANN 2013 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligenceand Machine Learning, Louvain-La-Neuve: ESANN , 2013, p. 191-196Conference paper (Refereed)
    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, representing possible sub-problems, while the networks are used to assess the quality of the resulting decomposition. We show that this approach is well suited to calculate correct decompositions of complex control systems. Furthermore, the obtained neural networks are used to predict important process factors with considerable better approximation quality than monolithic approaches that have to deal with all input channels in parallel.

  • 28.
    Khaliq, Ali
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Bringing Artificial Olfaction and Mobile Robotics Closer Together: An Integrated 3D Gas Dispersion Simulator in ROS2015In: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses, 2015, article id 137Conference paper (Refereed)
    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.

  • 29.
    Kucner, Tomasz Piotr
    et al.
    Mobile Robotics Group, School of Electrical Engineering, Aalto University, Finland; Finnish Center for Artificial Intelligence, Finland.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Mghames, Sariah
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Palmieri, Luigi
    BOSCH Corporate Research, Renningen, Germany.
    Verdoja, Francesco
    Intelligent Robotics Group, School of Electrical Engineering, Aalto University, Finland.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Krajnik, Tomas
    Artificial Intelligence Center, Czech Technical University, Praha, Czechia.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Bellotto, Nicola
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK; Department of Information Engineering, Univeristy of Padua, Padova, Italy.
    Hanheide, Marc
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology. Technical Univeristy of Munich, Munich, Germany.
    Survey of maps of dynamics for mobile robots2023In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 42, no 11, p. 977-1006Article in journal (Refereed)
    Abstract [en]

    Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

  • 30.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor Manuel
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed)
    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.

    Download full text (pdf)
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
  • 31.
    Lilienthal, Achim J.
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    It's always smelly around here! Modeling the Spatial Distribution of Gas Detection Events with BASED Grid Maps2013In: Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013), 2013Conference paper (Refereed)
    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.

  • 32.
    Lundell, Jens
    et al.
    Intelligent Robotics Group, Aalto University, Helsinki, Finland.
    Krug, Robert
    Royal Institute of Technology, Stockholm, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Kyrki, Ville
    Intelligent Robotics Group, Aalto University, Helsinki, Finland.
    Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization2018In: IEEE-RAS Conference on Humanoid Robots / [ed] Asfour, T, IEEE, 2018, p. 132-138Conference paper (Refereed)
    Abstract [en]

    Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.

  • 33.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks2014In: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE Robotics and Automation Society, 2014, p. 5685-5690Conference paper (Refereed)
    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.

  • 34.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J
    Örebro University, School of Science and Technology.
    Support relation analysis and decision making for safe robotic manipulation tasks2015In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 71, no SI, p. 99-117Article in journal (Refereed)
    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.

  • 35.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Inferring human body posture information from reflective patterns of protective work garments2016In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4131-4136Conference paper (Refereed)
    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.

  • 36.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors2014In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6/7, p. 1123-1127Article in journal (Refereed)
    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.

  • 37.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    TREFEX: trend estimation and change detection in the response of mox gas sensors2013In: Sensors, E-ISSN 1424-8220, Vol. 13, no 6, p. 7323-7344Article in journal (Refereed)
    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.

    Download full text (pdf)
    sensors-13-07323.pdf
  • 38.
    Rietz, Finn
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Stork, Johannes Andreas
    Örebro University, School of Science and Technology.
    Towards Task-Prioritized Policy Composition2022Conference paper (Refereed)
    Abstract [en]

    Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer. In control theory, prioritized composition is realized by null-space control, where low-priority control actions are projected into the null-space of high-priority control actions. Such a method is currently unavailable for Reinforcement Learning. We propose a novel, task-prioritized composition framework for Reinforcement Learning, which involves a novel concept: The indifferent-space of Reinforcement Learning policies. Our framework has the potential to facilitate knowledge transfer and modular design while greatly increasing data efficiency and data reuse for Reinforcement Learning agents. Further, our approach can ensure high-priority constraint satisfaction, which makes it promising for learning in safety-critical domains like robotics. Unlike null-space control, our approach allows learning globally optimal policies for the compound task by online learning in the indifference-space of higher-level policies after initial compound policy construction. 

  • 39.
    Schaffernicht, Erik
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Mobile robots for learning spatio-temporal interpolation models in sensor networks - The Echo State map approach: The Echo State map approach2017In: 2017 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 2659-2665Conference paper (Refereed)
    Abstract [en]

    Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration.

    Download full text (pdf)
    Schaffernicht-ICRA2017-EchoStateMaps.pdf
  • 40.
    Schaffernicht, Erik
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events2014In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6-7, p. 1142-1146Article in journal (Refereed)
    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.

  • 41.
    Schindler, Maike
    et al.
    Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany.
    Doderer, Jan H.
    Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany.
    Simon, Anna L.
    Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schäfer, Karolin
    Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany.
    Small number enumeration processes of deaf or hard-of-hearing students: A study using eye tracking and artificial intelligence2022In: Frontiers in Psychology, E-ISSN 1664-1078, Vol. 13, article id 909775Article in journal (Refereed)
    Abstract [en]

    Students who are deaf or hard-of-hearing (DHH) often show significant difficulties in learning mathematics. Previous studies have reported that students who are DHH lag several years behind in their mathematical development compared to hearing students. As possible reasons, limited learning opportunities due to a lesser incidental exposure to numerical ideas, delays in language and speech development, and further idiosyncratic difficulties of students who are DHH are discussed; however, early mathematical skills and their role in mathematical difficulties of students who are DHH are not explored sufficiently. In this study, we investigate whether students who are DHH differ from hearing students in their ability to enumerate small sets (1-9)-an ability that is associated with mathematical difficulties and their emergence. Based on a study with N = 63 who are DHH and N = 164 hearing students from third to fifth grade attempting 36 tasks, we used eye tracking, the recording of students' eye movements, to qualitatively investigate student enumeration processes. To reduce the effort of qualitative analysis of around 8,000 student enumeration processes (227 students x 36 tasks), we used Artificial Intelligence, in particular, a clustering algorithm, to identify student enumeration processes from the heatmaps of student gaze distributions. Based on the clustering, we found that gaze distributions of students who are DHH and students with normal hearing differed significantly on a group level, indicating differences in enumeration processes, with students who are DHH using advantageous processes (e.g., enumeration "at a glance") more often than hearing students. The results indicate that students who are DHH do not lag behind in small number enumeration as compared to hearing students but, rather, appear to perform better than their hearing peers in small number enumeration processes, as well as when conceptual knowledge about the part-whole relationship is involved. Our study suggests that the mathematical difficulties of students who are DHH are not related to difficulties in the small number enumeration, which offers interesting perspectives for further research.

  • 42.
    Schindler, Maike
    et al.
    University of Cologne, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Identifying student strategies through eye tracking and unsupervised learning: The case of quantity recognition2020In: Interim Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education. Khon Kaen, Thailand: PME / [ed] Inprasitha, M., Changsri, N. & Boonsena, N., 2020, p. 518-527Conference paper (Refereed)
    Abstract [en]

    Identifying student strategies is an important endeavor in mathematics education research. Eye tracking (ET) has proven to be valuable for this purpose: E.g., analysis of ET videos allows for identification of student strategies, particularly in quantity recognition activities. Yet, “manual”, qualitative analysis of student strategies from ET videos is laborious—which calls for more efficient methods of analysis. Our methodological paper investigates opportunities and challenges of using unsupervised machine learning (USL) in combination with ET data: Based on empirical ET data of N = 164 students and heat maps of their gaze distributions on the task, we used a clustering algorithm to identify student strategies from ET data and investigate whether the clusters are consistent regarding student strategies.

    Download full text (pdf)
    Identifying student strategies through eye tracking and unsupervised learning
  • 43.
    Schindler, Maike
    et al.
    Örebro University, School of Science and Technology. University of Cologne, Cologne, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Differences in Quantity Recognition Between Students with and without Mathematical Difficulties Analyzed Through Eye: Analysis Through Eye-Tracking and AI2019In: Proceedings of the 43rd Conference of the International Group for the Psychology of Mathematics Education / [ed] M. Graven, H. Venkat, A. Essien & P. Vale, PME , 2019, Vol. 3, p. 281-288Conference paper (Refereed)
    Abstract [en]

    Difficulties in mathematics learning are an important topic in practice and research. In particular, researchers and practitioners need to identify students’ needs for support to teach and help them adequately. However, empirical research about group differences of students with and without mathematical difficulties (MD) is still scarce. Previous research suggests that students with MD may differ in their quantity recognition strategies in structured whole number representations from students without MD. This study uses eye-tracking (ET), combined with Artificial Intelligence (AI), in particular pattern recognition methods, to analyze group differences in gaze patterns in quantity recognition of N=164 fifth grade students.

    Download full text (pdf)
    Differences in Quantity Recognition Between Students with and without Mathematical Difficulties Analyzed Through Eye
  • 44.
    Vuka, Mikel
    et al.
    Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Schmuker, Michael
    School of Computer Science, College Lane, University of Hertfordshire, Hatfield, United Kingdom.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Amigoni, Francesco
    Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
    Lilienthal, Achim J
    Örebro University, School of Science and Technology.
    Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot: A Gaussian Regression Bout Amplitude Approach2017In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, p. 164-166Conference paper (Refereed)
    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.

  • 45.
    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 University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling2017In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, p. 122-124Conference paper (Refereed)
    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.

  • 46.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Kotlyar, Oleksandr
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Matsukura, Haruka
    University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan.
    Ishida, Hiroshi
    Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo, Japan.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping2022In: ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2 / [ed] Danilo Tardioli; Vicente Matellán; Guillermo Heredia; Manuel F. Silva; Lino Marques, Springer, 2022, Vol. 590, p. 178-188Conference paper (Refereed)
    Abstract [en]

    To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.

  • 47.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Matsukura, Haruka
    University of Electro-Communications, Tokyo, Japan.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Ishida, Hiroshi
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network2022In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE , 2022Conference paper (Refereed)
    Abstract [en]

    Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.

  • 48.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Kohlhoff, Harald
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Erdmann, Jessica
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring2021In: SMSI 2021 Proceedings, 2021, p. 260-261Conference paper (Refereed)
    Abstract [en]

    A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown.

    Download full text (pdf)
    Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring
  • 49.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Using Redundancy in a Sensor Network to Compensate Sensor Failures2021In: 2021 IEEE SENSORS, 2021Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.

  • 50.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using Redundancy in a Sensor Network to Compensate Sensor Failures2021In: 2021 IEEE SENSORS, IEEE , 2021Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.

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