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  • 201.
    Tincani, Vinicio
    et al.
    University of Pisa, Pisa, Italy.
    Catalano, Manuel
    University of Pisa, Pisa, Italy.
    Grioli, Giorgio
    University of Pisa, Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    University of Pisa, Pisa, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.
    Sensitive Active Surfaces on the Velvet II Dexterous Gripper2015Conference paper (Refereed)
  • 202.
    Tincani, Vinicio
    et al.
    University of Pisa, Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Catalano, Manuel
    University of Pisa, Pisa, Italy.
    Grioli, Giorgio
    University of Pisa, Pisa, Italy.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Istituto Italiano di Tecnologia, Genova, Italy.
    The Grasp Acquisition Strategy of the Velvet II2015Conference paper (Refereed)
  • 203.
    Triebel, Rudolph
    et al.
    Department of Computer Science, Technische Universität München, Munich, Germany.
    Arras, Kai
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Alami, Rachid
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Beyer, Lucas
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Breuers, Stefan
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Chatila, Raja
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Chetouani, Mohamed
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Cremers, Daniel
    Department of Computer Science, Technische Universität München, Munich, Germany.
    Evers, Vanessa
    University of Twente, Enschede, Netherlands.
    Fiore, Michelangelo
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Hung, Hayley
    Delft University of Technology, Delft, Netherlands.
    Ramirez, Omar A. Islas
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Joosse, Michiel
    University of Twente, Enschede, Netherlands.
    Khambhaita, Harmish
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Leibe, Bastian
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Linder, Timm
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Lohse, Manja
    University of Twente, Enschede, Netherlands.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Okal, Billy
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Palmieri, Luigi
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Rafi, Umer
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    van Rooij, Marieke
    University of Amsterdam, Amsterdam, Netherlands.
    Zhang, Lu
    University of Twente, Enschede, Netherlands; Delft University of Technology, Delft, Netherlands.
    SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports2016In: Field and Service Robotics: Results of the 10th International Conference / [ed] David S. Wettergreen, Timothy D. Barfoot, Springer, 2016, p. 607-622Conference paper (Refereed)
    Abstract [en]

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

  • 204.
    Trincavelli, Marco
    et al.
    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.
    A Least Squares approach for learning gas distribution maps from a set of integral gas concentration measurements obtained with a TDLAS sensor2012In: Proceedings of the IEEE Sensors Conference, 2012, IEEE Sensors Council, 2012, p. 550-553Conference paper (Refereed)
    Abstract [en]

    Applications related to industrial plant surveillance and environmental monitoring often require the creation of gas distribution maps (GDM). In this paper an approach for creating a gas distribution map using a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor and a laser range scanner mounted on a pan tilt unit is presented. The TDLAS sensor can remotely sense the target gas, in this case methane, requiring novel GDM algorithms compared to the ones developed for traditional in-situ chemical sensors. The presented setup makes it possible to create a 3D model of the environment and to calculate the path travelled by the TDLAS beam. The knowledge of the beam path is of crucial importance since a TDLAS sensor provides an integral measurement of the gas concentration over that path. An efficient GDM algorithm based on a quadratic programming formulation is proposed. The approach is tested in an indoor scenario where transparent bottles filled with methane are successfully localized.

  • 205.
    Trincavelli, Marco
    et al.
    Örebro University, Department of Technology.
    Reggente, Matteo
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Ishida, Hiroshi
    Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Towards environmental monitoring with mobile robots2008In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, New York, NY, USA: IEEE, 2008, p. 2210-2215, article id 4650755Conference paper (Refereed)
    Abstract [en]

    In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an “electronic nose” and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties:

    an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed.

  • 206.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Vergara, A.
    Rulkov, N.
    Murguia, J. S.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Huerta, R.
    Optimizing the operating temperature for an array of MOX sensors on an open sampling system2011In: Olfaction and electronic nose: Proceedings of the 14th international symposium on olfaction and electonic nose, 2011, p. 225-227Conference paper (Refereed)
    Abstract [en]

    Chemo-resistive transduction is essential for capturing the spatio-temporal structure of chemical compounds dispersed in different environments. Due to gas dispersion mechanisms, namely diffusion, turbulence and advection, the sensors in an open sampling system, i.e. directly exposed to the environment to be monitored, are exposed to low concentrations of gases with many fluctuations making, as a consequence, the identification and monitoring of the gases even more complicated and challenging than in a controlled laboratory setting. Therefore, tuning the value of the operating temperature becomes crucial for successfully identifying and monitoring the pollutant gases, particularly in applications such as exploration of hazardous areas, air pollution monitoring, and search and rescue I. In this study we demonstrate the benefit of optimizing the sensor's operating temperature when the sensors are deployed in an open sampling system, i.e. directly exposed to the environment to be monitored.

  • 207.
    Valencia, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Vallvé, Joan
    CSIC-UPC, Barcelona,Spain.
    Andrade-Cetto, Juan
    CSIC-UPC, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Localization in highly dynamic environments using dual-timescale NDT-MCL2014In: 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE Robotics and Automation Society, 2014, p. 3956-3962Conference paper (Refereed)
    Abstract [en]

    Industrial environments are rarely static and oftentheir configuration is continuously changing due to the materialtransfer flow. This is a major challenge for infrastructure freelocalization systems. In this paper we address this challengeby introducing a localization approach that uses a dualtimescaleapproach. The proposed approach - Dual-TimescaleNormal Distributions Transform Monte Carlo Localization (DTNDT-MCL) - is a particle filter based localization method,which simultaneously keeps track of the pose using an aprioriknown static map and a short-term map. The short-termmap is continuously updated and uses Normal DistributionsTransform Occupancy maps to maintain the current state ofthe environment. A key novelty of this approach is that it doesnot have to select an entire timescale map but rather use thebest timescale locally. The approach has real-time performanceand is evaluated using three datasets with increasing levels ofdynamics. We compare our approach against previously proposedNDT-MCL and commonly used SLAM algorithms andshow that DT-NDT-MCL outperforms competing algorithmswith regards to accuracy in all three test cases.

  • 208.
    Valgren, Christoffer
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln, United Kingdom.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Incremental spectral clustering and its application to topological mapping2007In: 2007 IEEE international conference on robotics and automation (ICRA), New York, NY, USA: IEEE, 2007, p. 4283-4288Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm.

  • 209.
    Valgren, Christoffer
    et al.
    Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Incremental spectral clustering and seasons: appearance-based localization in outdoor environments2008In: 2008 IEEE international conference on robotics and automation, New York, NY, USA: IEEE, 2008, p. 1856-1861, article id 4543477Conference paper (Refereed)
    Abstract [en]

    The problem of appearance-based mapping and navigation in outdoor environments is far from trivial. In this paper, an appearance-based topological map, covering a large, mixed indoor and outdoor environment, is built incrementally by using panoramic images. The map is based on image similarity, so that the resulting segmentation of the world corresponds closely to the human concept of a place. Using high-resolution images and the epipolar constraint, the resulting map is shown to be very suitable for localization, even when the environment has undergone seasonal changes.

  • 210.
    Valgren, Christoffer
    et al.
    Department of Computer Science, Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments2010In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 58, no 2, p. 149-156Article in journal (Refereed)
    Abstract [en]

    In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straight-forward method that relies on local image features to compare single image pairs. We rst look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then ne-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The nal localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The nal localization rate in the single-image matching, cross-seasonal case is between 80 to 95%.

  • 211.
    Valgren, Christoffer
    et al.
    Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    SIFT, SURF and seasons: long-term outdoor localization using local features2007In: ECMR 2007: Proceedings of the European Conference on Mobile Robots, 2007, p. 253-258Conference paper (Refereed)
    Abstract [en]

    Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. In this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. Our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). Two different types of image feature algorithms, SIFT and the more recent SURF, have been used to compare the images. We show that two variants of SURF, called U-SURF and SURF-128, outperform the other algorithms in terms of accuracy and speed.

  • 212.
    Valgren, Christoffer
    et al.
    Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    Department of Computing and Informatics, University of Lincoln, Brayford Pool, Lincoln, United Kingdom.
    Incremental topological mapping using omnidirectional vision2006In: 2006 IEEE/RSJ international conference on intelligent robots and systems, New York, NY, USA: IEEE, 2006, p. 3441-3447, article id 4058933Conference paper (Refereed)
    Abstract [en]

    This paper presents an algorithm that builds topological maps, using omnidirectional vision as the only sensor modality. Local features are extracted from images obtained in sequence, and are used both to cluster the images into nodes and to detect links between the nodes. The algorithm is incremental, reducing the computational requirements of the corresponding batch algorithm. Experimental results in a complex, indoor environment show that the algorithm produces topologically correct maps, closing loops without suffering from perceptual aliasing or false links. Robustness to lighting variations was further demonstrated by building correct maps from combined multiple datasets collected over a period of 2 months.

  • 213.
    Vaskevicius, N.
    et al.
    Jacobs University, Bremen, Germany.
    Mueller, C. A.
    Jacobs University, Bremen, Germany.
    Bonilla, M.
    University of Pisa, Pisa, Italy.
    Tincani, V.
    University of Pisa, Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Fantoni, G.
    University of Pisa, Pisa, Italy.
    Pathak, K.
    Jacobs University, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bicchi, A.
    University of Pisa, Pisa, Italy.
    Birk, A.
    Jacobs University, Bremen, Germany.
    Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading2014Conference paper (Refereed)
    Abstract [en]

    The work presented here is embedded in research on an industrial application scenario, namely autonomous shipping-container unloading, which has several challenging constraints: the scene is very cluttered, objects can be much larger than in common table-top scenarios; the perception must be highly robust, while being as fast as possible. These contradicting goals force a compromise between speed and accuracy. In this work, we investigate a state of the art perception system integrated with a dexterous gripper. In particular, we are interested in pose estimation errors from the recognition module and whether these errors can be handled by the abilities of the gripper.

  • 214.
    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.

  • 215.
    Wandel, Michael
    et al.
    University of Tübingen, Tübingen, Germany.
    Lilienthal, Achim J.
    University of Tübingen, Tübingen, Germany.
    Duckett, Tom
    Örebro University, Department of Technology.
    Weimar, Udo
    University of Tübingen, Tübingen, Germany.
    Zell, Andreas
    University of Tübingen, Tübingen, Germany.
    Gas distribution in unventilated indoor environments inspected by a mobile robot2003In: Proceedings of the IEEE international conference on advanced robotics 2003, Coimbra, Portugal: University of Coimbra , 2003, Vol. 1-3, p. 507-512Conference paper (Refereed)
    Abstract [en]

    Gas source localisation with robots is usually performed in environments with a strong, unidirectional airflow created by artificial ventilation. This tends to create a strong, well defined analyte plume and enables upwind searching. By contrast, this paper presents experiments conducted in unventilated rooms. Here, the measured concentrations also indicate an analyte plume with, however, different properties concerning its shape, width, concentration profile and stability over time. In the results presented in this paper, two very different mobile robotic systems for odour sensing were investigated in different environments, and the similarities as well as differences in the analyte gas distributions measured are discussed.

  • 216.
    Wandel, Michael
    et al.
    University of Tübingen, Tübingen, Germany.
    Lilienthal, Achim J.
    University of Tübingen, Tübingen, Germany.
    Zell, Andreas
    University of Tübingen, Tübingen, Germany.
    Weimar, Udo
    University of Tübingen, Tübingen, Germany.
    Mobile robot using different senses2002In: Proceedings of the international symposium on olfaction and electronic nose: ISOEN 2002, 2002, p. 128-129Conference paper (Refereed)
  • 217.
    Wandel, Michael R.
    et al.
    University of Tübingen, Tübingen, Germany.
    Weimar, Udo
    University of Tübingen, Tübingen, Germany.
    Lilienthal, Achim J.
    University of Tübingen, Tübingen, Germany.
    Zell, Andreas
    University of Tübingen, Tübingen, Germany.
    Leakage localisation with a mobile robot carrying chemical sensors2001In: The 8th IEEE international conference on electronics, circuits and systems: ICECS 2001, Malta, Malta: IEEE, 2001, Vol. 3, p. 1247-1250, article id 957441Conference paper (Refereed)
    Abstract [en]

    On the way to developing an electronic watchman one more sense, i.e. gas sensing facilities, are added to an autonomous mobile robot. For the gas detection, up to eight metal oxide sensors are operated using a commercial sensor system. The robot is able to move and navigate autonomously. The geometric information is extracted from laser range finder data. This input is used to build up an internal map while driving. Using the new sensor the localisation of a gas source in unventilated in-house environments is performed. First experiments in a one-dimensional case show a very good correlation between the peak and the gas source. The one-dimensional concentration profile is repeatedly recorded and stable for at least two hours. The two-dimensional experiments exhibit a circulation of the air within the room due to temperature and hence density effects. The latter is limiting the available recording time for the two-dimensional mapping

  • 218.
    Wiedemann, Thomas
    et al.
    German Aerospace Center, Oberpfaffenhofen, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Shutin, Dmitriy
    German Aerospace Center, Oberpfaffenhofen, Germany.
    Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id 520Article in journal (Refereed)
    Abstract [en]

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

  • 219.
    Wiedemann, Thomas
    et al.
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany.
    Manss, Christoph
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany.
    Shutin, Dmitriy
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Karolj, Valentina
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany.
    Viseras, Alberto
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany.
    Probabilistic modeling of gas diffusion with partial differential equations for multi-robot exploration and gas source localization2017In: 2017 European Conference on Mobile Robots (ECMR), Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 8098707Conference paper (Refereed)
    Abstract [en]

    Employing automated robots for sampling gas distributions and for localizing gas sources is beneficial since it avoids hazards for a human operator. This paper addresses the problem of exploring a gas diffusion process using a multi-agent system consisting of several mobile sensing robots. The diffusion process is modeled using a partial differential equation (PDE). It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The goal of the multi-robot exploration is thus to identify source parameters, in particular, their number, locations and magnitudes. Therefore, this paper develops a probabilistic approach towards PDE identification under sparsity constraint using factor graphs and a message passing algorithm. Moreover, the message passing schemes permits efficient distributed implementation of the algorithm. This brings significant advantages with respect to scalability, computational complexity and robustness of the proposed exploration algorithm. Based on the derived probabilistic model, an exploration strategy to guide the mobile agents in real time to more informative sampling locations is proposed. Hardware- in-the-loop experiments with real mobile robots show that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling.

  • 220.
    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, 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.

  • 221.
    Xing, Yuxin
    et al.
    School of Engineering, University of Warwick, Coventry, UK.
    Vincent, Timothy A.
    School of Engineering, University of Warwick, Coventry, UK.
    Cole, Marina
    School of Engineering, University of Warwick, Coventry, UK.
    Gardner, Julian W.
    School of Engineering, University of Warwick, Coventry, UK.
    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
    Örebro University, School of Science and Technology.
    Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments2017In: IEEE SENSORS 2017: Conference Proceedings, New York: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1691-1693Conference paper (Refereed)
    Abstract [en]

    In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%).

  • 222.
    Åstrand, Björn
    et al.
    Halmstad University.
    Rögnvaldsson, Thorsteinn
    Halmstad University.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    An Autonomous Robotic System for Load Transportation2009In: Proceedings of the 4th Swedish Workshop on Autonomous Robotics (SWAR), 2009, p. 56-57Conference paper (Refereed)
2345 201 - 222 of 222
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