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  • 201.
    Wiedemann, Thomas
    et al.
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Manss, Christoph
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Shutin, Dmitriy
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Karolj, Valentina
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Viseras, Alberto
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, 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.

  • 202.
    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.

  • 203.
    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%).

  • 204.
    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 - 204 of 204
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