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Descripción
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| A Multi-robot network used for search and rescue or exploration tasks need wireless connectivity as its heartbeat. Spatio-temporal prediction of the radio signal strength (RSS) in a wireless mobile robot network is essential in communication autonomy computations, wireless positioning (localization), and in providing communication-aware intelligence features. In this paper, we describe a solution to quickly predict the future spatiotemporal RSS values in an unknown complex environment such as underground tunnels where the fading effects are significant. We mitigate the effects of multipath fading using a simple moving average filter. The large scale path loss fading is fitting empirically using a Linear Least Squares (LLS) regression and the medium-scale shadow fading is modelled using an empirical variogram fitted with an exponential kernel function. Both these models are integrated into a Discrete Kalman filter (DKF) for on-line estimation and prediction. Localized RSS input samples are considered for fast prediction process as the environment is assumed to be unknown as well as the method does not rely on huge amount of samples. The proposed method is fast and efficient in both Line of Sight (LoS) and non-Line of Sight (nLoS) conditions. We test our algorithm with RSS measurements from a youBot mobile robot in both indoor and underground scientific facilities at CERN. In both cases we are able to obtain more than to 90% mean prediction accuracy in predicting the future samples of up to 20m. | |
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Internacional
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Nombre congreso
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Workshop on "Communication-aware Robotics: New Tools for Multi-Robot Networks, Autonomous Vehicles, and Localization (CarNet)" at Robotics: Science and Systems 2014 |
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Tipo de participación
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960 |
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Lugar del congreso
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Berkeley, USA |
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Revisores
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ISBN o ISSN
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978-0-9923747-0-9 |
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DOI
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Fecha inicio congreso
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12/07/2014 |
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Fecha fin congreso
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16/07/2014 |
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Desde la página
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1 |
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Hasta la página
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2 |
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Título de las actas
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Workshop on "Communication-aware Robotics: New Tools for Multi-Robot Networks, Autonomous Vehicles, and Localization (CarNet)" |