Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
2D prediction of radio signal strength for mobile robots in unknown environments
Año:2014
Áreas de investigación
  • Sistema de comunicación,
  • Transmisión de datos,
  • Robots móviles,
  • Vehículos inteligentes,
  • Telerobots
Datos
Descripción
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.
Internacional
Si
JCR del ISI
No
Título de la revista
Submitted to IEEE Sensors Journal
ISSN
1530-437X
Factor de impacto JCR
1,475
Información de impacto
Datos JCR del año 2012
Volumen
DOI
Número de revista
Desde la página
1
Hasta la página
2
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Ramviyas Nattanmai Parasuraman (UPM)
  • Autor: Luca Molinari (CERN)
  • Autor: Alessandro Masi (CERN)
  • Autor: Manuel Ferre Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
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