Dynamic objects tracking with a mobile robot using passive UHF RFID tags

Recent research deals more and more with the application of ultra high frequency (UHF) radio-frequency identification (RFID) on mobile robots. However, the sensing characteristics between the reader and the tag (i.e. detections and signal strength) are challenging to model due to the influence of environmental effects (e.g. tag density, reflection, diffraction, or absorption). In this paper, we address the problem of dynamic objects tracking with a mobile agent using the signal strength from UHF RFID tags attached to objects. Our solution estimates the positions of RFID tags under a Bayesian framework. More precisely, we combine a two stage dynamic motion model with the dual particle filter, to capture the dynamic motion of the object and to quickly recover from failures in tracking. This approach is then tested on a Scitos G5 mobile robot through various experiments.

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