Reasoning about Uncertainty in Location Identification with RFID 

James Brusey*, Christian Floerkemeier+, Mark Harrison* and Martyn Fletcher* 

*Institute for Manufacturing Cambridge University Cambridge, UK 


+Institute for Pervasive Computing Department of Computer Science ETH Zurich, 
Switzerland 

Radio Frequency Identification (RFID) is set to revolutionise industrial 
control as it holds the potential to simplify and make more robust the 
tracking of parts or part carriers through manufacture, storage, distribution 
and ultimately the supply chain. RFID control is based on unique RFID 
transponder tags being attached to parts and used to identify the part as it 
moves through the factory or warehouse. Although RFID dramatically simplifies 
the process of tracking parts, there are certain situations that can lead to 
uncertainty about the true location of the part. This paper looks at two such 
situations: a robotic storage stack and a medicine cabinet. Both cases of 
uncertainty are successfully resolved by using a statistical filter. This 
work may lend itself to extensions and generalisations using Partially 
Observable Markov Decision Process (POMDP) models.