Reasoning about uncertainty in location identification with auto-ID

Abstract

Automatic Identification (Auto-ID) 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. Auto-ID control is based on unique radio frequency identification (RFID) transponder tags being attached to parts and used to identify the part as it moves through the factory or warehouse. Although Auto-ID 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.