A Mechanised 3D Scanning Method for Item-level Radio Frequency Identification of Palletised Products

The automatic, non-line-of-sight characteristics of radio frequency identification (RFID) technology for identifying multiple objects are conducive to full visibility and traceability of individual product items in a supply chain. However, practical implementation of item-level RFID-based applications necessitates solving some critical issues. Among these issues, reading tag data to identify a relatively large number of individual product items, which are usually packed in batches and distributed on pallets, is particularly a bottleneck, because it affects the accuracy and trustworthiness of batch distribution of products and all subsequent logistics operations in the supply chain. Current techniques for batch identification at item-level of palletised products suffer low reading rate and incomplete tag data acquisition, rendering the RFID systems unreliable. We address this issue by proposing a mechanised 3D scanning method for identification of tagged products in large numbers to facilitate supply chain management. The proposed method requires installing RFID readers only in the X-Y plane. The readers scan tagged products in the X and Y directions while the pallet is simultaneously rotated around to be effectively scanned in the Z direction. Different scanning patterns are adopted to alleviate the problems due to randomness of tag orientation and reader collisions. As such, 3D scanning of RFID tags for item-level applications is effectively achieved without incurring much hardware cost. The performance of the proposed method is validated using an RFID-enabled gate-door for identification of palletised apparel products with item-level RFID tagging. Experiment results show that the proposed method can achieve batch reading rates remarkably higher than those reported in literature. Moreover, a correlation between the batch reading rate and the batch density is established. Apparently, the proposed mechanised 3D scanning method for batch identification of item-level tagged product items can substantially enhance the accuracy and reliability of RFID-based supply chain management systems