Simulation evaluation of tag movement direction estimation methods in RFID gate systems

Buffi

Nepa

et al. 2021

Sensors

The I-READ 4.0 project is aimed at developing an integrated and autonomous Cyber-Physical System for automatic management of very large warehouses with a high-stock rotation index. Thanks to a network of Radio Frequency Identification (RFID) readers operating in the Ultra-High-Frequency (UHF) band, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in an indoor scenario. A key component to accomplish this goal is the UHF-RFID Smart Gate, which consists of a checkpoint infrastructure based on RFID technology to identify forklifts and their direction of transit. This paper presents the implementation of a UHF-RFID Smart Gate with a single reader antenna with asymmetrical deployment, thus allowing the correct action classification with reduced infrastructure complexity and cost. The action classification method exploits the signal phase backscattered by RFID tags placed on the forklifts. The performance and the method capabilities are demonstrated through an on-site demonstrator in a real warehouse.

Section: Rfid Gatesmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios

Buffi

Nepa

et al. 2021

Sensors

“…Although effective, those solutions require extra space, cost and complexity, which in case of using cameras may also raise privacy concerns. Another solution employed the signature captured from different reader antennas to estimate the movement direction of tagged goods [10]. Other authors proposed the use of low-level parameters extracted from the reader measurements such as the Received Signal Strength Indicator (RSSI) or the timestamps indicating when the data were acquired [11].…”

Section: Introductionmentioning

confidence: 99%

A UHF-RFID gate control system based on a Convolutional Neural Network

Álvarez-Narciandi

2019 IEEE International Conference on RFID Technology and Applications (RFID-TA)

Pino

et al. 2019

In this paper, a robust, easy-to-deploy UHF-RFID system to classify transpallet actions at gates or checkpoints has been presented. The system is based on deploying a set of reference RFID tags on the floor of the checkpoint arranged in a matrix form. In addition, a single RFID reader antenna, which is over the checkpoint, is used to query both the reference tags of the system and those that are used identify goods and transpallets. When a transpallet crosses the controlled gate or checkpoint, it directly blocks the reference tags under it. Hence, reference RFID tags under the transpallet are progressively shadowed. As a consequence, if the number of readings of each reference tag is observed versus time, the movement direction of the transpallet can be inferred. This information was used to build images which are then fed to a Convolutional Neural Network (CNN) that classifies transpallet movements in incoming, outgoing or passing through the controlled checkpoint. A total of 159 measurements were acquired for different transpallet trajectories using 24 reference tags and a CNN was trained showing promising results.

“…Moreover, the use of cameras may raise privacy issues. Other solutions employ more than one antenna to estimate the motion direction of the goods by comparing the signature of tagged items measured from each antenna [6] or creating different interrogation zones [7]. Keller et al [8] suggest to use various aggregated features based on the lowlevel reader data (Electronic Product Code, Received Signal Strength Indicator -RSSI, timestamp, reading antenna) to discriminate moving tags in forklift truck applications, with multiple antennas.…”

Section: Introductionmentioning

confidence: 99%

A UHF-RFID Gate Control System Based on a Recurrent Neural Network

Álvarez-Narciandi

Antennas Wirel. Propag. Lett.

Pino

et al. 2019

This paper presents a novel, cost-effective and easyto-deploy solution to discriminate the direction of goods crossing a UHF-RFID gate in warehouse scenario. The system is based on a grid of UHF-RFID tags deployed on the floor underneath the gate equipped with a single reader antenna. When a transpallet crosses the gate, it shadows the tags of the deployed grid differently, according to the specific direction, namely incoming or outgoing. Such distinguishable signature is employed as input of a recurrent neural network. In particular, the number of readings for each tag is aggregated within short time-windows and a sequence of binary read/missed tag data over the time is extracted. Such temporal sequences are used to train a Long Short-Term Memory neural network. Classification performance of the proposed method is shown through a set of measurements in indoor scenario.