The use of radio frequency identification (RFID) technology for the traceability of products throughout the production chain, warehouse management, and the retail network is spreading in the last years, especially in those industries in line with the concept of Industry 4.0. The last decade has seen the development of increasingly precise and high-performance methods for the localization of goods. This work proposes a reliable 2-D localization methodology that is faster and provides a competitive accuracy, concerning the state-of-the-art techniques. The proposed method leverages a phase-distance model and exploits the synthetic aperture approach and unwrapping techniques for facing phase ambiguity and multipath phenomena. Trilateration applied on consecutive phase readings allows finding hyperbolae as the localization solution space. Analytic calculus is used to compute intersections among the conics that estimate the tag position. An algorithm computes intersections quality to select the best estimation. Experimental tests are conducted to assess the quality of the proposed strategy. A mobile robot equipped with a reader antenna localizes in 2-D the tags placed in an indoor scenario and reconstructs the map of the environment through a simultaneous localization and mapping (SLAM) algorithm. Note to Practitioners —A localization technology based on passive ultrahigh-frequency (UHF) radio frequency identification (RFID) is an enabling technology for intelligent warehouses, logistics, and retails. For this reason, this work presents a novel method to estimate the tag location with high accuracy. A reader antenna is mounted on an autonomous mobile robot that can move in an indoor or outdoor environment due to a simultaneous localization and mapping (SLAM) algorithm. The motion of the antenna generates a synthetic aperture. The system receives the phase measurements from the RFID tags and generates a distance model through phase unwrapping. In such a way, the possible locations of the tags in the environment are generated, creating conics. The trilateration step is performed analytically, intersecting the obtained conics. The resulting estimations are very accurate and not computation expensive. Therefore, the proposed approach can be employed in any application where localizing objects is fundamental even when reduced computational power is available, e.g., in warehouses where the products are at known heights, or where the items are placed on a fixed infrastructure, such as high-shelves logistics, to produce the inventory of the tagged objects within each shelf.

This paper presents a particle swarm optimization (PSO) in synthetic aperture radar (SAR) methods to enable real-time 3D localization of UHF-RFID tags. The reader antennas are moved through a moving agent (e.g., unmanned aerial vehicle, unmanned ground vehicle, robot) and they collect several phase samples by resembling a synthetic array. Thanks to the PSO approach the 3D matching function of the SAR method can be calculated in a reduced number of points by keeping an acceptable localization error. A numerical analysis demonstrates the method applicability through a comparison with conventional SAR methods based on the exhaustive search (3D dense grid) of the maximum point. Localization performance is investigated when an agent is equipped with a single antenna moving along a 3D trajectory or with two reader antennas at different height running a planar trajectory. Then, an experimental campaign in indoor scenario with an RFID-equipped unmanned ground vehicle shows the method effectiveness in performing real-time 3D positioning with centimeter-order localization error.

The Fourth Industrial Revolution, or Industry 4.0, aims at automating traditional manufacturing and industrial practices using modern smart technology. Autonomous mobile robots equipped with different sensors and employing different techniques have been proposed in the literature in the logistics and inventory warehouse management contexts. Efficient robot motion and planning depend on precise localization, mapping, and awareness of the environment. To properly localize items, recent attempts have been made employing radio frequency identification (RFID) in a 3D environment. This manuscript introduces four least mean squares methods to estimate the 3D positions of tags employing synthetic apertures and phase unwrapping. The proposed methods approach the localization problem solving a system of equations typical of multilateration methods to find the intersections of multiple hyperboloids. The novelty introduced here is the use of unwrapped phase distances to compute pseudo ranges for the multilateration problem. The use of such a technique is feasible thanks to precise mobile robot localization and custom navigation policies. All the analyzed methods are suitable for online localization due to reduced computation timings and have been tested on three different datasets. Two of them contain virtual data that has been generated by simulation, while the other one comes from an indoor experimental setup. Simulated tests show that combined antenna motions in 3D space (including Z-axis) improve the localization obtaining errors under the centimeter for 3D localization. Experimental tests obtained results as low as 13 cm of mean accuracy for 3D localization and 0.21 cm for 2D localization.

Radio Frequency Identification (RFID) technology is becoming very popular in the new era of Industry 4.0, especially for warehouse management, retails, and logistics. RFID systems can be used for objects identification, localization, and tracking, facilitating everyday operators’ efforts. However, the deployment of RFID tags and reader antennas in real-world application scenarios is crucial and takes time. Indeed, deciding where to place tags and/or readers’ requires examining many conditions. If some weaknesses appear in the design, the arrangement must be reconsidered. The proposed work presents a novel open-source RFID simulator that allows modeling environments and testing the deployment of RFID tags and antennas apriori. In such a way, validating the performance of the localization or tracking algorithms in simulation, possible weaknesses that could arise may be fixed before facilities are applied on the field. Any number of tags and antennas can be placed in any position in the created scenario, and the simulator provides the phase and the RSSI signals for each tag. Every reader antenna is parametrized so that different antennas of different vendors can be reproduced. The simulator is implemented as a plugin of Gazebo, a widely used robotic framework integrated with the Robot Operating System (ROS), to reach a broad audience. In order to validate the simulator, a warehouse scenario is modeled, and a tag localization algorithm that uses the phase unwrapping technique and hyperbolae intersection method employing a reader antenna mounted on a mobile robot is used to estimate the position of the tags deployed in the scenario. The outcomes of the experiments showed realistic results.

This paper presents the development and testing of a robot designed for automated inventory and 3D localization of items in high-density shelving systems. To enhance accuracy in locating UHF-RFID tagged objects by using synthetic aperture-based methods, rotating antennas are installed on the robot. This allows more degrees of freedom in antenna trajectory, by reaching large synthetic apertures that may deliver high-performance localization. The robot can navigate complex environments by utilizing a depth camera and a visual odometry system, and it can effectively avoid obstacles with the help of a Laser Imaging Detection And Ranging system. Extensive testing is conducted in a realistic shop-like scenario where tagged products are placed on either wooden shelves or hung on metal racks. The robot navigation capabilities are verified together with its inventory and localization performance for different visibility conditions of the tagged items.

Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment.