RF-compass: Robot object manipulation using RFIDs

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches). This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compass's key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robot's consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.National Science Foundation (U.S.

RFID Gazebo-Based Simulator With RSSI and Phase Signals for UHF Tags Localization and Tracking

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

ReLoc: Hybrid RSSI- and phase-based relative UHF-RFID tag localization with COTS devices

Radio frequency identification (RFID) technology brings tremendous advancements in the Industrial Internet of Things (IIoT), especially for smart inventory management, as it provides a fast and low-cost way of counting or positioning items in the warehouse. In the last decade, many novel solutions, including absolute and relative positioning methods, have been proposed for this application. However, the available methods are quite sensitive to the minor changes in the deployment scenario, including the orientation of the tag and antenna, the materials contained inside the carton, tag distortion, and multipath propagation. To this end, we propose a hybrid relative passive RFID localization method (ReLoc) based on both the received signal strength indicator (RSSI) and measured phases, which orders the RFID tags horizontally and vertically. In this article, the phase-based variant maximum likelihood estimation is proposed for lateral positioning, and the RSSI profiles of two tilted antennas are compared with each other for level distinguishing. We implement the proposed positioning system ReLoc with commercial off-the-shelf RFID devices. The experiment in a warehouse shows that ReLoc is a powerful solution for practical item-level inventory management. The experimental results show that ReLoc achieves an average lateral and level ordering accuracy of 94.6% and 94.3%, respectively. Notably, when considering liquid or metal materials inside the carton or tag distortion, ReLoc still performs excellently with more than 93% ordering accuracy both horizontally and vertically, indicating the robustness of the proposed method

RePos : relative position estimation of UHF-RFID tags for item-level localization

Radio frequency identification (RFID) technology brings tremendous applications in location-based services. Specifically, ultra-high frequency (UHF) RFID tag positioning based on phase (difference) of arrival (PoA/PDoA) has won great attention, due to its better positioning accuracy than signal strength-based methods. In most cases, such as logistics, retailing, and smart inventory management, the relative orders of the objects are much more attractive than absolute positions with centimetre-level accuracy. In this paper, a relative positioning (RePos) approach based on inter-tag distance and direction estimation is proposed. In the RePos positioning system, the measured phases are reconstructed based on unwrapping method. Then the distances from antenna to the tags are calculated using the distance differences of pairs of antenna's positions via a least-squares method. The relative relationships of the tags, including relative distances and angles, are obtained based on the geometry information extracted from PDoA. The experimental results show that the RePos RFID positioning system can realize about 0.28-meter ranging accuracy, and distinguish the levels and columns without ambiguity

The Design and Implementation of a Mobile RFID Tag Sorting Robot

Libraries, manufacturing lines, and offices of the future all stand to benefit from knowing the exact spatial order of RFID-tagged books, components, and folders, respectively. To this end, radio- based localization has demonstrated the potential for high accuracy. Key enabling ideas include motion-based synthetic aperture radar, multipath detection, and the use of different frequencies (channels). But indoors in real-world situations, current systems often fall short of the mark, mainly because of the prevalence and strength of "multipath" reflections of the radio signal off nearby objects. In this paper we describe the design and implementation of MobiTagbot, an autonomous wheeled robot reader that conducts a roving survey of the above such areas to achieve an exact spatial order of RFID- tagged objects in very close (1–6 cm) spacings. Our approach leverages a serendipitous correlation between the changes in multipath reflections that occur with motion and the effect of changing the carrier frequency (channel) of the RFID query. By carefully observing the relationship between channel and phase, MobiTagbot detects if multipath is likely prevalent at a given robot reader location. If so, MobiTagbot excludes phase readings from that reader lo- cation, and generates a final location estimate using phase readings from other locations as the robot reader moves in space. Experimentally, we demonstrate that cutting-edge localization algorithms including Tagoram are not accurate enough to exactly order items in very close proximity, but MobiTagbot is, achieving nearly 100% ordering accuracy for items at low (3–6 cm) spacings and 86% accuracy for items at very low (1–3 cm) spacings

Current and Future Trends of RFID Systems: Guest Editorial of the Special Issue on SpliTech 2021 and IEEE RFID-TA 2021 Conferences

This year, the IEEE Journal of Radio Frequency Identification (JRFID), decided to host a joint Special Issue collecting extended versions of papers coming from two international events. The former is the IEEE International Conference on RFID Technology and Applications (RFID-TA) 2021, virtually held in Delhi, India, on October 6-8, 2021. The latter is the International Symposium on Advances in RFID Technology organized within the International Conference on Smart and Sustainable Technologies (SpliTech), hosted in Split and Bol, Croatia, on September 8-11, 2021. SpliTech was technically co-sponsored by the IEEE and technically media sponsored by the IEEE Council on RFID (CRFID)

A Handheld Fine-Grained RFID Localization System with Complex-Controlled Polarization

There is much interest in fine-grained RFID localization systems. Existing systems for accurate localization typically require infrastructure, either in the form of extensive reference tags or many antennas (e.g., antenna arrays) to localize RFID tags within their radio range. Yet, there remains a need for fine-grained RFID localization solutions that are in a compact, portable, mobile form, that can be held by users as they walk around areas to map them, such as in retail stores, warehouses, or manufacturing plants. We present the design, implementation, and evaluation of POLAR, a portable handheld system for fine-grained RFID localization. Our design introduces two key innovations that enable robust, accurate, and real-time localization of RFID tags. The first is complex-controlled polarization (CCP), a mechanism for localizing RFIDs at all orientations through software-controlled polarization of two linearly polarized antennas. The second is joint tag discovery and localization (JTDL), a method for simultaneously localizing and reading tags with zero-overhead regardless of tag orientation. Building on these two techniques, we develop an end-to-end handheld system that addresses a number of practical challenges in self-interference, efficient inventorying, and self-localization. Our evaluation demonstrates that POLAR achieves a median accuracy of a few centimeters in each of the x/y/z dimensions in practical indoor environments

Next-Best-Sense: a multi-criteria robotic exploration strategy for RFID tags discovery

Automated exploration is one of the most relevant applications of autonomous robots. In this paper, we suggest a novel online coverage algorithm called Next-Best-Sense (NBS), an extension of the Next-Best-View class of exploration algorithms that optimizes the exploration task balancing multiple criteria. This novel algorithm is applied to the problem of localizing all Radio Frequency Identification (RFID) tags with a mobile robotic platform that is equipped with a RFID reader. We cast this problem as a coverage planning problem by defining a basic sensing operation -- a scan with the RFID reader -- as the field of “view” of the sensor. NBS evaluates candidate locations with a global utility function which combines utility values for travel distance, information gain, sensing time, battery status and RFID information gain, generalizing the use of Multi-Criteria Decision Making. We developed an RFID reader and tag model in the Gazebo simulator for validation. Experiments performed both in simulation and with a real robot suggest that our NBS approach can successfully localize all the RFID tags while minimizing navigation metrics such sensing operations, total traveling distance and battery consumption. The code developed is publicly available on the authors' repository

Phase-based variant maximum likelihood positioning for passive UHF-RFID tags

Radio frequency identification (MD) technology brings tremendous advancement in Internet-of-Things, especially in supply chain and smart inventory management. Phase-based passive ultra high frequency RFID tag localization has attracted great interest, due to its insensitivity to the propagation environment and tagged object properties compared with the signal strength based method. In this paper, a phase-based maximum-likelihood tag positioning estimation is proposed. To mitigate the phase uncertainty, the likelihood function is reconstructed through trigonometric transformation. Weights are constructed to reduce the impact of unexpected interference and to augment the positioning performance. The experiment results show that the proposed algorithms realize line-grained tag localization, which achieve centimeter-level lateral accuracy, and less than 15-centimeters vertical accuracy along the altitude of the racks