Personal robots with autonomy, mobility, and manipulation capabilities have the potential to dramatically improve quality of life for various user populations, such as older adults and individuals with motor impairments. Unfortunately, unstructured environments present many challenges that hinder robot deployment in ordinary homes. This thesis seeks to address some of these challenges through a new robotic sensing modality that leverages a small amount of environmental augmentation in the form of Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) tags. Previous research has demonstrated the utility of infrastructure tags (affixed to walls) for robot localization; in this thesis, we specifically focus on tagging objects. Owing to their low-cost and passive (battery-free) operation, users can apply UHF RFID tags to hundreds of objects throughout their homes. The tags provide two valuable properties for robots: a unique identifier and receive signal strength indicator (RSSI, the strength of a tag's response). This thesis explores robot behaviors and radio frequency perception techniques using robot-mounted UHF RFID readers that enable a robot to efficiently discover, locate, and interact with UHF RFID tags applied to objects and people of interest. The behaviors and algorithms explicitly rely on the robot's mobility and manipulation capabilities to provide multiple opportunistic views of the complex electromagnetic landscape inside a home environment. The electromagnetic properties of RFID tags change when applied to common household objects. Objects can have varied material properties, can be placed in diverse orientations, and be relocated to completely new environments. We present a new class of optimization-based techniques for RFID sensing that are robust to the variation in tag performance caused by these complexities. We discuss a hybrid global-local search algorithm where a robot employing long-range directional antennas searches for tagged objects by maximizing expected RSSI measurements; that is, the robot attempts to position itself (1) near a desired tagged object and (2) oriented towards it. The robot first performs a sparse, global RFID search to locate a pose in the neighborhood of the tagged object, followed by a series of local search behaviors (bearing estimation and RFID servoing) to refine the robot's state within the local basin of attraction. We report on RFID search experiments performed in Georgia Tech's Aware Home (a real home). Our optimization-based approach yields superior performance compared to state of the art tag localization algorithms, does not require RF sensor models, is easy to implement, and generalizes to other short-range RFID sensor systems embedded in a robot's end effector. We demonstrate proof of concept applications, such as medication delivery and multi-sensor fusion, using these techniques. Through our experimental results, we show that UHF RFID is a complementary sensing modality that can assist robots in unstructured human environments.

現在越來越多的廠商和美國國防部都需要較好的無限射頻辨識系統(RFID)系統，所以它越來越受到注目，本論文即在探討無限射頻辨識系統(RFID)導入成功的關鍵因素。 / With the driving force from Wal-Mart, the world’s largest retailer and the US Department of Defense, suppliers are required to integrate Radio Frequency Identification (RFID) in their case and pallet shipments to distribution centers in their supply chain. In comparison with traditional bar code labels and magnetic strips for supply chain management, RFID technology offers better visibility and information integration in the supply chain management. In this paper, a variety of Automatic identification technologies will be compared to demonstrate the advantages of RFID solutions. The introduction of RFID technology will be made as well to detail the components in the RFID systems and the factors taken into account in the RFID selection and design phases. In the technology implementation, the diffusion model is adopted to explain the evolution of new technology implementation process. The strategic model for the adoption of RFID technology in business process and management is presented as a guideline for companies who are considering adopting the RFID solutions. The impact on business management and the practice guideline to the RIFD implementation are illustrated. The factor analysis in the driving forces and resistance to the RFID adoption are examined to identify the attributes of its successful implementation and the variables of its adoption. According to the factor analysis, the driving forces are summarized into four major ones, including technology innovation, government and standard organization influence, organizational readiness and inter-organization demands. Two cases of RFID applications are presented to illustrate the factors taken into account in the RFID implementation. These two cases include the health care application and agricultural product process application. Companies gain the benefits of the improvement in the production efficiency and quality control over the business process and management. Information flow and capturing are becoming visible and automatic with the implementation of RFID technology.

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RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a consumer moves inside a store. By capturing the entire shopping process including the movement path rather than analyzing merely the shopping basket at check-out, the RFID-collected data provide unique and exciting opportunities to study consumer purchase behavior and thus lead to actionable marketing applications.This dissertation research focuses on (a) advancing the representation and management of the RFID-collected shopping path data; (b) analyzing, modeling and predicting customer shopping activities with a spatial pattern discovery approach and a dynamic probabilistic modeling based methodology to enable advanced spatial business intelligence. The spatial pattern discovery approach identifies similar consumers based on a similarity metric between consumer shopping paths. The direct applications of this approach include a novel consumer segmentation methodology and an in-store real-time product recommendation algorithm. A hierarchical decision-theoretic model based on dynamic Bayesian networks (DBN) is developed to model consumer in-store shopping activities. This model can be used to predict a shopper's purchase goal in real time, infer her shopping actions, and estimate the exact product she is viewing at a time. We develop an approximate inference algorithm based on particle filters and a learning procedure based on the Expectation-Maximization (EM) algorithm to perform filtering and prediction for the network model. The developed models are tested on a real RFID-collected shopping trip dataset with promising results in terms of prediction accuracies of consumer purchase interests.This dissertation contributes to the marketing and information systems literature in several areas. First, it provides empirical insights about the correlation between spatial movement patterns and consumer purchase interests. Such correlation is demonstrated with in-store shopping data, but can be generalized to other marketing contexts such as store visit decisions by consumers and location and category management decisions by a retailer. Second, our study shows the possibility of utilizing consumer in-store movement to predict consumer purchase. The predictive models we developed have the potential to become the base of an intelligent shopping environment where store managers customize marketing efforts to provide location-aware recommendations to consumers as they travel through the store.

With the ever growing application requirements for wireless power transmission in recent years, use of Ultra High Frequency (UHF) band via passive RFID technology escalates quickly. However, limited read range and outdoor interference has always been a great obstacle for various RFID applications. Escalating power transmission at the tag to identify and amplify received power under flawless conditions of electromagnetic theory do not provide estimates of read-rates, which bring major limitations to RFID system performance. Therefore, discovering the reason behind these problems and assessing the performance of backscatter power to improve the system performance remains as a crying need.   Implying radar cross section (RCS) mechanism into RFID can enhance the system performance at a larger extent, as passive RFID works same as radar at far field range by detecting backscatter signal from target object. Antenna radiation pattern and co located interference effect are vital considerations for RFID propagation mechanism and tag read range optimization. Consequently, the robust performance of transmitting and receiving antenna will provide a better RCS value when we get them in good agreement with experimental results.   This thesis provides analytical framework for backscatter performance modeling and suggest techniques to enhance the efficiency of reader to tag to reader performance. It explores uncertainties associated with certain parameters like antenna far field radiation property, antenna spacing, optimal backscatter power and communication range, which implies scattering efficiency of the tag and establish a relationship between the measured and predicted values of tag read-rate probabilities. Comparing measurement patterns in both outdoor and in an-echoic chamber, finally it determines method to increase efficiency at power transmission and reception end. Obtained results will encourage the future researchers to design, analyze and enhance the backscattered passive RFID systems at a larger scenario.

This work includes formulation of mechanically flexible magnetic composites and application to a quarter-wavelength microstrip patch antenna benchmarking structure operating in the lower UHF spectrum (~300-500 MHz) to investigate capability for miniaturization. A key challenge is to introduce sufficiently low magnetic loss for successful application. Particles of NiZn ferrite and BaCo ferrite, also known as Co2Z, were characterized. Flexible magnetic composites comprised of 40 vol% NiZn ferrite or BaCo ferrite particles in a silicone matrix were formulated. Effects of treating the particles with silane in the formulation process were not detectable, but larger particle size showed to increase complex permittivity and complex permeability. By comparing complex permittivity and complex permeability of the composites, BaCo ferrite was selected for the antenna application. Antennas on the developed magnetic composite and pure silicone substrates were electromagnetically modeled in a full-wave FEM EM solver. A prototype of the antenna on the magnetic composite was fabricated. Good agreement between the simulated and measured results was found. Comparison of the antennas on the magnetic composite versus the pure silicone substrate showed miniaturization capability of 2.4X and performance differences of increased bandwidth, reduced Q, and reduced gain. A key finding of this study is that a small amount of permeability (relative permeability ~2.5) can provide relatively substantial capability for miniaturization, while sufficiently low magnetic loss can be introduced for successful application at the targeted operating frequency. The magnetic composite showed the capability to fulfill this balance and to be a feasible option for RFID applications in the lower UHF spectrum.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (p. 67-68).

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph.D.)--University of Melbourne, Dept. of Electrical and Electronic Engineering, 2007. / Typescript. Includes bibliographical references (leaves 139-156).

Thesis (Ph.D.)--Mississippi State University. Department of Management and Information Systems. / Title from title screen. Includes bibliographical references.