Ultra high frequency (UHF) radio-frequency identification (RFID) for robot perception and mobile manipulation

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.PhDCommittee Chair: Kemp, Charles C.; Committee Member: Abowd, Gregory; Committee Member: Howard, Ayanna; Committee Member: Ingram, Mary Ann; Committee Member: Reynolds, Matt; Committee Member: Tentzeris, Emmanoui

Object localization with RFID technology

In this work we analyse the approaching of a mobile robot using RFID (Radio Frequency Identification) Technology with the purpose of finding and navigating towards RFID tags without visual object detection. The aim is the evaluation of ROS to provide the means to allow a mobile robot to approach to RFID tags, using a radiofrequency antenna as a transmitter/receptor element of signals. As part of this system the robot emits radiofrequency signals searching for a particular RFID tag, and then it is approached towards the particular RFID tag until the signal strength was maximized, it means that the object was localized and captured. The approach has been tested using two different algorithms which work together: - Linear search. - Advanced search. All the code and tests of this project are based to work with one antenna. On one hand it means simplicity, but on the other hand it can mean a lack of precision.En este proyecto se pone en práctica la tecnología RFID (Radio Frequency Identification) con el propósito de localización y aproximación de un robot móvil hacia una etiqueta RFID fijada en un objeto sin utilizar detección visual. El objetivo es utilizar ROS (Robot Operating System) para lograr que un robot móvil se acerque a las etiquetas RFID, utilizando una antena RF como elemento TX/ RX. El robot emite señales RF en busca de una etiqueta RFID en particular. Tras hallar la etiqueta RFID deseada se comienza a realizar la maniobra de aproximación hasta superar cierto umbral de potencia de señal recibida, RSSI (Received Signal Strengh Indicator), especificado por parámetro. Este proyecto funciona con una sola antena. Esto significa simplicidad, pero por otro lado, cierta falta de precisión.Ingeniería de Telecomunicació

Helmsman, Set a Course : Using a Compass and RFID Tags for Indoor Localisation and Navigation

Localisation and navigation are still two of the most important issues in mobile robotics. In certain indoor application scenarios RFID (radio frequency identification)-based absolute localisation has been found to be especially successful in supporting navigation. In this paper we evaluate the feasibility of an RFID and compass based approach to robot localisation and navigation for indoor environments that are dominated by corridors. We describe our system and evaluate its performance in a small, but full-scale, test environment

Indoor Localization System based on Artificial Landmarks and Monocular Vision

 This paper presents a visual localization approach that is suitable for domestic and industrial environments as it enables accurate, reliable and robust pose estimation. The mobile robot is equipped with a single camera which update sits pose whenever a landmark is available on the field of view. The innovation presented by this research focuses on the artificial landmark system which has the ability to detect the presence of the robot, since both entities communicate with each other using an infrared signal protocol modulated in frequency. Besides this communication capability, each landmark has several high intensity light-emitting diodes (LEDs) that shine only for some instances according to the communication, which makes it possible for the camera shutter and the blinking of the LEDs to synchronize. This synchronization increases the system tolerance concerning changes in brightness in the ambient lights over time, independently of the landmarks location. Therefore, the environment’s ceiling is populated with several landmarks and an Extended Kalman Filter is used to combine the dead-reckoning and landmark information. This increases the flexibility of the system by reducing the number of landmarks required. The experimental evaluation was conducted in a real indoor environment with an autonomous wheelchair prototype

Navigating the Corridors of Power : Using RFID and Compass Sensors for Robot Localisation and Navigation

Localisation and navigation are still of the most important issues in mobile robotics. In certain indoor application scenarios Radio frequency identification (RFID) based absolute localisation has been found to be especially successful in supporting navigation. In this paper we examine the feasibility of an RFID and compass based approach to robot localisation and navigation for indoor environments that are dominated by corridors. We present a proof of concept system and show how it can be used to localized within and navigate through an environment

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Estimation of unknown node positions of a localization network with a multi-robot system

In this thesis, a novel method for estimating the node positions of a localization network is presented. A multi-robot system is used to map the positions of the network nodes, while the robots track their own position simultaneously. It is an application of simultaneous localization and mapping (SLAM). The localization is based on bearing angle measurements between a robot and a network node. Hence, the method used for the localization can be called bearing-only SLAM. The localization method is based on a probabilistic approach. All the measurement data are collected to a centralized Kalman Filter. As a result of the non-linear measurement equation, the Extended Kalman Filter (EKF) algorithm is used. The centralized structure maintains the covariances between all the entities and thus takes full advantage of the cooperation in a multi-robot system. The algorithm is shown to work with a sparse distribution of landmarks. A robot makes a bearing angle measurement to only one landmark at a time. Therefore, the computational complexity of the Kalman filter stays low. The Radio Frequency Identification (RFID) technology is used in the case study presented in this thesis. It is shown that passive RFID tags can serve as landmarks with a unique ID. The inexpensive, maintenance-free RFID tags can easily be distributed over the intended working area of the robots to form a localization network. The bearing angle measurements to the RFID tags do not need to be highly accurate as the proposed algorithm can handle uncertain measurements. Simulations and laboratory experiments are used in order to prove the performance of the proposed method

Finding and Navigating to Household Objects with UHF RFID Tags by Optimizing RF Signal Strength

©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 14-18 September 2014, Chicago, IL.DOI: 10.1109/IROS.2014.6942914We address the challenge of finding and navigating to an object with an attached ultra-high frequency radio- frequency identification (UHF RFID) tag. With current off-the- shelf technology, one can affix inexpensive self-adhesive UHF RFID tags to hundreds of objects, thereby enabling a robot to sense the RF signal strength it receives from each uniquely identified object. The received signal strength indicator (RSSI) associated with a tagged object varies widely and depends on many factors, including the object’s pose, material prop- erties and surroundings. This complexity creates challenges for methods that attempt to explicitly estimate the object’s pose. We present an alternative approach that formulates finding and navigating to a tagged object as an optimization problem where the robot must find a pose of a directional antenna that maximizes the RSSI associated with the target tag. We then present three autonomous robot behaviors that together perform this optimization by combining global and local search. The first behavior uses sparse sampling of RSSI across the entire environment to move the robot to a location near the tag; the second samples RSSI over orientation to point the robot toward the tag; and the third samples RSSI from two antennas pointing in different directions to enable the robot to approach the tag. We justify our formulation using the radar equation and associated literature. We also demonstrate that it has good performance in practice via tests with a PR2 robot from Willow Garage in a house with a variety of tagged household objects