19 research outputs found

    Recent Advances in Indoor Localization Systems and Technologies

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    Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

    Mobile Robots Navigation

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    Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

    Mapping, Path Following, and Perception with Long Range Passive UHF RFID for Mobile Robots

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    Service robots have shown an impressive potential in providing assistance and guidance in various environments, such as supermarkets, shopping malls, homes, airports, and libraries. Due to the low-cost and contactless way of communication, radio-frequency identification (RFID) technology provides a solution to overcome the difficulties (e.g. occlusions) that the traditional line of sight sensors (e.g. cameras and laser range finders) face. In this thesis, we address the applications of using passive ultra high frequency (UHF) RFID as a sensing technology for mobile robots in three fundamental tasks, namely mapping, path following, and tracking. An important task in the field of RFID is mapping, which aims at inferring the positions of RFID tags based on the measurements (i.e. the detections as well as the received signal strength) received by the RFID reader. The robot, which serves as an intelligent mobile carrier, is able to localize itself in a known environment based on the existing positioning techniques, such as laser-based Monte Carlo localization. The mapping process requires a probabilistic sensor model, which characterizes the likelihood of receiving a measurement, given the relative pose of the antenna and the tag. In this thesis, we address the problem of recovering from mapping failures of static RFID tags and localizing non-static RFID tags which do not move frequently using a particle filter. The usefulness of negative information (e.g. non-detections) is also examined in the context of mapping RFID tags. Moreover, we present a novel three dimensional (3D) sensor model to improve the mapping accuracy of RFID tags. In particular, using this new sensor model, we are able to localize the 3D position of an RFID tag by mounting two antennas at different heights on the robot. We additionally utilize negative information to improve the mapping accuracy, especially for the height estimation in our stereo antenna configuration. The model-based localization approach, which works as a dual to the mapping process, estimates the pose of the robot based on the sensor model as well as the given positions of RFID tags. The fingerprinting-based approach was shown to be superior to the model-based approach, since it is able to better capture the unpredictable radio frequency characteristics in the existing infrastructure. Here, we present a novel approach that combines RFID fingerprints and odometry information as an input of the motion control of a mobile robot for the purpose of path following in unknown environments. More precisely, we apply the teaching and playback scheme to perform this task. During the teaching stage, the robot is manually steered to move along a desired path. RFID measurements and the associated motion information are recorded in an online-fashion as reference data. In the second stage (i.e. playback stage), the robot follows this path autonomously by adjusting its pose according to the difference between the current RFIDmeasurements and the previously recorded reference measurements. Particularly, our approach needs no prior information about the distribution and positions of the tags, nor does it require a map of the environment. The proposed approach features a cost-effective alternative for mobile robot navigation if the robot is equipped with an RFID reader for inventory in RFID-tagged environments. The capability of a mobile robot to track dynamic objects is vital for efficiently interacting with its environment. Although a large number of researchers focus on the mapping of RFID tags, most of them only assume a static configuration of RFID tags and too little attention has been paid to dynamic ones. Therefore, we address the problem of tracking dynamic objects for mobile robots using RFID tags. In contrast to mapping of RFID tags, which aims at achieving a minimum mapping error, tracking does not only need a robust tracking performance, but also requires a fast reaction to the movement of the objects. To achieve this, we combine a two stage dynamic motion model with the dual particle filter, to capture the dynamic motion of the object and to quickly recover from failures in tracking. The state estimation from the particle filter is used in a combination with the VFH+ (Vector Field Histogram), which serves as a local path planner for obstacle avoidance, to guide the robot towards the target. This is then integrated into a framework, which allows the robot to search for both static and dynamic tags, follow it, and maintain the distance between them. [untranslated]Service-Roboter bergen ein großes Potential bei der Unterstützung, Beratung und Führung von Kunden oder Personal in verschiedenen Umgebungen wie zum Beispiel Supermärkten, Einkaufszentren, Wohnungen, Flughäfen und Bibliotheken. Durch die geringen Kosten und die kontaktlose Kommunikation ist die RFID Technologie in der Lage vorhandene Herausforderungen traditioneller sichtlinienbasierter Sensoren (z.B. Verdeckung beim Einsatz von Kameras oder Laser-Entfernungsmessern) zu lösen. In dieser Arbeit beschäftigen wir uns mit dem Einsatz von passivem Ultrahochfrequenz (UHF) RFID als Sensortechnologie für mobile Roboter hinsichtlich drei grundlegender Aufgabenstellungen Kartierung, Pfadverfolgung und Tracking. Kartierung nimmt eine wesentliche Rolle im Bereich der Robotik als auch beim Einsatz von RFID Sensoren ein. Hierbei ist das Ziel die Positionen von RFID-Tags anhand von Messungen (die Erfassung der Tags als solche und die Signalstärke) zu schätzen. Der Roboter, der als intelligenter mobiler Träger dient, ist in der Lage, sich selbst in einer bekannten Umgebung auf Grundlage der bestehenden Positionierungsverfahren, wie Laser-basierter Monte-Carlo Lokalisierung zurechtzufinden. Der Kartierungsprozess erfordert ein probabilistisches Sensormodell, das die Wahrscheinlichkeit beschreibt, ein Tag an einer gegebenen Position relativ zur RFID-Antenne (ggf. mit einer bestimmten Signalstärke) zu erkennen. Zentrale Aspekte dieser Arbeit sind die Regeneration bei fehlerhafter Kartierung statischer RFID-Tags und die Lokalisierung von nicht-statischen RFID-Tags. Auch wird die Verwendbarkeit negativer Informationen, wie z.B. das Nichterkennen von Transpondern, im Rahmen der RFID Kartierung untersucht. Darüber hinaus schlagen wir ein neues 3D-Sensormodell vor, welches die Genauigkeit der Kartierung von RFID-Tags verbessert. Durch die Montage von zwei Antennen auf verschiedenen Höhen des eingesetzten Roboters, erlaubt es dieses Modell im Besonderen, die 3D Positionen von Tags zu bestimmen. Dabei nutzen wir zusätzlich negative Informationen um die Genauigkeit der Kartierung zu erhöhen. Dank der Eindeutigkeit von RFID-Tags, ist es möglich die Lokalisierung eines mobilen Roboters ohne Mehrdeutigkeit zu bestimmen. Der modellbasierte Ansatz zur Lokalisierung schätzt die Pose des Roboters auf Basis des Sensormodells und den angegebenen Positionen der RFID-Tags. Es wurde gezeigt, dass der Fingerprinting-Ansatz dem modellbasierten Ansatz überlegen ist, da ersterer in der Lage ist, die unvorhersehbaren Funkfrequenzeigenschaften in der vorhandenen Infrastruktur zu erfassen. Hierfür präsentieren wir einen neuen Ansatz, der RFID Fingerprints und Odometrieinformationen für die Zwecke der Pfadverfolgung in unbekannten Umgebungen kombiniert. Dieser basiert auf dem Teaching-and-Playback-Schema. Während der Teaching-Phase wird der Roboter manuell gelenkt, um ihn entlang eines gewünschten Pfades zu bewegen. RFID-Messungen und die damit verbundenen Bewegungsinformationen werden als Referenzdaten aufgezeichnet. In der zweiten Phase, der Playback-Phase, folgt der Roboter diesem Pfad autonom. Der vorgeschlagene Ansatz bietet eine kostengünstige Alternative für die mobile Roboternavigation bei der Bestandsaufnahme in RFID-gekennzeichneten Umgebungen, wenn der Roboter mit einem RFID-Lesegerät ausgestattet ist. Die Fähigkeit eines mobilen Roboters dynamische Objekte zu verfolgen ist entscheidend für eine effiziente Interaktion mit der Umgebung. Obwohl sich viele Forscher mit der Kartierung von RFID-Tags befassen, nehmen die meisten eine statische Konfiguration der RFID-Tags an, nur wenige berücksichtigen dabei dynamische RFID-Tags. Wir wenden uns daher dem Problem der RFID basierten Verfolgung dynamischer Objekte mit mobilen Robotern zu. Im Gegensatz zur Kartierung von RFID-Tags, ist für die Verfolgung nicht nur eine stabile Erkennung notwendig, es ist zudem erforderlich schnell auf die Bewegung der Objekte reagieren zu können. Um dies zu erreichen, kombinieren wir ein zweistufiges dynamisches Bewegungsmodell mit einem dual-Partikelfilter. Die Zustandsschätzung des Partikelfilters wird in Kombination mit dem VFH+ (Vektorfeld Histogramm) verwendet, um den Roboter in Richtung des Ziels zu leiten. Hierdurch ist es dem Roboter möglich nach statischen und dynamischen Tags zu suchen, ihnen zu folgen und dabei einen gewissen Abstand zu halten

    Data Fusion for Materials Location Estimation in Construction

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    Effective automated tracking and locating of the thousands of materials on construction sites improves material distribution and project performance and thus has a significant positive impact on construction productivity. Many locating technologies and data sources have therefore been developed, and the deployment of a cost-effective, scalable, and easy-to-implement materials location sensing system at actual construction sites has very recently become both technically and economically feasible. However, considerable opportunity still exists to improve the accuracy, precision, and robustness of such systems. The quest for fundamental methods that can take advantage of the relative strengths of each individual technology and data source motivated this research, which has led to the development of new data fusion methods for improving materials location estimation. In this study a data fusion model is used to generate an integrated solution for the automated identification, location estimation, and relocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is paid to noisy environments where low-cost RFID tags are attached to all materials, which are sometimes moved repeatedly around the site. A portion of the work focuses partly on relocation detection because it is closely coupled with location estimation and because it can be used to detect the multi-handling of materials, which is a key indicator of inefficiency. This research has successfully addressed the challenges of fusing data from multiple sources of information in a very noisy and dynamic environment. The results indicate potential for the proposed model to improve location estimation and movement detection as well as to automate the calculation of the incidence of multi-handling

    Robust, Energy-Efficient, and Scalable Indoor Localization with Ultra-Wideband Technology

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    Ultra-wideband (UWB) technology has been rediscovered in recent years for its potential to provide centimeter-level accuracy in GNSS-denied environments. The large-scale adoption of UWB chipsets in smartphones brings demanding needs on the energy-efficiency, robustness, scalability, and crossdevice compatibility of UWB localization systems. This thesis investigates, characterizes, and proposes several solutions for these pressing concerns. First, we investigate the impact of different UWB device architectures on the energy efficiency, accuracy, and cross-platform compatibility of UWB localization systems. The thesis provides the first comprehensive comparison between the two types of physical interfaces (PHYs) defined in the IEEE 802.15.4 standard: with low and high pulse repetition frequency (LRP and HRP, respectively). In the comparison, we focus not only on the ranging/localization accuracy but also on the energy efficiency of the PHYs. We found that the LRP PHY consumes between 6.4–100 times less energy than the HRP PHY in the evaluated devices. On the other hand, distance measurements acquired with the HRP devices had 1.23–2 times lower standard deviation than those acquired with the LRP devices. Therefore, the HRP PHY might be more suitable for applications with high-accuracy constraints than the LRP PHY. The impact of different UWB PHYs also extends to the application layer. We found that ranging or localization error-mitigation techniques are frequently trained and tested on only one device and would likely not generalize to different platforms. To this end, we identified four challenges in developing platform-independent error-mitigation techniques in UWB localization, which can guide future research in this direction. Besides the cross-platform compatibility, localization error-mitigation techniques raise another concern: most of them rely on extensive data sets for training and testing. Such data sets are difficult and expensive to collect and often representative only of the precise environment they were collected in. We propose a method to detect and mitigate non-line-of-sight (NLOS) measurements that does not require any manually-collected data sets. Instead, the proposed method automatically labels incoming distance measurements based on their distance residuals during the localization process. The proposed detection and mitigation method reduces, on average, the mean and standard deviation of localization errors by 2.2 and 5.8 times, respectively. UWB and Bluetooth Low Energy (BLE) are frequently integrated in localization solutions since they can provide complementary functionalities: BLE is more energy-efficient than UWB but it can provide location estimates with only meter-level accuracy. On the other hand, UWB can localize targets with centimeter-level accuracy albeit with higher energy consumption than BLE. In this thesis, we provide a comprehensive study of the sources of instabilities in received signal strength (RSS) measurements acquired with BLE devices. The study can be used as a starting point for future research into BLE-based ranging techniques, as well as a benchmark for hybrid UWB–BLE localization systems. Finally, we propose a flexible scheduling scheme for time-difference of arrival (TDOA) localization with UWB devices. Unlike in previous approaches, the reference anchor and the order of the responding anchors changes every time slot. The flexible anchor allocation makes the system more robust to NLOS propagation than traditional approaches. In the proposed setup, the user device is a passive listener which localizes itself using messages received from the anchors. Therefore, the system can scale with an unlimited number of devices and can preserve the location privacy of the user. The proposed method is implemented on custom hardware using a commercial UWB chipset. We evaluated the proposed method against the standard TDOA algorithm and range-based localization. In line of sight (LOS), the proposed TDOA method has a localization accuracy similar to the standard TDOA algorithm, down to a 95% localization error of 15.9 cm. In NLOS, the proposed TDOA method outperforms the classic TDOA method in all scenarios, with a reduction of up to 16.4 cm in the localization error.Cotutelle -yhteisväitöskirj

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    Urban Informatics

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    This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
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