Real-time Location System Research Articles

Potentials and technical implications of tag based and AI enabled optical real-time location systems (RTLS) for manufacturing use cases

Utilizing real-time position information of individual factory objects is a promising field of action in manufacturing systems. For realizing that, different types of real-time locating systems (RTLS) are available which differ according their inherent characteristics, performance and, thus, their feasibility for value adding manufacturing use cases. The paper introduces an innovative RTLS which utilizes optical AI enhanced sensors to detect and track objects. Detailed technical analyses also in comparison with established tag based ultra-wideband (UWB) RTLS underline the general feasibility and good performance. Finally, the capability profiles of those different RTLS technologies were brought together with the requirements of specific manufacturing use cases in order to derive favourable application fields.

Exploring the Added-Value of Integrating Real-Time Location Systems for Tracking Critical Maintenance Tools

Searching and optimally locating maintenance tools is an important undertaking that influences productivity. The efficiency lost can be high for intensive maintenance processes, particularly in instances where technicians share critical (and expensive) tooling. Real-Time Location Systems (RTLS) can serve as a feasible solution for minimizing travel and search times, by tagging tools, thereby dynamically tracking their spatial location real-time. Moreover, RTLS can be deployed alongside Automated Guided Vehicles (AGV’s) thereby supporting smart material handling.In this paper, we propose a model for exploring the added value of implementing RTLS with AGV to better support maintenance processes in high-demand manufacturing and maintenance shop floors. The NetLogo (multi-agent programmable environment) simulation model mimics interactions of maintenance technicians, tasks, and critical tooling within an aircraft maintenance facility. To evaluate the added value of tagging critical tooling to better enhance efficiency locating tools, we define three use-cases. The first case mimics a current scenario in which critical tools are untagged, hence assuming significant search and travel times are incurred by technicians. The second case explores scenarios where tools are tagged, and trackable within the spatial environment of the hangar. The third case integrates both RTLS and AGVs to support both locating tools, and minimising travel times by technicians. Initial simulations results show significant added value for the second and third experiments, verifying suitability of implementing RTLS for complex maintenance and manufacturing processes.

Empirical findings for the usage of 5G as a basis for real time locating systems (RTLS) in the automotive industry

The digitalization of processes and integration of a digital twin are key factors for a successful system design to realize optimization and control in industrial production. The usage of positioning data is essential to achieve these goal - knowing where tools, products, and parts are in real-time. It enables processes to become intelligent, automated, and logged throughout production. Current radio real-time-locating-systems (RTLS) in manufacturing are often using Ultra-Wideband (UWB) solutions requiring individual infrastructure resulting in high costs. 5G technology is turning its focus from mobile-communication to become a globally standardized industry solution - now including high accuracy positioning. Development shows that the infrastructure already used for communication can potentially be utilized for positioning. This contribution aims to determine the industrial maturity of 5G positioning by performing guided empirical expert interviews based on a structured literature review with 28 experts. Topics such as state-of-the-art, comparison to UWB-RTLS and safety are discussed. Results show that industrial interest in 5G positioning is high and centimeter-level accuracies are pursued. However, practical tests show hardware is in early development, and tests are only performed in controlled environments. Concluding, 5G positioning shows great potential - but industrial pilots are required to validate characteristics in complex scenarios.

Digital Value Stream Mapping: Application of UWB Real Time Location Systems

Value stream mapping (VSM) is a valuable practice employed by industry experts to identify inefficiencies in the value chain due to its visual representation capability and general ease of use. VSM supports the visualization of product and material flows as a snapshot and enhances understanding of production behavior across multiple domains; however, it can lead to oversimplification, tedious/inaccurate data collection, and can have a static nature. Through improved digital integration this paper presents a semiautomated VSM solution that leverages dynamic Real Time Location System (RTLS) data to track motion throughout a production floor. This solution incorporates a high precision UWB RTLS, that allows for the real-time analysis and formulation of different state maps to track processes, equipment, people, and materials on the manufacturing floor. This multi-state mapping outcome has demonstrated the ability to allow for the rapid comparisons of various state maps and supports more effective and robust decision making. Based on initial results the system was able to continuously map accurate and reliable shop floor data and update new maps with limited human intervention, demonstrating the ability for RTLS to enhance VSM.

Real Time Locating Systems for Human Centered Production Planning and Monitoring

Production companies often operate in a dynamic and volatile environment. This leads to an increasing demand for continuous changes in their production systems and processes. Furthermore, decreasing product life cycle times and rising market demand for product variety and individualized products is bringing about the necessity for the monitoring and coordination of processes in the operations phase. As a result of these developments production management is facing new challenges in decision making for the optimal settings of the production system design and the related coordination of production. All of this demands enormous efforts to maintain a consistent and reliable database for the ongoing configuration and coordination of the production system. It is thus a remarkable challenge for industrial companies. Real time locating systems (RTLS) with their ability to continuously monitor the current position and parameters (speed, direction, etc.) of process resources (operators, equipment, products, etc.) offer several potential benefits for the manufacturing industry. The potential areas of application can be identified in different layers of production management. They range from data acquisition on shop floor level through reconfiguration of production systems to providing real time feedback for the blue collars. Furthermore, it allows dynamic coordination of production orders for industrial plants via appropriate digital twin (DT) technologies. This paper proposes an original framework for RTLS in industrial environments and presents a case study for framework application at the TU Graz Learning Factory.

Lamb wave-based real-time damage location system with integrated internet of things

In this paper, an automated Structural Health Monitoring (SHM) system that uses Lamb waves is presented. A miniaturized prototype named ‘Real-Time Damage Location System’ (RTDLS) is designed and fabricated for thin plate structure, which can automatically locate single damage in an area of 100 mm × 100 mm by manually pressing a tactile button or by using an Internet of Things (IoT) platform. RTDLS is a lightweight, mobile, and plug-and-play device that uses 3 piezoelectric wafer transducers. It uses the pitch-catch technique with a novel damage location algorithm to locate damage in a plate. It displays the location of the identified damage on an LCD screen and on the webpage through live broadcasts using an IoT platform. RTDLS can store Lamb wave data corresponding to the pristine and damaged states of the structure in a FLASH memory so that no data is erased until reset action is performed.

Trajectory Prediction of Workers to Improve AGV and AMR Operation based on the Manufacturing Schedule

In semi-automated manufacturing, an increasing amount of intelligent mobile robots operate in close proximity to human workers. Considering future positions of humans allows to further improve the efficiency in terms of throughput of Autonomous Mobile Robots (AMR) and Automated Guided Vehicles (AGV). The longer the prediction horizon, i.e. the more position values of humans can be predicted in the near and distant future, the more a robot can adjust its route accordingly and optimize the process. This paper discusses the challenges of human motion trajectory prediction in manufacturing and presents a schedule-based approach that uses real-time schedule data obtained from Manufacturing Execution Systems (MES). Schedule-awareness in human motion trajectory prediction extends semantic mapping approaches and effectively reduces the number of probable destinations by considering which process steps are next for the currently produced goods. With a reduced set of destinations, the performance of forward-planning trajectory prediction can be improved. For evaluation, a commercial MES is used together with an Ultra-wideband-based Real-Time Locating System (RTLS) for obtaining position data of humans. On this basis, a naive Bayes classifier utilizes MES-schedule and real-time position data to predict human motion intentions. Abstract activity modeling ensures that only a few training data sets are required for deployment, thus making this approach suitable for rapidly changing manufacturing environments such as in flexible manufacturing.

Understanding the RFID Deployment at Sacred Heart Medical Center: Using Technology-Organization-Environment Framework Lenses

This qualitative study based on a single case features the radio frequency identification (RFID)-based real-time location system (RTLS) system to track intravenous (IV) pumps deployed by the Sacred Heart Medical Center (SHMC), Eugene, Oregon --- one unit of the PeaceHealth network based in western U.S.A. Asset tracking management, followed by the tracking of medical staff and patients continue to be leading applications for RFID in the healthcare industry in north America. This case study uses Tornatzky’s technology-organization-environment framework in interpreting and understanding the RFID deployment at SHMC/Eugene, Oregon.

Constrained Localization: A Survey

Indoor localization techniques have been extensively studied in the last decade. The well-established technologies enable the development of Real-Time Location Systems (RTLS). A good body of publications emerged, with several survey papers that provide a deep analysis of the research advances. Existing survey papers focus on either a specific technique and technology or on a general overview of indoor localization research. However, there is a need for a use case-driven survey on both recent academic research and commercial trends, as well as a hands-on evaluation of commercial solutions. This work aims at helping researchers select the appropriate technology and technique suitable for developing low-cost, low-power localization system, capable of providing centimeter level accuracy. The article is both a survey on recent academic research and a hands-on evaluation of commercial solutions. We introduce a specific use case as a guiding application throughout this article: localizing low-cost low-power miniature wireless swarm robots. We define a taxonomy and classify academic research according to five criteria: Line of Sight (LoS) requirement, accuracy, update rate, battery life, cost. We discuss localization fundamentals, the different technologies and techniques, as well as recent commercial developments and trends. Besides the traditional taxonomy and survey, this article also presents a hands-on evaluation of popular commercial localization solutions based on Bluetooth Angle of Arrival (AoA) and Ultra-Wideband (UWB). We conclude this article by discussing the five most important open research challenges: lightweight filtering algorithms, zero infrastructure dependency, low-power operation, security, and standardization.

2-D Indoor Passive Real-Time Location System Based on Ultrawideband Technology

2-D Indoor Passive Real-Time Location System Based on Ultrawideband Technology

Design and Implementation of Real-Time Localization System (Rtls) Based on Uwb and Tdoa Algorithm

Nowadays, accurate localization plays an essential role in many fields, like target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and high cost. So the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive Ultra Wide-Band (UWB) based RTLS is presented in this paper. We first introduce the architecture of the real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and analyze the relationship between anchor deployment and positioning accuracy. The Extended Kalman Filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy in moving targets.

Exploring a Novel Tool to Measure Wandering Behavior in the Early Childhood Classroom.

Wandering, or random movement, affects cognitive and social skills. However, we lack methods to objectively measure wandering behavior. The purpose of this pilot study was to explore the use of the Ubisense real-time location system (RTLS) in an early childhood setting to explore wandering in typically developing (TD) children (n = 2) and children with or at risk for developmental disabilities (WA-DD; n = 3). We used the Ubisense RTLS, a tool for capturing locations of individuals in indoor environments, and Fractal Dimension (FD) to measure the degree of wandering or the straightness of a path. Results of this descriptive, observational study indicated the Ubisense RTLS collected 46,229 1-s location estimates across the five children, and TD children had lower FD (M = 1.36) than children WA-DD (M = 1.42). Children WA-DD have more nonlinear paths than TD children. Implications for measuring wandering are discussed.

Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power

Retailers need to manage a series of complex decisions relating to numerous products. To reduce this complexity, they have introduced category management practices, which consider groups of similar products (categories) that can be managed separately as single business units (SBUs). Although the concept that the store offer should be organised as a category mix and that this strategy allows for better overall store management is already consolidated, retailers still struggle to adopt an approach to the store performance measurement starting from a category level perspective. Nowadays, the available methods for measuring categories’ performance are quite limited. The current trend sees the measurement of category performance mainly based on sell-out data that are ill-equipped to fully address category management issues. Retailers should broaden their field of analysis not only by focusing on the product/sales perspective but also by including other methodologies such as shopper behaviour analysis. In this regard, the use of technology offers the retail sector new perspectives for those analysis. Therefore, we intend to contribute to the ongoing debate on the retail analytics topic by presenting a shopper behaviour analytics system for category management performance monitoring. More in detail, we could derive a new key performance indicator, category conversion power (CCP), aimed at analysing and comparing the single categories organised within the store. The research is based on a unique dataset obtained from a real-time locating system (RTLS), which allowed us to collect behavioural data togheter with sell-out data (from POS scanner). We argue that retailers could exploit this new analytical method to gain more understanding at the category level and therefore make data-driven decisions aimed at improving performance at the store level.

Production and maintenance in industries: impact of industry 4.0

PurposeProduction industries are undergoing a digital transition, referred to as the fourth industrial revolution or Industry 4.0, as a result of rapidly expanding advances in information and communication technology. The purpose of this research is to provide a conceptual insight into the impact of unique capabilities from the fourth industrial revolution on production and maintenance tasks in terms of providing the existing production companies a boost by making recommendations on areas and tasks of great potential.Design/methodology/approachA survey and a literature review are among the research methods used in the research. The survey collected empirical data using a semi-structured questionnaire, which provided a broad overview of the company's present condition in terms of production and maintenance, resulting in more comprehensive and specific information regarding the study topics.FindingsThe study points out that, the implementation of I4.0-technology leads to an increase in production, asset utilization, quality, reduced machine down time in industries, and maintenance. Sensor technology, big data analysis, cloud technologies, mobile end devices, and real-time location systems are now being implemented to improve production processes and boost organizational competitiveness. Moreover, the study highlights that data acquired throughout the production process is utilized for quality control, predictive maintenance, and automatic production control. Furthermore, I4.0 solutions help companies to be more efficient with assets at each stage of the process, allowing them to have a stronger control on inventories and operational-optimization potential.Originality/valueThe findings of the study was supported by empirical data collected through survey that provides an intangible understanding of the importance of distinctive capabilities from the I4.0 revolution on production and maintenance tasks. In this study, some recommendations and guidelines to enhance these tasks are provided that are vital for existing production companies.

OffshoreMuster: An Integrated Real Time Localisation, Mustering and Evacuation Management System for Offshore Oil & Gas Health and Safety Operations

OffshoreMuster: An Integrated Real Time Localisation, Mustering and Evacuation Management System for Offshore Oil & Gas Health and Safety Operations

Staff and Facility Utilization in Direct Patient Transfer to the Comprehensive Stroke Center: Testing a Real-Time Location System for Automatic Patient Pathway Characterization.

Introduction: Starting reperfusion therapies as early as possible in acute ischemic strokes are of utmost importance to improve outcomes. The Comprehensive Stroke Centers (CSCs) can use surveys, shadowing personnel or perform journal analysis to improve logistics, which can be labor intensive, lack accuracy, and disturb the staff by requiring manual intervention. The aim of this study was to measure transport times, facility usage, and patient–staff colocalization with an automated real-time location system (RTLS).Patients and Methods: We tested IR detection of patient wristbands and staff badges in parallel with a period when the triage of stroke patients was changed from admission to the emergency room (ER) to direct admission to neuroradiology.Results: In total, 281 patients were enrolled. In 242/281 (86%) of cases, stroke patient logistics could be detected. Consistent patient–staff colocalizations were detected in 177/281 (63%) of cases. Bypassing the ER led to a significant decrease in median time neurologists spent with patients (from 15 to 9 min), but to an increase of the time nurses spent with patients (from 13 to 22 min; p = 0.036). Ischemic stroke patients used the most staff time (median 25 min) compared to hemorrhagic stroke patients (median 13 min) and stroke mimics (median 15 min).Discussion: Time spent with patients increased for nurses, but decreased for neurologists after direct triage to the CSC. While lower in-hospital transport times were detected, time spent in neuroradiology (CT room and waiting) remained unchanged.Conclusion: The RTLS could be used to measure the timestamps in stroke pathways and assist in staff allocation.

The feasibility and validity of using a real time location system (RTLS) to measure bedside contact time.

The association between the nurse-to-patient ratio and patient outcomes has been extensively investigated. Real time location systems have the potential capability of measuring the actual amount of bedside contact patients receive. This study aimed to determine the feasibility and accuracy of real time location systems as a measure of the amount of contact time that nurses spent in the patients' bed space. An exploratory, observational, feasibility study was designed to compare the accuracy of data collection between manual observation performed by a researcher and real time location systems data capture capability. Four nurses participated in the study, which took place in 2019 on two hospital wards. They were observed by a researcher while carrying out their work activities for a total of 230 minutes. The amount of time the nurses spent in the patients' bed space was recorded in 10-minute blocks of time and the real time location systems data were extracted for the same nurse at the time of observation. Data were then analysed for the level of agreement between the observed and the real time location systems measured data, descriptively and graphically using a kernel density and a scatter plot. The difference (in minutes) between researcher observed and real time location systems measured data for the 23, 10-minute observation blocks ranged from zero (complete agreement) to 5 minutes. The mean difference between the researcher observed and real time location systems time in the patients' bed space was one minute (10% of the time). On average, real time location systems measured time in the bed space was longer than the researcher observed time. There were good levels of agreement between researcher observation and real time location systems data of the time nurses spend at the bedside. This study confirms that it is feasible to use real time location systems as an accurate measure of the amount of time nurses spend at the patients' bedside.

Commentary: The feasibility and validity of using a real time location system (RTLS) to measure bedside nursing care.

Commentary: The feasibility and validity of using a real time location system (RTLS) to measure bedside nursing care.

Current technologies and the applications of data analytics for crude oil leak detection in surface pipelines

Pipeline pressure monitoring has been the traditional and most popular leak detection approach, however, the delays with leak detection and localization coupled with the large number of false alarms led to the development of other sensor-based detection technologies. The Real Time Transient Model (RTTM) currently has the best performance metric, but it requires collection and analysis of large data volume which, in turn, has an impact in the detection speed. Several data mining (DM) methods have been used for leak detection algorithm development with each having its own advantages and shortcomings. Mathematical modelling is used for the generation of simulation data and this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the ANN and SVM require a large training dataset for development of accurate models, mathematical modelling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modelling for the development of a robust real time leak detection and localization system for surface pipelines. Several case studies are also presented.

ZigBee Technology to Provide Elderly People with Well-Being at Home

Background: The number of elderly people is growing, and it becomes a challenge for the families and governments regard their health. They are facing declines in their health and missing some of the quality of life that is provided to them. They are unable to control their self. They are vulnerable to fall and get in dangerous or restricted area. Aim: This study aims at conducing an experiment using Real-Time Location System (RTLS) via ZigBee technology in order to track the elderly person at his/her home. Objectives: To track the old age people and provide them proper care and support whenever they are in challenging times. Method: RTLS is a system that offers a real-time tracking and positioning of people or objects. Results: The results showed that the ZigBee technology provides valuable insights to determine the location of an elderly person at home and sends an alert when a person gets in a restricted area. Conclusion: ZigBee have contributed to be one of the best of its kind protocol that can help to track and mange old aged people at house.