Real-time Positioning System Research Articles

Development and Experimentation of a Real-Time Greenhouse Positioning System Based on IUKF-UWB

Development and Experimentation of a Real-Time Greenhouse Positioning System Based on IUKF-UWB

Real-Time Low Energy Indoor Positioning System to Efficient Use of Operating Theaters with Medical Asset and Staff Tracking

Real-Time Low Energy Indoor Positioning System to Efficient Use of Operating Theaters with Medical Asset and Staff Tracking

Assessment of compaction quality of highway subgrade via roller-integrated positive maximum velocity detection technique

ABSTRACT Quality control of compaction is vital for the long-term performance of highways. Specifically, the assessment of subgrade compaction quality is a key factor. Currently, compaction quality is primarily controlled by parameters set during compaction and subsequent random sampling tests post-construction. However, the impact of human factors and limitations of these sampling tests fall short of meeting the standards of large-scale precision construction. This study introduces a real-time monitoring index to characterise the compaction quality of silt subgrades: the maximum velocity compaction value (MVCV). This index is derived using a combination of the roller-integrated positive maximum velocity detection system and real-time kinematic Beidou positioning system (RTK-BDS). Furthermore, the Green spline interpolation method allows for estimating compaction quality values at any point. A case study from the Hengyong Highway project in China reveals a strong linear relationship between compaction parameters and compactness. This suggests the suitability of this method for characterising the compaction quality of such silt. This assessment approach facilitates a detailed evaluation of compaction quality across the entire construction zone. Hence, it aids in effectively minimising compaction defects, reducing unnecessary rework, and enhancing the overall compaction quality and efficiency of highway construction.

FPGA-Based Real-Time Synchronous Parallel System for Underwater Acoustic Positioning and Navigation

With the development of autonomous underwater vehicles, the research of underwater acoustic positioning and navigation technology is becoming increasingly important. However, most underwater positioning systems use the traditional acoustic response synchronization method, which leads to insufficient positioning real-time performance and refresh rate. To solve this problem, this paper proposes a real-time global synchronization mechanism with high-precision atomic clocks, which can realize one positioning through one-way propagation, so that the positioning efficiency is at least doubled. To further improve the positioning accuracy, an M-sequence-based phase offset modulation method is proposed to facilitate beacon identification. Consequently, a normalized signal matching capture algorithm against Doppler effect is designed, and the positioning solution is fused with the real-time sound ray bending compensation mechanism. Based on the above content, we developed an FPGA-based real-time synchronous parallel underwater positioning and navigation (RSPUPN) system, which uses FPGA+MCU architecture to accelerate signal processing operations in a low-power and parallel way. The test results in the South China Sea show that the operating range of the system can reach 8 km, the positioning refresh rate can reach 0.2 Hz, and the relative average position error is 0.25%, confirming the effectiveness of the designed system in industrial applications.

Assessment of the GNSS-RTK for Application in Precision Forest Operations

A smart thinning operation refers to an advanced method of selecting and cutting trees to be thinned based on digitally captured forest information. In smart thinning operations, workers use the coordinates of individual trees to navigate to the target trees for thinning. However, it is difficult to accurately locate individual trees in a forest stand covered with a canopy, necessitating a precise real-time positioning system that can be used in the forest. Therefore, this study aimed to evaluate the applicability of the global navigation satellite system real-time kinematic (GNSS-RTK) device in a forest stand through analysis of its positioning accuracy within the forest environment and evaluation of the operational range of the single-baseline RTK based on analysis of the positioning precision and radio signal strength index (RSSI) change with increasing distance from the base station. The results showed that the root mean square error (RMSE) of the horizontal positioning error was highly accurate, with an average of 0.26 m in Larix kaempferi stands and 0.48 m in Pinus koraiensis stands. The RSSI decreased to a minimum of −103.3 dBm within 1 km of distance from the base station; however, this had no significant impact on the horizontal positioning precision. The conclusion is that the GNSS-RTK is suitable for use in smart thinning operations.

Applicability of real-time PPP technique in polar regions as an accurate and efficient real-time positioning system

Applicability of real-time PPP technique in polar regions as an accurate and efficient real-time positioning system

Bunching behaviour in housed dairy cows at higher ambient temperatures

Bunching behavior in cattle may occur for several reasons including enabling social interactions, a response to stress or danger, or due to shared interest in resources such as feeding or watering areas. There is evidence in pasture grazed cattle that bunching may occur more frequently at higher ambient temperatures, possibly due to sharing of fly-load or to seek shade from the direct sun under heat stress conditions. Here we demonstrate how bunching behavior is associated with higher ambient temperatures in a barn-housed UK dairy herd. A real-time local positioning system was used, as part of a precision livestock farming (PLF) approach, to track the spatial position and activity of a commercial dairy herd (∼100 cows) in a freestall barn continuously at high temporal resolution for 4 mo between August and November 2014. Bunching was determined using 4 different spatial measures determined on an hourly basis: herd full and core range size, mean herd intercow distance (ICD), and mean herd nearest-neighbor distance (NND). For hourly mean ambient temperatures above 20°C, the herd showed higher bunching behavior with increasing ambient temperature (i.e., reduced full and core range size, ICD, and NND). Aggregated space-use intensity was found to positively correlate with localized variations in temperature across the barn (as measured by animal-mounted sensors), but the level of correlation decreased at higher ambient barn temperatures. Bunching behavior may increase localized temperatures experienced by individuals and hence may be a maladaptive behavioral response in housed dairy cattle, which are known to suffer heat stress at higher temperatures. Our study is the first to use high-resolution positional data to provide evidence of associations between bunching behavior and higher ambient temperatures for a barn-housed dairy herd in a temperate region (UK). Further studies are needed to explore the exact mechanisms for this response to inform both welfare and production management.

Visual Place Recognition of Robots via Global Features of Scan-Context Descriptors with Dictionary-Based Coding

Self-localization is a crucial requirement for visual robot place recognition. Particularly, the 3D point cloud obtained from 3D laser rangefinders (LRF) is applied to it. The critical part is the efficiency and accuracy of place recognition of visual robots based on the 3D point cloud. The current solution is converting the 3D point clouds to 2D images, and then processing these with a convolutional neural network (CNN) classification. Although the popular scan-context descriptor obtained from the 3D data can retain parts of the 3D point cloud characteristics, its accuracy is slightly low. This is because the scan-context image under the adjacent label inclines to be confusing. This study reclassifies the image according to the CNN global features through image feature extraction. In addition, the dictionary-based coding is leveraged to construct the retrieval dataset. The experiment was conducted on the North-Campus-Long-Term (NCLT) dataset under four-seasons conditions. The results show that the proposed method is superior compared to the other methods without real-time Global Positioning System (GPS) information.

Establishing security measures for the protection of production workers through UWB real-time localization technology

This article discusses Sewio's real-time positioning system, which helps organizations protect workers in production areas by improving their safety, reducing the time needed for collection and rescue operations, increasing safety and simplifying the reconciliation of workplace time. The system works on the principle that every employee, supplier or visitor to the production plant receives a tag that identifies their exact location in real time. The tag can be part of a standard visitor card, such as cards issued at conferences. Optionally, the labels can also be attached to the clothing within the worker's arm or possibly to a part of the helmet. This article describes the applications of the use of the RTLS localization system in critical cases of threats to workers' health.

Exploring distinct types of intertidal bars on either side of a small estuary using a multifaceted approach

Multiple intertidal bars are common sedimentary landforms found on low-gradient sandy coasts exposed to low to moderate wave energy with a significant tidal range. However, studying the factors that influence their morphology can be challenging due to limited high-resolution field data and tools for bedform analysis. To address this, a multifaceted approach was used to investigate the morphology of intertidal bars at two adjacent tidal beaches on the north and south coasts of a small estuary on the Lianyungang coast of China. This approach included repeated real-time kinematic-global positioning system (RTK-GPS) leveling, unmanned aerial vehicle (UAV) surveys, surface sediment sampling, satellite remote sensing analysis, and automated bedform analysis. The study revealed two distinct types of intertidal bars on either side of the estuary with similar hydrodynamic settings. The north coast presented low-amplitude ridges (also called ridges and runnels), while the south coast had sand waves. The north coast displayed 2–5 oblique, asymmetrical, and dynamic bars that appeared at the seaward end of the intertidal zone and migrated landward to eventually attach to the steep beach on the landward end of the intertidal zone, representing a life cycle. The south coast had 6–9 parallel, continuous, symmetrical, and stable sand bars occupying the lower part of the intertidal zone. Due to the nearly identical hydrodynamic background, the alongshore variability in sediment supply is suggested as a key factor driving the formation of distinct types of bars.

Order picking system evaluation using RTLS method


 In order to maintain competitiveness, it is essential for modern industrial companies to record as much data as possible, by processing which they can obtain valuable information for the development and optimization of production and logistics processes. The RTLS system provides an opportunity to perform this task. RTLS, or Real Time Location System, is a Real-Time Local Positioning System used to identify people and moving objects and to determine and continuously track their current position in a configurable space. Real-time positioning can help us to locate the position of objects and people inside the building. The paper presents and compares the relevant RTLS systems used in practice. In addition, the implementation process of an RTLS system created at the Institute of Logistics is described, as well as its application potential in relation to a case study developed. he abstract contains a short, general description of the topic and the research shown in the paper.

Potential of Precise Fertilization through Adoption of Management Zones Strategy to Enhance Wheat Production

The variability in soil properties and crop yield can be overcome by adoption of smart farming practices through interpolation and mapping of spatial variability patterns. Geospatial technologies can be utilized to determine the cause of spatial variability in fields for site-specific application of fertilizer. This study was designed to quantify and identify the spatial variation in soil properties and wheat (Triticum aestivum L.) yield and to delineate prescription maps for precise application of fertilizer in a semi-arid subtropical region of Pakistan. To examine the variability in soil properties on the production of the considered crop, this study comprised two different fields and each field was divided into (20 × 20 m) grids. The samples of soil were collected at 15 cm and 30 cm soil depths before the fertilization to analyze the different soil characteristics i.e., nitrogen (N), electrical conductivity (EC), potassium (K), soil organic matter (SOM), phosphorus (P), and pH. The boundaries of selected fields and grid points were established with a real-time kinematics-global positioning system (RTK-GPS). The soil data were acquired with a soil proximal sensor at a depth of 7 cm after fertilization. The statistical analysis coefficient of variation (CV), geostatistical-analysis-nugget-to-sill ratio (N:S), and the interpolated maps (ArcGIS pro 2.3) were used to characterize the least to moderate variability of soil parameters and yield, demanding site-specific management of fertilizer application. Cluster analysis was conducted using Minitab 21, which classified soil and yield characteristics into five categories: “very good”, “very low”, “good”, “poor”, and “medium”, with an external heterogeneity and internal homogeneity both more than 60%. Significant relationships (p < 0.05) between soil and crop properties were used to develop the management zones (MZs) for the precise application of fertilizer in wheat fields. Significant differences (p < 0.05) in soil nutrients were found in the very high and very low productivity zones at both sampling times, which suggest delineating the MZs for precise application of fertilizer according to the need of crop and soil properties. The results revealed that the optimum number of MZs for the wheat fields was five and there was heterogeneity in the soil nutrients in five MZs. The findings of this study also highlight the necessity of predicting the crop and soil factors by using precision technologies to develop the prescription maps, because sampling and analysis of soil are expensive and time-consuming. Based on the demand of the soil and crops, site-specific fertilization can increase economic and environmental efficiency.

OCR-RTPS: an OCR-based real-time positioning system for the valet parking

Obtaining the position of ego-vehicle is a crucial prerequisite for automatic control and path planning in the field of autonomous driving. Most existing positioning systems rely on GPS, RTK, or wireless signals, which are arduous to provide effective localization under weak signal conditions. This paper proposes a real-time positioning system based on the detection of the parking numbers as they are unique positioning marks in the parking lot scene. It does not only can help with the positioning with open area, but also run independently under isolation environment. The result tested on both public datasets and self-collected dataset show that the system outperforms others in both performances and applies in practice. In addition, the code and dataset will release later.

Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform.

A low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved.

SPS: Accurate and Real-Time Semantic Positioning System Based on Low-Cost DEM Maps.

This paper presents a Semantic Positioning System (SPS) to enhance the accuracy of mobile device geo-localization in outdoor urban environments. Although the traditional Global Positioning System (GPS) can offer a rough localization, it lacks the necessary accuracy for applications such as Augmented Reality (AR). Our SPS integrates Geographic Information System (GIS) data, GPS signals, and visual image information to estimate the 6 Degree-of-Freedom (DoF) pose through cross-view semantic matching. This approach has excellent scalability to support GIS context with Levels of Detail (LOD). The map data representation is Digital Elevation Model (DEM), a cost-effective aerial map that allows for fast deployment for large-scale areas. However, the DEM lacks geometric and texture details, making it challenging for traditional visual feature extraction to establish pixel/voxel level cross-view correspondences. To address this, we sample observation pixels from the query ground-view image using predicted semantic labels. We then propose an iterative homography estimation method with semantic correspondences. To improve the efficiency of the overall system, we further employ a heuristic search to speedup the matching process. The proposed method is robust, real-time, and automatic. Quantitative experiments on the challenging Bund dataset show that we achieve a positioning accuracy of 73.24%, surpassing the baseline skyline-based method by 20%. Compared with the state-of-the-art semantic-based approach on the Kitti dataset, we improve the positioning accuracy by an average of 5%.

Application of Real-time Positioning Systems to a Forest Stand for Precision Forest Management

Application of Real-time Positioning Systems to a Forest Stand for Precision Forest Management

Wireless sensor network node real-time positioning system based on embedded MCU

In order to reduce the node positioning error and improve the stability of the system, this paper proposes and designs a new wireless sensor network node real-time positioning system based on Embedded MCU. Firstly, the characteristics of wireless sensor networks are analyzed. Secondly, in the hardware part of the system, the embedded node positioning MCU, reset circuit, debugging circuit and serial port circuit are designed to meet the node positioning requirements of the system. Finally, in the software design part, the least square algorithm is used to construct the real-time positioning function of wireless sensor network nodes, and the bat algorithm is used to solve the objective function to complete the real-time positioning of nodes. The experimental results show that all functions of the designed system operate normally, and the average error of node positioning is only 0.21.

Research on the agile management system of workshop production resources

Industry 4.0 has a key technical point that the intelligent factory uses the ‘ raw materials ’ containing information to complete the production tracking. The high-performance real-time positioning system is an important part of the real-time tracking and positioning of materials in the industrial Internet of Things. Passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) technology has become the preferred solution for the industrial 4.0 material real-time positioning system. This paper first analyzes the RFID positioning technology, gives the process architecture of workshop manufacturing resource RFID real-time information-driven production line and the classification and management scheme of multi-source real-time manufacturing information, and realizes the RFID tracking and positioning of workshop manufacturing resource.

Designing and developing a mobile application for indoor real-time positioning and navigation in healthcare facilities.

Navigation portable applications have largely grown during the last years. However, the majority of them works just for outdoor positioning and routing, due to their architecture based upon Global Positioning System signals. Real-Time Positioning System intended to provide position estimation inside buildings is known as Indoor Positioning System (IPS). This paper presents an IPS implemented as a mobile application that can guide patients and visitors throughout a healthcare premise. The proposed system exploits the geolocation capabilities offered by existing navigation frameworks for determining and displaying the user's position. A hybrid mobile application architecture has been adopted because it allows to deploy the code to multiple platforms, simplifying maintenance and upgrading. The developed application features two different working modes for on-site and off-site navigation, which offer both the possibility of actual navigation within the hospital, or planning a route from a list of available starting points to the desired target, without being within the navigable area. Tests have been conducted to evaluate the performance and the accuracy of the system. The proposed application aims to overcome the limitations of Global Navigation Satellite System by using magnetic fingerprinting in combination with sensor fusion simultaneously. This prevents to rely on a single technology, reducing possible system failures and increasing the scalability.

LIDAR-Inertial Real-Time State Estimator with Rod-Shaped and Planar Feature

State estimation and mapping based on Light Detection and Ranging (LIDAR) are important for autonomous systems. Point cloud registration is a crucial module affecting the accuracy and real-time performance of LIDAR simultaneous localization and mapping (SLAM). In this paper, a novel point cloud feature selection for LIDAR-inertial tightly coupled systems is proposed. In the front-end, a point cloud registration is carried out after marking rod-shaped and planar feature information which is different from the existing LIDAR and inertial measurement unit (IMU) integration scheme. This preprocessing method subsequently reduces the outliers. IMU pre-integration outputs high-frequency result and is used to provide the initial value for LIDAR solution. In the scan-to-map module, a computationally efficient graph optimization framework is applied. Moreover, the LIDAR odometry further constrains the IMU states. In the back-end, the optimization based on sliding-window incorporates the LIDAR-inertial measurement and loop closure global constraints to reduce the cumulative error. Combining the front-end and back-end, we propose the low drift and high real-time LIDAR-inertial positioning system. Furthermore, we conducted an exhaustive comparison in open data sequences and real-word experiments. The proposed system outperforms much higher positioning accuracy than the state-of-the-art methods in various scenarios. Compared with the LIO-SAM, the absolute trajectory error (ATE) average RMSE (Root Mean Square Error) in this study increases by 64.45% in M2DGR street dataset (street_01, 04, 07, 10) and 24.85% in our actual scene datasets. In the most time-consuming mapping module of each system, our system runtime can also be significantly reduced due to the front-end preprocessing and back-end graph model.