Search Results (509)

Background/Objectives: this study describes the development of a novel three-dimensional electrogoniometer for the quantitative assessment of knee mobility and stability during gait. The primary objective is to determine whether real-time measurements obtained during dynamic activity provide more clinically relevant information than traditional static assessments. Methods: the device employs angular position encoders to capture knee joint kinematics—specifically flexion, extension, rotation, and tibial translation—during locomotion. Data are transmitted in real time to an Android-based application, enabling immediate graphical visualization. A descriptive observational study was conducted involving healthy participants and individuals with anterior cruciate ligament (ACL) injuries to evaluate the device’s performance. Results: results showed that the electrogoniometer captured knee flexion-extension with a range of up to 90°, compared to 45° typically recorded using conventional systems. The device also demonstrated enhanced sensitivity in detecting variations in tibial translation during gait cycles. Conclusions: this electrogoniometer provides a practical tool for clinical assessment of knee function, enabling real-time monitoring of joint behavior during gait. By capturing functional mobility and stability more accurately than static methods, it may enhance diagnostic precision and support more effective rehabilitation planning in orthopedic settings. Full article

In addressing the limited absolute positioning accuracy of industrial robots, which stems from the discrepancy between the nominal kinematic model and the physical entity, this paper proposes a novel paradigm for online compensation based on data-driven error prediction. The present study utilized a KUKA KR4 R600 robot as the experimental platform to construct a high-fidelity digital twin system capable of real-time synchronization. Within this framework, a new machine learning model, termed the Global Configuration-Error Forest (GCE-Forest), was developed and validated. The fundamental principle of GCE-Forest, based on the Random Forest algorithm, is its offline learning of the complex, highly non-linear mapping from the robot’s six-dimensional joint space configuration to its three-dimensional end-effector Cartesian error space. This facilitates online, feedforward, and predictive compensation for the nominal trajectory during robot operation. Through rigorous comparative experiments, the superiority of the proposed GCE-Forest was established. The final outcomes of dynamic trajectory tracking validation demonstrate that the system, by accurately predicting a mean nominal error of 0.1977 mm, successfully reduced the average spatial positioning error of the end-effector to 0.0845 mm, achieving an accuracy improvement of 57.25%. This research provides comprehensive validation of the method’s robust performance, offering a low-cost, non-invasive, and highly effective solution for significantly enhancing robotic accuracy. Full article

In recent years, the rapid development of moving platforms, especially unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), has promoted their widespread applications in various fields such as precision agriculture and formation flight. In these applications, for accurate real-time kinematic positioning between two moving platforms, receiver autonomous integrity monitoring (RAIM) is necessary to assure the reliability of the obtained relative positioning. However, the existing carrier phase-based RAIM (CRAIM) algorithms are mainly a direct extension of pseudorange-based RAIM (PRAIM), whose availability is also a major challenge in signal-harsh environments. Learning from the integrated system between Global Navigation Satellite System (GNSS) and INS and based on a multiple hypothesis solution separation (MHSS) algorithm, we have developed an improved CRAIM algorithm, which combines Beidou Navigation Satellite System (BDS) and INS to offer integrity information for real-time kinematic relative positioning between two moving platforms in challenging environments. To achieve more robust and efficient fault detection and exclusion (FDE) results, an algorithm of observation-domain outlier detection combined with MHSS (OOD-MHSS) is also proposed. In this algorithm, the kinematic relative positioning method with INS addition is performed first, then, based on double-difference (DD) phase observations with known integer ambiguities and the OOD-MHSS method, the integrity monitoring information can be provided for the kinematic relative positioning between two moving platforms. To assess the performance of the OOD-MHSS and the improved CRAIM algorithm, a series of kinematic experiments between different platforms was analyzed and discussed. The results show that the improved CRAIM algorithm can perform effective FDE and provide reliable integrity information, which offers centimeter-level relative position solutions with decimeter-level protection levels (PLs) (integrity budget: ${1\times 10}^{-5}$/h). Both observation outlier detection and INS improve the continuity and availability of kinematic relative positioning and the PLs in horizontal and vertical directions. The PL values have been improved by up to 24.3%, and availability has reached 96.67% in harsh urban areas. This is of great significance for applications requiring higher precision and integrity in kinematic relative positioning. Full article

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

Adhesive capsulitis is a painful and progressive condition marked by significant limitations in shoulder mobility, particularly affecting external rotation. Although magnetic resonance imaging is regarded as the reference standard for assessing intra-articular structures, its high cost and limited availability present challenges in routine clinical use. In contrast, musculoskeletal ultrasound has emerged as an accessible, real-time, and cost-effective imaging modality for both the diagnosis and treatment guidance of adhesive capsulitis. This narrative review compiles and illustrates current evidence regarding the role of ultrasound, encompassing static B-mode imaging, dynamic motion analysis, contrast-enhanced techniques, and sonoelastography. Key sonographic features—such as thickening of the coracohumeral ligament, fibrosis in the axillary recess, and abnormal tendon kinematics—have been consistently associated with adhesive capsulitis and demonstrate favorable diagnostic performance. Advanced methods like contrast-enhanced ultrasound and elastography provide additional functional insights (enabling evaluation of capsular stiffness and vascular changes) which may aid in disease staging and prediction of treatment response. Despite these advantages, the clinical utility of ultrasound remains subject to operator expertise and technical variability. Limited visualization of intra-articular structures and the absence of standardized scanning protocols continue to pose challenges. Nevertheless, ongoing advances in its technology and utility standardization hold promise for the broader application of ultrasound in clinical practice. With continued research and validation, ultrasound is positioned to play an increasingly central role in the comprehensive assessment and management of adhesive capsulitis. Full article

Multiple-reference-station-based real-time kinematics (MR-RTK) is an advanced RTK technique that leverages global navigation satellite system (GNSS) measurements from multiple reference stations and their known baselines. This study investigates the fault detection capabilities of MR-RTK by employing additional measurements from continuously operating reference stations (CORSs) to evaluate the probability of missed detection. The proposed method was validated using test data from a ground rover and a few CORSs within a 10 km radius. The test results show that the missed detection probability decreased by up to 55.0% as the number of reference stations increased up to four. Full article

This article presents the design, implementation, and validation of a Remotely Piloted Safety Cone (RPSC), an autonomous robotic system developed to enhance safety and operational efficiency in road maintenance. The RPSC addresses challenges associated with road works, including workers’ exposure to traffic hazards and inefficiencies of traditional traffic cones, such as manual placement and retrieval, limited visibility in low-light conditions, and inability to adapt to dynamic changes in work zones. In contrast, the RPSC offers autonomous mobility, advanced visual signalling, and real-time communication capabilities, significantly improving safety and operational flexibility during maintenance tasks. The RPSC integrates sensor fusion, combining Global Navigation Satellite System (GNSS) with Real-Time Kinematic (RTK) for precise positioning, Inertial Measurement Unit (IMU) and encoders for accurate odometry, and obstacle detection sensors within an optimised navigation framework using Robot Operating System (ROS2) and Micro Air Vehicle Link (MAVLink) protocols. Complying with European regulations, the RPSC ensures structural integrity, visibility, stability, and regulatory compliance. Safety features include emergency stop capabilities, visual alarms, autonomous safety routines, and edge computing for rapid responsiveness. Field tests validated positioning accuracy below 30 cm, route deviations under 15 cm, and obstacle detection up to 4 m, significantly improved by Kalman filtering, aligning with digitalisation, sustainability, and occupational risk prevention objectives. Full article

With the availability of multi-frequency, multi-constellation global navigation satellite system (GNSS) modules, precise transportation applications have become attainable. For transportation applications, GNSS geodetic-grade receivers can achieve an accuracy of a few centimeters to a few decimeters through differential, precise point positioning (PPP), real-time kinematic (RTK), and PPP-RTK solutions in both post-processing and real-time modes; however, these receivers are costly. Therefore, this research aims to assess the accuracy of a cost-effective multi-GNSS real-time PPP solution for transportation applications. For this purpose, the U-blox ZED-F9P module is utilized to collect dual-frequency multi-GNSS observations through a moving vehicle in a suburban area in New Aswan City, Egypt; thereafter, datasets involving different multi-GNSS combination scenarios are processed, including GPS, GPS/GLONASS, GPS/Galileo, and GPS/GLONASS/Galileo, using both RT-PPP and RTK solutions. For the RT-PPP solution, the satellite clock and orbit correction products from Bundesamt für Kartographie und Geodäsie (BKG), Centre National d’Etudes Spatiales (CNES), and the GNSS research center of Wuhan University (WHU) are applied to account for the real-time mode. Moreover, GNSS datasets from two geodetic-grade Trimble R4s receivers are collected; hence, the datasets are processed using the traditional kinematic differential solution to provide a reference solution. The results indicate that this cost-effective multi-GNSS RT-PPP solution can attain positioning accuracy within 1–3 dm, and is thus suitable for a variety of transportation applications, including intelligent transportation system (ITS), self-driving cars, and automobile navigation applications. Full article

Since 2020, the BDS-3 has been providing real-time corrections via the B2b signal, enabling users in China and its neighboring regions to achieve kinematic positioning accuracy at the decimeter level. The rapid geometric changes of Low-Earth-Orbit (LEO) satellites facilitate the rapid resolution of phase ambiguities and accelerate the convergence of Precise Point Positioning (PPP). Therefore, this study proposes an LEO-enhanced BDS-3 PPP-B2b positioning model. Firstly, a novel BDS-3 PPP model accounting for satellite clock bias characteristics is proposed, and experimental validation confirms its efficacy. Subsequently, an LEO-enhanced BDS-3 PPP model is developed. Finally, the positioning performance is rigorously evaluated using combined LEO simulation observations and BDS-3 observations. The results indicate that, compared with the traditional PPP model, the new model yields an average convergence time of 25.1 min for experiments where the convergence criterion is jointly satisfied, representing a 35.6% improvement in convergence speed, while maintaining the same positioning accuracy after convergence. When augmented with LEO satellites, the convergence time of the BDS-3 PPP-B2b solution is reduced to less than 2 min. Furthermore, when more than three LEO satellites are available, the mean convergence time is shortened to within 1 min. Full article

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

Precise point positioning–real-time kinematic (PPP-RTK) enables users to achieve rapid centimeter-level absolute positioning accuracy within a few epochs. The interpolation of ionospheric delay corrections at the user end, extracted from reference stations, constitutes a key aspect of the process, which depends not solely on the precision of the interpolation model. This study investigates the recommended number of selected reference stations and proposes a method to mitigate the potential loss of observations due to missing ionospheric corrections. According to the experimental results, the number of reference stations should be determined based on the reference network size. Under normal conditions (terrain is relatively flat and the atmospheric conditions are inactive) where reference stations are approximately evenly distributed in all directions, and using low-order surface interpolation model, for networks with 50 km spacing, four or five reference stations are recommended, while for 100 km networks, six or seven stations are enough to calculate precise corrections. Adding more stations beyond these thresholds provides limited improvement in interpolation accuracy and increases the communication load. In addition, an interpolation basis recovery algorithm is proposed to preserve otherwise excluded satellite observations through intelligent handling of correction data gaps at individual reference stations. Experimental validation demonstrates that the recovered ionospheric delay corrections obtained through the algorithm deviate from the ground-truth interpolated values of no more than ±1 cm, an accuracy level deemed adequate for PPP-RTK applications. Furthermore, approximately 3% of the observations, which would otherwise have been discarded due to the missing corrections from a specific reference station, are retained by the algorithm. Full article

Ensuring Global Navigation Satellite System (GNSS) integrity, which provides operational reliability via fault detection, is important for safety-critical applications using high-precision techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK). Ionospheric errors, from atmospheric free electrons, challenge this integrity by introducing variable uncertainties into positioning solutions. This study investigates how ionospheric error modeling spatial resolution impacts protection level (PL) calculations, a metric defining positioning error bounds with high confidence. A comparative evaluation was conducted in low-latitude (Guangdong) and mid-latitude (Shandong) regions, contrasting large-scale with small-scale grid-based ionospheric models from regional GNSS networks. Experimental results show small-scale grids improve characterization of localized ionospheric variability, reducing ionospheric residual standard deviation by approximately 30% and enhancing PL precision. Large-scale grids show limitations, especially in active low-latitude conditions, leading to conservative PLs that reduce system availability and increase missed fault detection risks. A user-side PL computation framework incorporating this high-resolution ionospheric residual uncertainty improved system availability to 94.7% and lowered misleading and hazardous outcomes by over 80%. This research indicates that refined, high-resolution ionospheric modeling improves operational reliability and safety for high-integrity GNSS applications, particularly under diverse and challenging ionospheric conditions. Full article

In the practical application of Real-Time Precise Point Positioning (RT-PPP), the outages in receiving spatial state representation (SSR) information due to communication anomalies can result in a decrease or even divergence of the positioning accuracy of RT-PPP. To mitigate the decline in positioning accuracy, we propose a method of INS aiding RT-PPP based on an optimized stochastic model. First, the correlation between SISRE and SSR age was analyzed by using a dataset of 1800 continuous time series. A new stochastic model called clock–orbit degradation (COD) stochastic model was established to match clock–orbit time-varying statistical characteristics. Second, we introduced Inertial Navigation System (INS) enhancement information to optimize the functional model, leveraging its autonomy and high-precision short-term position constraints. Finally, the real-world static and kinematic experiments were designed to verify the proposed method. The static results showed that the RT-PPP positioning accuracy with COD stochastic model is always higher than the traditional fixed equivalent-weight stochastic model at different level SSR outages. Even with SSR interruptions, the positioning accuracy can reach 0.131 m in the horizontal direction and 0.269 m in the 3D direction, representing improvements of 23.2% and 19.0%, respectively. Furthermore, the kinematic results showed that the positioning accuracy of PPP/INS with COD stochastic model had improved by 38.7% in the horizontal direction and 69.9% in the 3D direction at half an hour of SSR age. Full article

The use of spherical cameras for mapping purposes is a common application in surveying. Very expensive and high-quality cameras are used for surveying purposes and are supplemented by systems for determining their position. Cheap cameras, in most cases, only complement laser scanners, and the images are then used to color the laser point cloud. This article investigates the use of action cameras in combination with low-cost GNSS (Global Navigation Satellite System) equipment. The research involves the development of a methodology and software for georeferencing spherical images, created by the kinematic method, using GNSS RTK (Real-Time Kinematics) or PPK (Post-Processing Kinematics) coordinates. Testing was carried out in two case studies where the environment surveyed had varying properties. Considering that the images from the low-cost 360 camera are of lower quality, an artificial intelligence tool was used to improve the quality of the images. The point clouds from a low-cost device are compared with more accurate methods. One of them is the SLAM (Simultaneous Localization and Mapping) method with the Faro Orbis device. The results in this work show sufficient accuracy and data quality for mapping purposes. Due to the very low price of the low-cost device used in this work, it is very easy to extend this method to practice. Full article