Search Results (123)

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

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

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

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

This paper proposes an analytical framework for pre-analyzing the potential performance of online sensor calibration in Kalman filtering. Taking a multi-sensor integrated kinematic positioning and navigation system as an example, a pre-analysis of the system performance can be conducted: the observability of individual sensor systematic error states; minimum estimable values of sensor systematic error states; and minimum detectable systematic errors in sensor observations. These measures together allow for a rigorous characterization of the potential performance of a system as part of mission planning. The proposed framework enables a thorough evaluation of the relative value of different calibration maneuvers and sensor configurations before data collection by simulating the anticipated trajectory, without even requiring the construction of a physical system. When used with the Generic Multisensor Integration Strategy (GMIS), the proposed framework provides unique insight into the potential performance of IMU sensors. To illustrate the utility of the proposed framework, two situations were analyzed: one where no specific calibration maneuvers were undertaken and one where a circular motion maneuver was undertaken. The results show the potential and practicality of the proposed framework in firmly establishing best practices for field procedures and learning about the system’s capability when using online sensor calibration. Full article

We are working on an innovative approach to outdoor human motion capture, using a wearable device that integrates a low-cost GNSS (Global Navigation Satellite System) receiver and an INS (Inertial Navigation System) via a zero-velocity update (ZUPT) methodology. In this study, we focused on using these devices to reconstruct the foot trajectory. Our work addresses the challenge of capturing precise foot movements in uncontrolled outdoor environments, a task traditionally constrained by the limitations of laboratory settings. We equipped devices that combine inertial measurement units (IMUs) with GNSS receivers in the following configuration: one on each foot and one on the head. We experimented with different GNSS data processing techniques, such as Post-Processed Kinematic (PPK) positioning with and without Moving Base (MB), and after the integration with the IMU, we obtained centimeter-level precision in horizontal and vertical positioning for various walking speeds. This integration leverages a loosely coupled GNSS/INS approach, where the GNSS solution is independently processed and subsequently used to refine the INS outputs. Enhanced by ZUPT and Madgwick filtering, this method significantly improves the trajectory reconstruction accuracy. Indeed, our research includes a study of the impact of moving speed on the performance of these low-cost GNSS receivers. These insights pave the way for future exploration into tightly coupled GNSS/INS integration using low-cost GNSS receivers, promising advancements in fields like sports science, rehabilitation, and well-being. This work seeks not only to contribute to the field of wearable technology, but also to open possibilities for further innovation in affordable, high-accuracy personal navigation and activity monitoring devices. Full article

In this study, we introduce a decentralized architecture aimed at enhancing the efficiency of precise point positioning real-time kinematics (PPP-RTK) in large-scale networks with a common pivot station. Initially, we partition the extensive network into multiple smaller subnetworks (SNs), each with a common pivot station. The augmentation parameters for each SN are then computed using the precise orbit corrections and ionosphere-weighted constraints. However, directly applying the estimated augmentation parameters to users across subnetworks poses challenges due to inter-subnetwork discontinuities. These discontinuities arise from variations in the network configurations and the time correlation of the Kalman filters, despite the use of the same pivot station. To address this, common augmentation parameters, such as the satellite clocks and phase biases from each SN, are integrated into a unified set of parameters and broadcast to users. The aligned common augmentation parameters are then fed back into each SN, and the Kalman filter is re-updated to mitigate the inter-subnetwork discontinuities. The proposed architecture offers a reduced computational burden compared to the centralized PPP-RTK architecture, which handles a full-scale network simultaneously. Unlike previous research on decentralized PPP-RTK, the use of a common pivot station ensures a consistent basis for the common augmentation parameters. This approach enables seamless user positioning during transitions between SNs, eliminating the need to reset the user navigation filter during handover operations and simplifying the integration process. To evaluate the effectiveness of our proposed architecture, we gather dual-frequency global positioning system (GPS) observation data from over 40 continuously observed reference stations (CORSs) in Korea. These data are then partitioned into four SNs, each sharing a common pivot station. Subsequently, we compare the static positioning error and processing time of our proposed architecture with those of the centralized architecture. Additionally, the mitigation performance of the inter-network discontinuities is shown. Full article

With the rapid advancement of artificial intelligence, particularly in fields such as autonomous driving, drone delivery, and logistics automation, the demand for high-precision and robust navigation services has become critical. In complex and dynamic urban environments, the navigation capabilities of single-sensor systems struggle to meet the practical requirements of autonomous driving technology. To address this issue, we propose a multi-source fusion navigation algorithm tailored for dynamic urban canyon scenarios, aiming to achieve reliable and continuous state estimation in complex environments. In our proposed method, we utilize independent threads on a graphics processing unit (GPU) to perform real-time detection and removal of dynamic objects in visual images, thereby enhancing the visual accuracy of multi-source fusion navigation in dynamic scenes. To tackle the challenges of significant Global Navigation Satellite System (GNSS) positioning errors and limited satellite availability in urban canyon environments, we introduce a specialized GNSS Real-Time Kinematic (RTK) stochastic model for such settings. The navigation performance of the proposed algorithm was evaluated using public datasets. The results demonstrate that our RTK/INS/Vision integrated navigation algorithm effectively improves both accuracy and availability in dynamic urban canyon environments. Full article

Performing long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-attention mechanisms with long short-term memory (SALSTM) to enhance GNSS-denied navigation performance. The estimation task of GNSS-denied navigation is first modeled based on UAV aerodynamics and kinematics, enabling a precise definition of the inputs and outputs that SALSTM needs to map. A self-attention layer is inserted in multiple LSTM layers to capture long-range dependencies in subtle dynamic changes. The output layer is designed to generate state sequences, leveraging the recursive nature of LSTM to enforce state continuity constraints. The outputs of SALSTM are fused to enhance integrated navigation within an extended Kalman filter framework. The performance of the proposed method is evaluated using flight data obtained from field tests. The results demonstrate that SALSTM-enhanced integrated navigation achieves superior long-term stability and improves velocity and position estimation accuracy by more than 50% compared to the best existing methods. Full article

Achieving high-precision spatiotemporal synchronization is crucial for the implementation of virtual coupling (VC) in railway systems. This paper proposes a moving baseline real-time kinematic (MB-RTK) framework to enhance relative positioning accuracy and synchronization robustness between coupled trains. By leveraging global navigation satellite system (GNSS) carrier-phase differential processing and dynamic baseline estimation, MB-RTK effectively mitigates positioning errors caused by GNSS signal degradation, multipath interference, and synchronization latency, ensuring stable and reliable inter-train coordination. The proposed framework was evaluated through comprehensive simulations and field experiments. The results demonstrate that MB-RTK achieves centimeter-level relative positioning accuracy under normal GNSS conditions, maintains tracking errors within 10 m, and typically keeps velocity synchronization deviations within ±0.5 km/h. Furthermore, the RTK status analysis reveals that NARROW_INT provides the highest stability, while continuous RTK corrections are essential to ensure seamless synchronization in dynamic environments. To further enhance synchronization performance, a decentralized distributed synchronization algorithm was introduced, reducing communication overhead and improving real-time responsiveness. The proposed approach exhibits strong resilience to GNSS disruptions, making it well-suited for high-density and autonomous train operations. Overall, this study highlights MB-RTK as a promising solution for VC applications, offering high accuracy, low latency, and strong adaptability in complex railway scenarios. Future research will focus on AI-driven dynamic corrections, integration with complementary localization methods, and large-scale deployment strategies to further optimize the system’s robustness and scalability. Full article

Unmanned surface vehicles (USVs) are increasingly critical in modern maritime operations, where reliable cooperative formation control under actuator failure is essential for safe navigation and efficient mission execution. Thus, this study presents an innovative fault-tolerant control strategy for USV formations, specifically addressing the challenges posed by actuator degradation, compound uncertainties, and input saturation. Concretely, the main contribution of this study is as follows. First, a detailed analysis of the USV kinematics and dynamics is conducted, and a novel position constraint model is developed through a formation transformation approach. To mitigate internal and external disturbances, a new non-singular terminal sliding mode surface is designed in conjunction with a dynamically regulated convergence law, ensuring finite-time convergence while reducing chattering. An adaptive terminal sliding mode controller is then formulated, integrating an event-triggered mechanism and an RBF neural network to compensate for model uncertainties and input saturation effects. Simulation results demonstrate that the proposed method not only achieves robust cooperative formation control under partial actuator failure but also significantly enhances the tracking accuracy and reduces the communication load compared to conventional sliding mode approaches. Full article

Based on the characteristics of small agricultural machinery in terms of flexibility and high efficiency when operating in small plots of hilly and mountainous areas, as well as the demand for improving the automation and intelligence levels of agricultural machinery, this paper conducted research on the path tracking control of the automatic navigation operation of a crawler sprayer. Based on the principles of the kinematic model and the position prediction model of the agricultural machinery chassis, a pure pursuit controller based on adaptive look-ahead distance was designed for the tracked motion chassis. Using a lightweight crawler sprayer as the research platform, integrating onboard industrial control computers, sensors, communication modules, and other hardware, an automatic navigation operation system was constructed, achieving precise control of the crawler sprayer during the path tracking process. Simulation test results show that the path tracking control method based on adaptive look-ahead distance has the characteristics of smooth control and small steady-state error. Field tests indicate that the crawler sprayer exhibits small deviations during path tracking, with an average absolute error of 2.15 cm and a maximum deviation of 4.08 cm when operating at a speed of 0.7 m/s. In the line-following test, with initial position deviations of 0.5 m, 1.0 m, and 1.5 m, the line-following times were 7.45 s, 11.91 s, and 13.66 s, respectively, and the line-following distances were 5.21 m, 8.34 m, and 9.56 m, respectively. The maximum overshoot values were 6.4%, 10.5%, and 12.6%, respectively. The autonomous navigation experiments showed a maximum deviation of 5.78 cm and a mean absolute error of 2.69 cm. The proportion of path deviations within ±5 cm and ±10 cm was 97.32% and 100%, respectively, confirming the feasibility of the proposed path tracking control method. This significantly enhanced the path tracking performance of the crawler sprayer while meeting the requirements for autonomous plant protection spraying operations. Full article

To achieve high-precision positioning using the global navigation satellite system (GNSS), the onboard GNSS receiver integrates real-time kinematic (RTK) technology to enable centimeter-level positioning accuracy. Furthermore, array anti-interference technology is incorporated into the RTK receiver to enhance positioning reliability and mitigate interference in complex environments such as urban areas or regions with high electromagnetic activity. However, this approach can introduce signal distortion, which adversely affects the convergence of RTK positioning. To address the issue of bias introduced by interference suppression in RTK positioning, this paper focuses on error modeling and bias compensation through a phase bias compensation algorithm. A novel phase compensation algorithm is proposed, leveraging the anti-interference weighting coefficients of array elements and the anti-interference output signal. Compared to the conventional minimum variance distortionless response (MVDR) algorithm, the proposed method features a simpler architecture and achieves phase compensation at a lower computational cost using the power inverse (PI) algorithm. Simulation experiments demonstrate the effectiveness of the compensation method, achieving a mean phase bias of approximately 0.25 degrees and a variance of 4.62 degrees. This level of accuracy makes it highly suitable for UAVs operating in challenging environments where precision and reliability are paramount. Full article

Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the İstanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs. Full article