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Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment. NEW & NOTEWORTHY: In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems have enabled significant improvements in aeronautical navigation. However, in recent years, a growing number of interference events have been reported by flight crews. In this paper, we first identify such events using crowd-sourced surveillance data collected between February and December 2022 for three different regions: the Baltic states, eastern Europe bordering the Black Sea, and the eastern Mediterranean. Then, we assess the extent and duration of these events to determine their impact on civil aviation. The analysis shows different characteristics, ranging from isolated events to regular large-scale and recurrent disruptions. Next, we identify aircraft types for the affected flights and evaluate flight plan data with respect to navigation equipment in order to identify flights that rely solely on satellite navigation and that might require assistance in the case of a loss of satellite navigation. Finally, we show the impact of radio frequency interference (RFI) on a selected passenger flight by analyzing automatic dependent surveillance-broadcast data as well as avionics data obtained from the airline's flight data monitoring department for that specific flight and link the observations to the warnings triggered by the aircraft to alert the flight crew while encountering RFI. [ABSTRACT FROM AUTHOR]

This study investigated the navigation problem for cellular-connected unmanned aerial vehicles (UAVs), particularly in highly dynamic urban environments. To address this problem, the UAV is required not only to evade high-speed obstacles in the airspace but also to avoid the coverage holes of cellular base stations (BS). Moreover, the UAV needs to reach the destination to complete the navigation task. Hence, it is imperative to design the trade-off in action selections between collision evasion and destination-approaching scenarios, while also considering the expected communication outage duration as a crucial reference. To overcome this multiobjective optimization challenge, we propose a deep reinforcement learning (DRL)-based algorithm aimed at enabling the UAV to acquire an optimal decision-making policy. Specifically, we formulated the navigation problem as a Markov decision process (MDP) and developed a layered recurrent soft actor–critic (RSAC)-based DRL framework, stimulating the UAV to resolve two fundamental subtasks of UAV navigation. Furthermore, we develop a multilayer perception (MLP)-based integrated evaluation network to select a particular action from the two subsolutions, satisfying the demands for the entire navigation problem. The layered architecture simplifies the navigation problem, thereby enhancing the convergence speed of the proposed algorithm. Numerical results indicate that the layered-RSAC-based UAV can autonomously perform scheduled navigation tasks in our designed simulated urban environments with superior effectiveness. [ABSTRACT FROM AUTHOR]

Traditional approaches to safety assessment and management are limited when it comes to complex socio-technical systems, such as air traffic management (ATM). A modelling approach proposed in this paper relies on Resilience Engineering perspective, which is about work-as-done, where safety is one of the targets trading off with other goals. The model provides a description of everyday practices, procedures, strategies, etc. in providing, not only safe, but also efficient ATM system operations. The aim of the model is to help air navigation service providers (ANSPs) that still do not measure and monitor resilience to distinguish between main drivers of traditional safety and drivers of resilience emerging from everyday adaptation to uncertainties. The conceptual layer of the model uses the analogy of lever mechanism to represent the balancing between the demand side (including the uncertainties) and available resources on the supply side that considers air traffic controllers (ATCos), tools and working procedures. The quantitative layer of the model allows identification of periods of resilient behavior (negative demand-capacity imbalances) and thereby steers ANSPs in discovering sources of resilience and finding appropriate resilience indicators to monitor in the future. A data-driven application of the proposed model is shown with the example of Barcelona Area Control Centre – assessment of balancing conditions, analysis of proposed resilience indicators and discussion of the trade-offs. This research focuses on the tactical imbalances that emerge from work-as-done on an everyday basis. [ABSTRACT FROM AUTHOR]

The present study shows the development of computational algorithms to represent aircraft instruments such as the attitude indicator or the turn-and-slip indicator; moreover, the algorithms represent a magnetic compass and other instruments that function according to other physical theories. These instruments work by using the gyroscope and magnetic principles and help the pilot in navigation. These are considered to be the basic instruments required to provide location-related and positional information about the actual aircraft attitude. The algorithms developed in this study are capable of working in concordance with other instruments and the physical conditions established. The programming language used was C++ and the algorithms were compiled in independent files and subroutines for computational efficiency, eliminating unnecessary code. The display options were successfully tested. Additionally, an analysis that evaluated the error and approached flight simulation as a function of step time (Δt) is also described. [ABSTRACT FROM AUTHOR]

Safe Micro Aerial Vehicle (MAV) navigation requires detecting and avoiding obstacles. For safe MAV navigation, expansion‐based algorithms are effective for detecting obstacles. However, accurate and real‐time obstacle detection is a fundamental challenge. Some traditional methods focus on extracting geometric features from images and applying geometric constraints to identify potential obstacles. Others may leverage machine learning algorithms for object detection and classification, using features such as texture, shape, and context to distinguish obstacles from background clutter. The choice of approach depends on factors such as the specific requirements of the application, the complexity of the scene, and the available computational resources. Since obstacles, in reality, take the form of objects (e.g., persons, walls, pillars, trees, automobiles, and other structures), it is preferable to represent them according to human comprehension and as objects. Therefore, the objective of this study is to reflect on the previous research and address the issues mentioned above by extracting objects from the fisheye image using a panoptic deep‐learning network. The extracted object regions are, then, used to identify obstacles with a novel area‐based expansion rate we developed in a previous study. We compared the accuracy of obstacle detection in our proposed method to the existing method when moving forward and to the right; thus, we improved it between 10% and 18%, respectively. In addition, compared with the existing method, and due to replacing a single object with multiple regions, obstacle‐detection runtime for forward and right direction is 15.71 and 25.5 times faster, respectively, and the required match points have decreased by 49% and 55%. [ABSTRACT FROM AUTHOR]

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×106) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads. [ABSTRACT FROM AUTHOR]

Navigation is an essential capability for autonomous robots. In particular, visual navigation has been a major research topic in robotics because cameras are lightweight, power-efficient sensors that provide rich information on the environment. However, the main challenge of visual navigation is that it requires substantial computational power and memory for visual processing and storage of the results. As of yet, this has precluded its use on small, extremely resource-constrained robots such as lightweight drones. Inspired by the parsimony of natural intelligence, we propose an insect-inspired approach toward visual navigation that is specifically aimed at extremely resource-restricted robots. It is a route-following approach in which a robot's outbound trajectory is stored as a collection of highly compressed panoramic images together with their spatial relationships as measured with odometry. During the inbound journey, the robot uses a combination of odometry and visual homing to return to the stored locations, with visual homing preventing the buildup of odometric drift. A main advancement of the proposed strategy is that the number of stored compressed images is minimized by spacing them apart as far as the accuracy of odometry allows. To demonstrate the suitability for small systems, we implemented the strategy on a tiny 56-gram drone. The drone could successfully follow routes up to 100 meters with a trajectory representation that consumed less than 20 bytes per meter. The presented method forms a substantial step toward the autonomous visual navigation of tiny robots, facilitating their more widespread application. Editor's summary: For navigation, robots often rely either on external infrastructure or on mapping-based navigation algorithms that are generally associated with high computational demands. van Dijk et al. have now developed an insect-inspired approach for visual navigation of resource-constrained miniature drones by exploiting both visual homing and odometry. The framework was tested on a 56-g drone, which was shown to be capable of following routes in indoor environments over long distances with minimal memory requirements. This approach could substantially improve the autonomous navigation of aerial robots with constraints in computational power, energy demands, and weight. —Amos Matsiko [ABSTRACT FROM AUTHOR]

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable challenge emerges due to the inherent limitations of GPS signals within indoor environments, posing a threat to the accuracy of drone positioning. This limitation not only jeopardizes testing validity but also introduces instability and inaccuracies, compromising the assessment of drone performance. Given the pivotal role of precise GPS-derived data in drone autopilots, addressing this indoor-based GPS constraint is imperative to ensure the reliability and resilience of unmanned aerial vehicles (UAVs). This paper delves into the implementation of an Indoor Positioning System (IPS) leveraging computer vision. The proposed system endeavors to detect and localize UAVs within indoor environments through an enhanced vision-based triangulation approach. A comparative analysis with alternative positioning methodologies is undertaken to ascertain the efficacy of the proposed system. The results obtained showcase the efficiency and precision of the designed system in detecting and localizing various types of UAVs, underscoring its potential to advance the field of indoor drone navigation and testing. [ABSTRACT FROM AUTHOR]

With the intensification of ocean exploration and development in recent years, the navigation equipment in the marine environment has become increasingly diversified, making the marine environment more complex. Traditional methods for underwater target recognition are gradually becoming less applicable and unable to achieve better results. With the application of deep learning in underwater target recognition, the robustness of deep learning models used for underwater target recognition is crucial due to the significant environmental interference in underwater target data and the susceptibility of deep learning models to adversarial samples. This paper proposes an error sample feature compensation method for improving the robustness of deep learning models for underwater target recognition, focusing on the problem of the significant impact of sample data quality on the robustness of deep learning models for underwater target recognition. The method innovatively divides error samples into difficult-to-improve samples and easy-to-improve samples and proposes an adversarial training method combined with classification conditions. At the same time, the method uses a weighted index of model accuracy to combine adversarial training models with feature compensation methods, further improving the robustness of deep learning models for underwater target recognition tasks. Finally, the method is validated on an underwater dataset, and the results show that the proposed method improves the robustness of deep learning models used for underwater target recognition. [ABSTRACT FROM AUTHOR]

Rising levels of risk as cyber‐attackers look to exploit system vulnerabilities threatens the Air Traffic Control industry. Attacks on Air Navigation Service Providers' communications systems may lead to airspace closure and even cause safety issues. This paper presents a novel Model‐Based Systems Engineering method that enables systems engineers, in collaboration with system security and software engineers, to perform threat‐modeling analysis of cyber‐physical systems early in the system development process and incorporate mitigation strategies into the system design. The proposed model‐based method covers few security concepts, including misuse cases, system assets, threats, risks, vulnerabilities, and security control identification. The study found that the proposed method is suitable for conducting security analysis for complex cyber‐physical systems early in the system development process. [ABSTRACT FROM AUTHOR]

Accurate localization of unmanned aerial vehicle (UAV) is critical for navigation in GPS-denied regions, which remains a highly challenging topic in recent research. This article describes a novel approach to multi-sensor hybrid coupled cooperative localization network (HCCNet) system that combines multiple types of sensors including camera, ultra-wideband (UWB), and inertial measurement unit (IMU) to address this challenge. The camera and IMU can automatically determine the position of UAV based on the perception of surrounding environments and their own measurement data. The UWB node and the UWB wireless sensor network (WSN) in indoor environments jointly determine the global position of UAV, and the proposed dynamic random sample consensus (D-RANSAC) algorithm can optimize UWB localization accuracy. To fully exploit UWB localization results, we provide an HCCNet system which combines the local pose estimator of visual inertial odometry (VIO) system with global constraints from UWB localization results. Experimental results show that the proposed D-RANSAC algorithm can achieve better accuracy than other UWB-based algorithms. The effectiveness of the proposed HCCNet method is verified by a mobile robot in real world and some simulation experiments in indoor environments. [ABSTRACT FROM AUTHOR]

The collaboration among swarmed aircraft can provide additional observation to improve the integrity of its onboard navigation systems. But due to the limitation on relative communicating and measuring burdens, adopting all the observations between aircraft in the swarm is inefficient. The geometry of the collaborated partners is a key factor that influences the effectiveness of the navigation integrity augmentation, which needs to be optimized in collaborative integrity augmented navigation. In this paper, the integrity augmented navigation method for aerial swarm based on collaborative partner optimization is proposed. The geometry constructed by the GNSS satellites and the cooperative partners in the aerial swarm are analyzed dynamically, and the augmented integrity protection levels with different collaborative relationships are predicted to distinguish the partner essential for navigation integrity augmentation. Then the collaborative partner makes a key contribution to integrity augmentation adopted in collaborative navigation integrity monitoring, improving the efficiency of the collaborative navigation. The simulation results indicate the effectiveness of the proposed cooperative partnership optimization strategy, as well as the superiority of the proposed method compared with the traditional independent integrity framework in improving the integrity protection level and fault detection capacity. [ABSTRACT FROM AUTHOR]

This work presents a new compactly printed ultrawideband antenna for mobile satellites (8.025 GHz), metrological aids (9.5 GHz), and aeronautical radio navigation (9-9.2 GHz) applications. The proposed antenna is made up of four straightforward radiating rectangular-shaped strips, the overall geometry of which resembles two U-shaped in the radiating patch, fed by a 50O microstrip feed line. Two exponential patches, along with a rectangular patch, are implemented in the ground part of the structure, which can be effectively constructed by varying the sizes and geometries of these two U-shaped strips. The proposed antenna is compact overall. The proposed antenna is designed, simulated, fabricated, and tested minutely. The measured values of 10 dB return loss absolute/fractional BW of 10 GHz/142 % at the center frequency, minimum/maximum gain of 2/6.1 dBi, and maximum/minimum radiation efficiency of 92/70 % are achieved from the 2 to 12 GHz frequency range. These bandwidths are also well suited for 2.5/3.5/5.5-GHz WiMAX bands and 2.4/5.2/5.8-GHz WLAN bands. In addition, a comparison table is provided to prove the state-of-the-art design of the antenna. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) are widely used in literature for object detection utilizing convolutional neural networks (CNN). However, most UAVs make use of GNSS sensors for localization, which have low reception in indoor situations. Therefore, this study aimed to investigate the implementation of a multi-UAV object detection system and navigation with the aid of particle swarm optimization (PSO) in ultra-wideband (UWB) positioning systems for GNSSdenied environments, such as inside factories and warehouses. The performance of UWB systems was investigated to determine its viability in the PSO model. An object detection system based on the YOLOv5 network was trained with custom training images and subsequently evaluated with test images. The results of the object detection network were fed as inputs into PSO algorithms. Furthermore, different PSO algorithms were evaluated to determine the suitability for multi-UAV navigation and object detection. The results showed that UWB systems had sufficient accuracy for indoor localization, object detection, and navigation applications. YOLOv5 detection model detected objects with an F1 score of 0.93, given the optimal threshold of 0.8. Regarding the evaluation of PSO algorithms, the stochastic inertia weight variant of PSO algorithms (Sto-IW PSO) performed effectively across all metrics considered in the study compared to other algorithms that only performed effectively in one. Recommendations included the actual implementation of the system with multiple UAVs through field experiments and further refinements to PSO algorithms in order to match the kinematics and response time of the UAVs. [ABSTRACT FROM AUTHOR]

Timely identification of a fire's origin in its initial stages is essential for minimizing human as well as material losses. Hence, formulating a methodology and algorithm for autonomously planning the flight path of an unmanned aerial vehicle (UAV) during fire monitoring to identify ignition sources early is a pressing objective. The proposed ignition detection method comprises three flight plans (FPs); Flight Plan A (FPA) involves surveying the monitored area with tacks, and assessing harmful substance concentrations in each pixel. Upon identifying a pixel surpassing the threshold concentration, Flight Plan B (FPB) directs the UAV control for targeted navigation, employing local planning based on calculating local differential operators within a nine-element mask. FPB enables the UAV to directly reach and pinpoint the fire source coordinates. Flight Plan C (FPC) outlines the UAV's return journey to the departure point from any pixel within the monitoring zone. A multi-criteria algorithm for UAV flight path control has been devised, facilitating the determination of local target pixels and subsequent Local Flight Plan (LFP) construction. The guiding principle for constructing an LFP is the 'at least three pixels on the tack' rule, yielding a nine-element matrix with known harmful substance concentrations in the target pixel. It enables the identification of pixels within the LFP where obtaining this matrix is unattainable. Mathematical modelling of the UAV flight control algorithm, as per the proposed methodology, was executed using MATLAB® R2019b, demonstrating control stability and accelerated attainment of ignition source pixel coordinates. The achieved speed surpassed the set goal by 1.5 to 2 times, contingent on the ignition source's location concerning the monitored territory's flyby direction. [ABSTRACT FROM AUTHOR]

In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community. [ABSTRACT FROM AUTHOR]

Feature extraction and matching play integral roles in autonomous vision navigation. As a typical morphological feature, craters can be used as navigation landmarks. However, the spin of small bodies introduces substantial variations in illumination and viewing angles within acquired images, thereby posing challenges to feature extraction and matching algorithms. Addressing these challenges, a deep learning-based approach emerges as pivotal in mitigating the limitations of existing navigation algorithms. This paper presents an algorithm designed to segment noteworthy craters and irregular regions on such small bodies, thereby furnishing crucial autonomous navigation data for missions involving uncharted terrains. By seamlessly integrating deep learning techniques with conventional computer vision-based feature description methods, the algorithm achieves matching segmentation results of noteworthy precision. The experimental results show that the algorithm has better results compared to the existing mainstream segmentation networks. And it can achieve the accurate matching of segmentation results. • Accurate segmentation of small-body craters via deep-learning Algorithms. • Gradient descriptor construction method based on segmentation results. • The proposed method has viewpoint, illumination, scale, and rotation invariance. [ABSTRACT FROM AUTHOR]

Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios. [ABSTRACT FROM AUTHOR]

In global navigation satellite systems (GNSS)-denied environments, vision-based methods are commonly used for the positioning and navigation of aerial robots. However, traditional methods often suffer from accumulative estimation errors over time, leading to trajectory drift and lack real-time performance, particularly in large-scale scenarios. This article presents novel approaches, including feature-based cross-view image matching and the integration of visual odometry and photogrammetric space resection for camera pose determination in real-time. Experimental evaluation with real UAV datasets demonstrated that the proposed method reliably matches features in cross-view images with large differences in spatial resolution, coverage, and perspective views, achieving a root-mean-square error of 4.7 m for absolute position error and 0.33° for rotation error, and delivering real-time performance of 12 frames per second (FPS) when implemented in a lightweight edge device onboard UAV. This approach offters potential for diverse intelligent UAV applications in GNSS-denied environments based on real-time feedback control. [ABSTRACT FROM AUTHOR]

In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors, including GPS (global positioning system), an IMU (inertial measurement unit), and visual sensors, to complement the strengths and weaknesses of each hardware component, thereby mitigating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, a high-fidelity UAV model is established in the digital twin based on the real UAV parameters, providing a robust reference for data fusion. By utilizing the extended Kalman filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables the real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multisource, multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency. [ABSTRACT FROM AUTHOR]

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation. [ABSTRACT FROM AUTHOR]

This paper identifies and classifies the essential constraints that must be addressed to allow U-space traffic autonomous guidance. Based on an extensive analysis of the state of the art in robotic guidance, physics of flight, flight safety, communication and navigation, uncrewed aircraft missions, artificial intelligence (AI), social expectations in Europe on drones, etc., we analyzed the existing constraints and the information needs that are of essential importance to address the identified constraints. We compared the identified information needs with the last edition of the U-space Concept of Operations and identified critical gaps between the needs and proposed services. A high-level methodology to identify, measure, and close the gaps is proposed. [ABSTRACT FROM AUTHOR]

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed. [ABSTRACT FROM AUTHOR]

This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case. [ABSTRACT FROM AUTHOR]

The object of this study is the process of constructing a decision function by the optical-electronic system of technical vision under the conditions of the influence of obstacles on the current image, which is formed in the process of localization of a mobile robot. The paper reports the results of solving the problem of constructing a decision function when reducing the signalto-noise ratio of the current image by using a set of informative features for the selection of the binding object, namely, brightness, contrast, and its area. The selection of the binding object is proposed to be carried out by choosing the appropriate values of the quantization thresholds of the current image for the selected informative features, taking into account the signal-tonoise ratio, which provides the necessary probability of object selection. The dependence of the object selection probability on the selected values of the quantization thresholds was established. The use of the results could enable the construction of a unimodal decision function when localizing mobile robots on imaging surfaces with weakly pronounced brightness and contrast characteristics of objects, as well as with their small geometric dimensions. By modeling, the probability of forming a decision function was estimated depending on the degree of noise of the current images. It is shown that the application of the proposed approach allows selection of objects with a probability ranging from 0.78 to 0.99 for the values of the signal-to-noise ratio of images formed by the technical vision system under real conditions. The method to construct a decision function under the influence of interference could be implemented in information processing algorithms used in optical-electronic technical vision systems for the navigation of unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

This manuscript describes the computational process to calculate an airplane path and display it in a 2D and 3D coordinate system on a computer screen. The airplane movement is calculated as a function of its dynamic's conditions according to physical and logical theory. Here, the flight is divided into maneuvers and the aircraft conditions are defined as boundary conditions. Then the aircraft position is calculated using nested loops, which execute the calculation procedure at every step time (Δt). The calculation of the aircraft displacement is obtained as a function of the aircraft speed and heading angles. The simulator was created using the C++ programming language, and each part of the algorithm was compiled independently to reduce the source code, allow easy modification, and improve the programming efficiency. Aerial navigation involves very complex phenomena to be considered for an appropriate representation; moreover, in this manuscript, the influence of the mathematical approach to properly represent the aircraft flight is described in detail. The flight simulator was successfully tested by simulating some basic theoretical flights with different maneuvers, which include stationary position, running along the way, take off, and some movements in the airspace. The maximum aircraft speed tested was 120 km/h, the maximum maneuver time was 12 min, and the space for simulation was assumed to be without obstacles. Here, the geometrical description of path and speed is analyzed according to the symmetric and asymmetric results. Finally, an analysis was conducted to evaluate the approach of the numerical methods used; after that, it was possible to confirm that precision increased as the step time was reduced. According to this analysis, no more than 500 steps are required for a good approach in the calculation of the aircraft displacement. [ABSTRACT FROM AUTHOR]

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship's customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results. [ABSTRACT FROM AUTHOR]

Ring shape microstrip antenna designs supported via an altered ground plane profile are proposed for multi-band response with wider bandwidth in each band. The impedance bandwidth of a single ring patch supported via a slot cut ground plane on a substrate having a thickness of * 0.1λg, is 45.8% and the broadside gain is 5.6 dBi. By employing additional stack patches, dual and triple-band configurations are obtained. In the respective operative frequency bands, maximum 15% of bandwidth is achieved in the suggested antenna, with a maximum broadside gain of more than 5.5 dBi. Through the acquired antenna features, the proposed configuration fulfills the criteria of E-GSM900/Secondary Surveillance Radar/Aeronautical Radio Navigation Applications. [ABSTRACT FROM AUTHOR]

Autonomous drones offer immense potential in dynamic environments, but their navigation systems often struggle with moving obstacles. This paper presents a novel approach for drone trajectory planning in such scenarios, combining the Interactive Multiple Model (IMM) Kalman filter with Proximal Policy Optimization (PPO) reinforcement learning (RL). The IMM Kalman filter addresses state estimation challenges by modeling the potential motion patterns of moving objects. This enables accurate prediction of future object positions, even in uncertain environments. The PPO reinforcement learning algorithm then leverages these predictions to optimize the drone's real-time trajectory. Additionally, the capability of PPO to work with continuous action spaces makes it ideal for the smooth control adjustments required for safe navigation. Our simulation results demonstrate the effectiveness of this combined approach. The drone successfully navigates complex dynamic environments, achieving collision avoidance and goal-oriented behavior. This work highlights the potential of integrating advanced state estimation and reinforcement learning techniques to enhance autonomous drone capabilities in unpredictable settings. [ABSTRACT FROM AUTHOR]

Visual navigation has recently seen significant developments with the rise in autonomous navigation. Keypoint-based mapping and localization has served as a reliable localization method for many applications, but the push to run more applications on less expensive hardware becomes extremely limiting. In this paper, we present a novel approach for visual geolocalization and navigation that improves landmark detection reliability while reducing reference map complexity. Similar to prior techniques, we use the process of point based matching schemes to solve for the image-to-map transform. The critical difference is that we use object detection to identify key-regions instead of keypoints. During an initial flight key-regions are mapped into an identity dictionary with their geolocations and few-shot learning encoded descriptors. Then on subsequent flights, key-regions are detected and matched using the identity dictionary for re-identification. Using the identified vehicles as key-regions, the results show that the proposed key-region based localization produces GPS like localization while maintaining a higher resilience to image noise compared to keypoint-based techniques. [ABSTRACT FROM AUTHOR]

There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems' overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass inaccuracy once the ellipsoid fitting method has corrected manufacturing and installation errors in the digital compass system. Second, the digital compass's direction angle data is utilized to offset the combined navigation system's mistake. The final objective is to create a high-performing portable TMR digital compass system that will enhance the accuracy and stability of the combined navigation system (abbreviated as CNS). According to the experimental results, the digital compass's azimuth accuracy was 4.1824° before error compensation and 0.4580° after it was applied. The combined navigation system's path is now more accurate overall and is closer to the reference route than it was before the digital compass was added. Furthermore, compared to the combined navigation route without the digital compass, the combined navigation route with the digital compass included is more stable while traveling through the tunnel. It is evident that the digital compass system's design can raise the integrated navigation system's accuracy and stability. The integrated navigation system's overall performance may be somewhat enhanced by this approach. [ABSTRACT FROM AUTHOR]

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%. [ABSTRACT FROM AUTHOR]

Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based object detection approach can localize obstacles detected in a scene. Considering this approach, we propose a solution framework that includes masking with a color-based segmentation method to identify an empty area where the drone can fly. The scene is described using segmented regions and localization points. The proposed approach can be used to remotely guide drones in dynamic environments that have poor coverage from global positioning systems. The simulation results show that the proposed framework with object detection and the proposed masking technique support drone navigation in a dynamic environment based only on the visual input from the front field of view. [ABSTRACT FROM AUTHOR]

The existing methods for addressing visual navigation employ deep reinforcement learning as the standard tool for the task. However, they tend to be vulnerable to statistical shifts between the training and test data, resulting in poor generalization over novel environments that are out-of-distribution from the training data. In this study, we attempt to improve the generalization ability by utilizing the inductive biases available for the task. Employing the active neural SLAM that learns policies with the advantage actor-critic method as the base framework, we first point out that the mappings represented by the actor and the critic should satisfy specific symmetries. We then propose a network design for the actor and the critic to inherently attain these symmetries. Specifically, we use G-convolution instead of the standard convolution and insert the semi-global polar pooling layer, which we newly design in this study, in the last section of the critic network. Our method can be integrated into existing methods that utilize intermediate goals and 2D occupancy maps. Experimental results show that our method improves generalization ability by a good margin over visual exploration and object goal navigation, which are two main embodied visual navigation tasks. [ABSTRACT FROM AUTHOR]

The carrier phase received at the receivers of the Global Positioning System (GPS) links is used to detect navigation data and to precisely determine the position, speed and time corresponding to the user's equipment. Therefore, subsystems for carrier phase tracking are crucial parts in all GPS receivers. When the propagation conditions are favorable, the method frequently used for phase tracking is based on Digital Phase-Locked Loop (DPLL)) and implemented through the discrete Costas loop operating under the modulated L1 carrier, in the case of a GPS receiver. This technique is quite simple, well known and very suitable for implementation in low-cost receivers. In this article, we revisit the traditional Costas loop design and point out some issues that affect the phase tracking performance of this loop. In order to overcome these problems, we propose some modifications to the traditional Costas loop. The resulting architecture presents better performance and complexity equivalent to the original loop. Another contribution of this work is the mathematical analysis to evaluate the performance of the new architecture when operating on an Additive White Gaussian Noise (AWGN) channel. Various results from computational simulations carried out with the two architectures, in different operating scenarios, including AWGN, dynamic stress and ionospheric scintillation are presented and discussed. We conclude that the new architecture outperforms the traditional Costas loop in terms of the variance of the estimated phase error, root mean squared error of the estimated phase and robustness to cycle-slip and loss of lock. [ABSTRACT FROM AUTHOR]

The demand for autonomous navigation UAVs in reconnaissance is steadily increasing. One crucial aspect of these missions is the freshness of reconnaissance information, which serves as a vital indicator of mission effectiveness. However, there is a lack of targeted investigation in the research on autonomous single/multi-UAV missions and joint path planning. Furthermore, the use of visual–inertial odometry (VIO) in rotary-wing UAVs can lead to significant positional drift during flight, which may result in the loss of the UAV and mission failure. This paper investigates joint planning problems in single/multi-UAV reconnaissance missions under both GPS-available and GPS-unavailable scenarios and proposes an integrated data collection and beacon-assisted localization approach. Finally, the numerical results demonstrate the effectiveness of the proposed scheme in enhancing the freshness of reconnaissance information. [ABSTRACT FROM AUTHOR]

In this study an innovative parameterized water-bomb wheel modeling method based on recursive solving are introduced, significantly reducing the modeling workload compared to traditional methods. A multi-link supporting structure is designed upon the foundation of the water-bomb wheel model. The effectiveness of the supporting structure is verified through simulations and experiments. For robots equipped with this water-bomb wheel featuring the multi-link support, base on the kinematic model of multi-link structure, a mapping algorithm that incorporates parameterized kinematic solutions and IMU-fused parameterized odometry is proposed. Based on this algorithm, SLAM and autonomous navigation experiments are carried out in simulation environment and real environment respectively. Compared with the traditional algorithm, this algorithm the precision of SLAM is enhanced, achieving high-precision SLAM and autonomous navigation with a robot error rate below 5%. [ABSTRACT FROM AUTHOR]

In the dynamic realm of Unmanned Aerial Vehicles (UAVs), and, more specifically, Quadrotor drones, this study heralds a ground-breaking integrated optimal control methodology that synergizes a distributed framework, predictive control, H-infinity control techniques, and the incorporation of a Kalman filter for enhanced noise reduction. This cutting-edge strategy is ingeniously formulated to bolster the precision of Quadrotor trajectory tracking and provide a robust countermeasure to disturbances. Our comprehensive engineering of the optimal control system places a premium on the accuracy of orbital navigation while steadfastly ensuring UAV stability and diminishing error margins. The integration of the Kalman filter is pivotal in refining the noise filtration process, thereby significantly enhancing the UAV's performance under uncertain conditions. A meticulous examination has disclosed that, within miniature Quadrotors, intrinsic forces are trivial when set against the formidable influence of control signals, thus allowing for a streamlined system dynamic by judiciously minimizing non-holonomic behaviors without degrading system performance. The proposed control schema, accentuated by the Kalman filter's presence, excels in dynamic efficiency and is ingeniously crafted to rectify any in-flight model discrepancies. Through exhaustive Matlab/Simulink simulations, our findings validate the exceptional efficiency and dependability of the advanced controller. This study advances Quadrotor UAV technology by leaps and bounds, signaling a pivotal evolution for applications that demand high-precision orbital tracking and enhanced noise mitigation through sophisticated nonlinear control mechanisms. [ABSTRACT FROM AUTHOR]

An aerodynamic/hydrodynamic investigation of water cross-over is performed for a bionic unmanned aquatic–aerial amphibious vehicle (bionic UAAV). According to flying fish features and UAAV flight requirements of water cross-over, the bionic conceptual design of crossing over water is described and planned in multiple stages and modes of motion. A solution procedure for the numerical simulation method, based on a modified SST turbulence model and the VOF model, is expressed, and a verification study is presented using a typical case. Longitudinal–lateral numerical simulation analysis investigates the cruise performance underwater and in the air. The numerical simulation and principal experiment verification are conducted for crossing over water and water surface acceleration. The results indicate that the bionic UAAV has an excellent aerodynamic/hydrodynamic performance and variant configuration to adapt to water cross-over. The bionic UAAV has good water and air navigation stability, and the cruise flying lift–drag ratio is greater than 15 at a low Reynolds number. Its pitching moment has the phenomenon of a "water mound" forming and breaking at the water cross-over process. The present method and the bionic variant configuration provide a feasible water cross-over design and analysis strategy for bionic UAAVs. [ABSTRACT FROM AUTHOR]

Volcano observatories (VOs) around the world are required to maintain surveillance of their volcanoes and inform civil protection and aviation authorities about impending eruptions. They often work through consolidated procedures to respond to volcanic crises in a timely manner and provide a service to the community aimed at reducing the potential impact of an eruption. Within the International Airways Volcano Watch (IAVW) framework of the International Civil Aviation Organisation (ICAO), designated State Volcano Observatories (SVOs) are asked to operate a colour coded system designed to inform the aviation community about the status of a volcano and the expected threats associated. Despite the IAVW documentation defining the different colour-coded levels, operating the aviation colour code in a standardised way is not easy, as sometimes, different SVOs adopt different strategies on how, when, and why to change it. Following two European VOs and Volcanic Ash Advisory Centres (VAACs) workshops, the European VOs agreed to present an overview on how they operate the aviation colour code. The comparative analysis presented here reveals that not all VOs in Europe use this system as part of their operational response, mainly because of a lack of volcanic eruptions since the aviation colour code was officially established, or the absence of a formal designation as an SVO. We also note that the VOs that do regularly use aviation colour code operate it differently depending on the frequency and styles of eruptions, the historical eruptive activity, the nature of the unrest, the monitoring level, institutional norms, previous experiences, and on the agreement they may have with the local Air Transport Navigation providers. This study shows that even though the aviation colour code system was designed to provide a standard, its usage strongly depends on the institutional subjectivity in responding to volcano emergencies. Some common questions have been identified across the different (S)VOs that will need to be addressed by ICAO to have a more harmonised approach and usage of the aviation colour code. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) photogrammetry is an emerging means of acquiring high-precision rapid spatial information and data because it is cost-effective and highly efficient. However, securing uniform quality in the results of UAV photogrammetry is difficult due to the use of low-cost navigation devices, non-surveying cameras, and rapid changes in shooting locations depending on the aircraft's behavior. In addition, no specific procedures or guidelines exist for performing quantitative quality tests or certification methods on UAV images. Additionally, test tools for UAV image quality assessment only use the ground sample distance (GSD), often resulting in a reduced image quality compared with that of manned aircraft images. In this study, we performed a modulation transfer function (MTF) analysis using a slanted edge target and a GSD analysis to confirm the necessity of MTF analysis in UAV image quality assessments. In this study, we aimed to address this issue by conducting a modulation transfer function (MTF) analysis using a slanted edge target and a ground sample distance (GSD) analysis. This was carried out to confirm the necessity of MTF analysis in evaluating UAV image quality. Furthermore, we analyzed the impact of flight height and mounted sensors on image quality at different study sites. [ABSTRACT FROM AUTHOR]