Navigation Of Aerial Vehicles Research Articles

Flood risk assessment of the Garita River in the urban zone of San Luis Potosí City, by hydrodynamic modeling

Rapid and uncontrolled urban growth and land use changes in watersheds worldwide have led to increased surface runoff within metropolitan areas, coupled with climate change, creating a risk for residents during the rainy season. The city of San Luis Potosí is no exception to this phenomenon. One affected watercourse is the Garita Stream, which flows inside the city near urbanization. It is essential to analyze the effects of urban sprawl on this stream based on historical precipitation data for the town. Hydrological and topographical information were required to conduct this research. The hydrological study of the basin involved analyzing the region’s geomorphology and historical climatological data. For the stream’s topography, aerial photogrammetry using an unmanned aerial Vehicle (UAV) and Global Navigation Satellite System (GNSS) equipment was employed to conduct topographic surveys in the area. To find out when the Garita stream would overflow and which areas are most likely to flood, numerical modeling was done using 1D, 2D, and 3D programs like SWMM5 (Storm Water Management Model), HEC-RAS (Hydrologic Engineering Center’s River Analysis System), and EDFC Explorer (Environmental Fluid Dynamics Code). These models simulated different return periods and their correlation with current flooding events recorded in the area, thereby further proposing solutions to mitigate overflow issues. By conducting these simulations and analyzing the results, solutions can be suggested to address the overflow problems in the area based on historical flood events at various return periods caused by the Garita Stream.

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.

Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.

Control and motion planning of fixed-wing UAV through reinforcement learning

Autonomous systems, driven by advancements in artificial intelligence and machine learning, are increasingly integral to various domains, including aerial vehicle control. This paper explores the application of reinforcement learning (RL) in the Guidance, Navigation, and Control (GNC) systems of aerial vehicles, specifically focusing on motion planning for fixed-wing UAVs. We present two key applications: waypoint tracking and dynamic target interception. These findings underscore the potential of reinforcement learning to enhance the robustness, adaptability, and efficiency of aerial vehicle control systems, paving the way for more autonomous and intelligent flight operations.

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

In this paper, we present the exploration of indoor positioning technologies for UAVs, as well as navigation techniques for path planning and obstacle avoidance. The objective was to perform warehouse inventory tasks, using a drone to search for barcodes or markers to identify objects. For the indoor positioning techniques, we employed visual-inertial odometry (VIO), ultra-wideband (UWB), AprilTag fiducial markers, and simultaneous localization and mapping (SLAM). These algorithms included global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. For UAV navigation, we combined the SLAM-based RTAB-map indoor mapping and navigation path planning of the ROS for indoor environments. This system enabled precise drone positioning indoors and utilized global and local path planners to generate flight paths that avoided dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve warehouse inventory inspection, a reinforcement learning approach was proposed, recognizing markers by adjusting the UAV’s viewpoint. We addressed several of the main problems in inventory management, including efficiently planning of paths, while ensuring a certain detection rate. Two reinforcement learning techniques, AC (actor–critic) and PPO (proximal policy optimization), were implemented based on AprilTag identification. Testing was performed in both simulated and real-world environments, and the effectiveness of the proposed method was validated.

A low-complexity ISAC method for LFM-PSK signals using de-chirping and ISAC phase unwrapping techniques

Integrated Sensing and Communication (ISAC) has the potential to revolutionize wireless technology in areas such as autonomous vehicles, the Internet of Things, and unmanned aerial vehicle navigation technology. Nonetheless, conventional processing methods for Linear Frequency Modulation-Phase Shift Keying (LFM-PSK) signals predominantly depend on matched filtering, leading to high sampling rates and computational complexity. In response to these challenges, our research proposes a method to process LFM-PSK signals, which enables the use of de-chirping techniques. Applying the proposed ISAC unwrapping technique makes it possible to selectively recover the phase of the de-chirped LFM-PSK signal. Subsequently, the distance can be calculated by analyzing the slope of the recovered phase. Moreover, by adjusting the threshold in the ISAC unwrapping technique, communication information can be extracted by detecting phase hops associated with PSK. This method offers an attractive alternative to traditional methods such as matched filtering and coherent demodulation techniques. We derive the Cramér-Rao lower bound (CRLB) for ranging. Theoretical analysis and simulation indicate that our method effectively reduces the required sampling rate and provides higher-ranging performance than the matched filtering technique in radar sensing. In high signal-to-noise ratio scenarios, our proposed method delivers a ranging performance close to that of CRLB. Regarding communication, our method has the advantage of lower synchronization requirements, making it more feasible for practical engineering applications than the conventional coherent demodulation method. Furthermore, it also necessitates lower computational resources compared to matched filtering techniques. Importantly, our method allows high device sharing, promoting cost-effective ISAC implementation.

Landslide Deposit Erosion and Reworking Documented by Geomatic Surveys at Mount Meager, BC, Canada

Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 106 m3) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 106 m3 of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 105 m3 of that sediment was redeposited inside the study area. About 5.8 × 105 m3 of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems.

An Anonymous Authenticated Group Key Agreement Scheme for Transfer Learning Edge Services Systems

The visual information processing technology based on deep learning (DL) can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning (TL) to unload deep neural networks (DNN) to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity is still challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of UAV authentication protocol and collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.

Developing an expansion‐based obstacle detection using panoptic segmentation

AbstractSafe 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%.

Повышение контраста и точности локализации объектов интереса на ортофотопланах местности, построенных по искаженным изображениям с беспилотного летательного аппарата

In this article, we will consider and compare methods for eliminating distortions in the original images obtained from an unmanned aerial vehicle. The main goal of these methods is to increase the contrast and accuracy of determining the coordinates of objects of interest relative to the background. To conduct the study, aerial work is performed, during which initial images of the underlying surface with different exposure times are obtained, as well as data from the unmanned aerial vehicle navigation module. Further, cameral processing of the received materials is carried out. The exact centers for photographing each image are calculated and orthophotomaps of the area are built. Then we have evaluated the contrast and accuracy of determining the coordinates of objects of interest relative to the background on the orthophotomaps of the area built from the original and restored images using various methods.

Artificial Intelligence Application for Unmanned Aerial Vehicle Navigation

This paper analyzes the use of artificial intelligence technology for autonomous control of an unmanned aerial vehicle. The remote control in difficult conditions limits the capabilities of the unmanned aerial vehicle and reduces the efficiency of the entire system. Therefore, an urgent task is the autonomous navigation of an unmanned aerial vehicle using artificial intelligence technology. In this case, the aircraft can perform tasks depending on the variability of external factors. Artificial intelligence in the autonomous control of an unmanned aerial vehicle plays an important role in ensuring the characteristics of human control. The paper explores several artificial intelligence approaches for autonomous navigation of an unmanned aerial vehicle: mathematical optimization, learning programs. Various characteristics, types, models of navigation are analyzed. The equation for the errors of the inertial navigation system in the normal earth’s coordinate system is analyzed.
 Keywords— Unmanned Aerial Vehicle; autonomous navigation; artificial intelligence; navigation models.

Automated vision-based structural health inspection and assessment for post-construction civil infrastructure

In the search to enhance the effectiveness and efficiency of structural health monitoring for preserving safety and integrity in civil infrastructure, the search for innovative technologies is of critical importance. This paper introduces ExpoDet, a comprehensive framework designed for autonomous health inspection and infrastructure assessment. ExpoDet features a multi-detection detector, autonomous navigation for micro aerial vehicles facilitated through secondary reward reinforcement learning, and a damage aggregation scheme for autonomous health assessment following detections. Moreover, it presents an attention module called EEAM+ that introduces dynamic feature orientation and significantly enhances the capabilities of ExpoDet. ExpoDet is extensively tested and evaluated in both offsite and field test experiments. Comparisons with several state-of-the-art object detectors coupled with attention modules show an average improvement of approximately 3% across various evaluation metrics.

Efficient Transmitter Selection Strategies for Improved Information Gathering of Aerial Vehicle Navigation in GNSS-Denied Environments

Aerial vehicle navigation in global navigation satellite system (GNSS)-denied environments by utilizing pseudorange measurements from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> terrestrial signals of opportunity (SOPs) is considered. To this end, the aerial vehicle is tasked with choosing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> most informative terrestrial SOPs. Two computationally efficient, but sub-optimal, transmitter selection strategies are proposed. These selection strategies, termed opportunistic greedy selection (OGS) and one-shot selection (OSS), exploit the additive, iterative properties of the Fisher information matrix (FIM), where OGS selects the most informative transmitters in finite iterations, while the OSS selects in one-iteration. Monte Carlo simulation results are presented comparing the OGS and OSS strategies versus the optimal (exhaustive search) selection strategy, where it is concluded that OGS performs closely to the optimal selection, while executing in a fraction of the optimal selection's time. Experimental results are presented of a U.S. Air Force high-altitude aircraft navigating without GNSS signals in (i) a rural region and (ii) a semi-urban region. The performance of the aircraft's navigation solution with the selected SOP transmitters via optimal, OGS, OSS are compared over a flight segment where the selection remained valid. The position root mean-squared error (RMSE) with the optimal, OGS, and OSS were 4.53 m, 6.28 m, 7.13 m in the rural region; and 5.83 m, 6.08 m, 6.70 m in the semi-urban region for an aircraft traversing a trajectory of 1.48 km and 1.22 km, respectively.

A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention

Geo-localization has been widely applied as an important technique to get the longitude and latitude for unmanned aerial vehicle (UAV) navigation in outdoor flight. Due to the possible interference and blocking of GPS signals, the method based on image retrieval, which is less likely to be interfered with, has received extensive attention in recent years. The geo-localization of UAVs and satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone images from different perspectives. In this paper, an image transformation technique is used to extract cross-view geo-localization information from UAVs and satellites. A single-stage training method in UAV and satellite geo-localization is first proposed, which simultaneously realizes cross-view feature extraction and image retrieval, and achieves higher accuracy than existing multi-stage training techniques. A novel piecewise soft-margin triplet loss function is designed to avoid model parameters being trapped in suboptimal sets caused by the lack of constraint on positive and negative samples. The results illustrate that the proposed loss function enhances image retrieval accuracy and realizes a better convergence. Moreover, a data augmentation method for satellite images is proposed to overcome the disproportionate numbers of image samples. On the benchmark University-1652, the proposed method achieves the state-of-the-art result with a 6.67% improvement in recall rate (R@1) and 6.13% in average precision (AP). All codes will be publicized to promote reproducibility.

Retraction: Al Said, N, Gorbachev, Y, Avdeenko, A. An unmanned aerial vehicles navigation system on the basis of pattern recognition applications—Review of implementation options and prospects for development. Software: Practice and Experience 2021; 51: 1509–1517. https://doi.org/10.1002/spe.2964

The above article, published online on July 2021 in Wiley Online Library https://onlinelibrary.wiley.com/doi/10.1002/spe.2964 has been retracted by agreement between the journal's Editor‐in‐Chief and John Wiley &amp; Sons Ltd.The retraction has been agreed following concerns regarding manipulation of the peer review and publishing process. Concerns were originally raised by a third party (1). Further investigation by the publisher has found manipulation of the peer review process. The retraction has been agreed because the peer review of the article was compromised. The authors disagree with this retraction. Abalkina, A. (2022). Publication and collaboration anomalies in academic papers originating from a paper mill: evidence from a Russia‐based paper mill https://arxiv.org/abs/2112.13322

Efficient UAV path planning using coverage map‐based value iteration

AbstractThis letter presents an efficient coverage map‐based unmanned aerial vehicle (UAV) navigation framework in cellular communication systems. Unlike previous research that focused on viewing UAV navigation as a Markov decision process in unknown continuous state space and leveraged various model‐free and deep neural network‐based reinforcement learning algorithms, a more straightforward and efficient model‐based value iteration algorithm is proposed. The algorithm leverages prior knowledge obtained through empirical channel models to develop a sampled coverage map that can be used in value iteration. A deep neural network is subsequently trained with supervised learning to approximate the optimal Q function in continuous state space. Finally, the trained neural network is applied to obtain a UAV trajectory that optimizes the objective function.

Advanced Control Techniques for Unmanned Aerial Vehicle (UAV) Navigation and Flight Control

Advanced Control Techniques for Unmanned Aerial Vehicle (UAV) Navigation and Flight Control

Unmanned aerial vehicle navigation in underground structure inspection: A review

Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers &amp; professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

DWPIS: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection

The primary premise of autonomous railway inspection using unmanned aerial vehicles is achieving autonomous flight along the railway. In our previous work, fitted centerline-based unmanned aerial vehicle (UAV) navigation is proven to be an effective method to guide UAV autonomous flying. However, the empirical parameters utilized in the fitting procedure lacked a theoretical basis and the fitted curves were also not coherent nor smooth. To address these problems, this paper proposes a skeleton detection method, called the dynamic-weight parallel instance and skeleton network, to directly extract the centerlines that can be viewed as skeletons. This multi-task branch network for skeleton detection and instance segmentation can be trained end to end. Our method reformulates a fused loss function with dynamic weights to control the dominant branch. During training, the sum of the weights always remains constant and the branch with a higher weight changes from instance to skeleton gradually. Experiments show that our model yields 93.98% mean average precision (mAP) for instance segmentation, a 51.9% F-measure score (F-score) for skeleton detection, and 60.32% weighted mean metrics for the entire network based on our own railway skeleton and instance dataset which comprises 3235 labeled overhead-view images taken in various environments. Our method can achieve more accurate railway skeletons and is useful to guide the autonomous flight of a UAV in railway inspection.

Opportunistic Autonomous Integrity Monitoring for Enhanced UAV Safety

An opportunistic advanced receiver autonomous integrity monitoring (OARAIM) algorithm for unmanned aerial vehicle (UAV) navigation is developed. This algorithm fuses ambient terrestrial signals of opportunity (SOPs) with global navigation satellite system (GNSS) signals to improve the capability of detecting GNSS and SOP measurement faults and provide tight protection level (PL) bounds. A receiver is assumed to make pseudorange measurements on multiple GNSS satellites and terrestrial SOPs. Experimental tests are presented injecting simulated faults into the OARAIM algorithm, which successfully detects the faults in GPS satellites and SOPs. Moreover, the OARAIM algorithm and the inclusion of the SOPs reduces the vertical and horizontal PLs by more than 55&#x0025; and 70&#x0025;, respectively, compared to only using GNSS measurements.