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Drones
Number of Followers: 3  Open Access journalISSN (Online) 2504-446X Published by MDPI  [258 journals]
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- Drones, Vol. 8, Pages 417: CrackScopeNet: A Lightweight Neural Network for
Rapid Crack Detection on Resource-Constrained Drone Platforms
Authors: Tao Zhang, Liwei Qin, Quan Zou, Liwen Zhang, Rongyi Wang, Heng Zhang
First page: 417
Abstract: Detecting cracks during structural health monitoring is crucial for ensuring infrastructure safety and longevity. Using drones to obtain crack images and automate processing can improve the efficiency of crack detection. To address the challenges posed by the limited computing resources of edge devices in practical applications, we propose CrackScopeNet, a lightweight segmentation network model that simultaneously considers local and global crack features while being suitable for deployment on drone platforms with limited computational power and memory. This novel network features a multi-scale branch to improve sensitivity to cracks of varying sizes without substantial computational overhead along with a stripe-wise context attention mechanism to enhance the capture of long-range contextual information while mitigating the interference from complex backgrounds. Experimental results on the CrackSeg9k dataset demonstrate that our method leads to a significant improvement in prediction performance, with the highest mean intersection over union (mIoU) scores reaching 82.12%, and maintains a lightweight architecture with only 1.05 M parameters and 1.58 G floating point operations (FLOPs). In addition, the proposed model excels in inference speed on edge devices without a GPU thanks to its low FLOPs. CrackScopeNet contributes to the development of efficient and effective crack segmentation networks suitable for practical structural health monitoring applications using drone platforms.
Citation: Drones
PubDate: 2024-08-23
DOI: 10.3390/drones8090417
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 418: Collaborative Obstacle Detection for Dual USVs
Using MGNN-DANet with Movable Virtual Nodes and Double Attention
Authors: Zehao He, Ligang Li, Hongbin Xu, Lv Zong, Yongshou Dai
First page: 418
Abstract: To reduce missed detections in LiDAR-based obstacle detection, this paper proposes a dual unmanned surface vessels (USVs) obstacle detection method using the MGNN-DANet template matching framework. Firstly, point cloud templates for each USV are created, and a clustering algorithm extracts suspected targets from the point clouds captured by a single USV. Secondly, a graph neural network model based on the movable virtual nodes is designed, introducing a neighborhood distribution uniformity metric. This model enhances the local point cloud distribution features of the templates and suspected targets through a local sampling strategy. Furthermore, a feature matching model based on double attention is developed, employing self-attention to aggregate the features of the templates and cross-attention to evaluate the similarity between suspected targets and aggregated templates, thereby identifying and locating another USV within the targets detected by each USV. Finally, the deviation between the measured and true positions of one USV is used to correct the point clouds obtained by the other USV, and obstacle positions are annotated through dual-view point cloud clustering. Experimental results show that, compared to single USV detection methods, the proposed method reduces the missed detection rate of maritime obstacles by 7.88% to 14.69%.
Citation: Drones
PubDate: 2024-08-23
DOI: 10.3390/drones8090418
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 419: Traversability Analysis and Path Planning for
Autonomous Wheeled Vehicles on Rigid Terrains
Authors: Nan Wang, Xiang Li, Zhe Suo, Jiuchen Fan, Jixin Wang, Dongxuan Xie
First page: 419
Abstract: Autonomous vehicles play a crucial role in three-dimensional transportation systems and have been extensively investigated and implemented in mining and other fields. However, the diverse and intricate terrain characteristics present challenges to vehicle traversability, including complex geometric features such as slope, harsh physical parameters such as friction and roughness, and irregular obstacles. The current research on traversability analysis primarily emphasizes the processing of perceptual information, with limited consideration for vehicle performance and state parameters, thereby restricting their applicability in path planning. A framework of traversability analysis and path planning methods for autonomous wheeled vehicles on rigid terrains is proposed in this paper for better traversability costs and less redundancy in path planning. The traversability boundary conditions are established first based on terrain and vehicle characteristics using theoretical methods to determine the traversable areas. Then, the traversability cost map for the traversable areas is obtained through simulation and segmented linear regression analysis. Afterward, the TV-Hybrid A* algorithm is proposed by redefining the path cost functions of the Hybrid A* algorithm through the simulation data and neural network method to generate a more cost-effective path. Finally, the path generated by the TV-Hybrid A* algorithm is validated and compared with that of the A* and Hybrid A* algorithms in simulations, demonstrating a slightly better traversability cost for the former.
Citation: Drones
PubDate: 2024-08-23
DOI: 10.3390/drones8090419
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 420: Nonlinear Adaptive Control Design for Quadrotor
UAV Transportation System
Authors: Boyu Zhu, Dazhi Wang
First page: 420
Abstract: In response to the non-linear and underactuated characteristics of quadrotor UAV suspension transportation system, this paper proposes a novel control strategy aimed at achieving precise position control, attitude control, and anti-swing capabilities. Firstly, a dynamical model required for controller design is established through the Newton-Euler method. In the controller design process, the paper employs the energy method and barrier Lyapunov function to design a double-closed-loop nonlinear controller. This controller is capable of not only accurately controlling the position and attitude angles of the quadrotor UAV suspension transportation system but also effectively suppressing the swing of the payload. Building on this, considering the elastic deformation of the lifting cable, and by analyzing the forces in the Newton-Euler equations, this paper proposes an adaptive control design for the case where the length of the cable connecting the UAV and the payload is unknown. To validate the effectiveness of the proposed control scheme, comparative experiments were conducted in the MATLAB simulation environment, and the results indicate that the method proposed in this paper exhibits superior control performance compared to traditional controllers.
Citation: Drones
PubDate: 2024-08-24
DOI: 10.3390/drones8090420
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 421: Military Image Captioning for Low-Altitude UAV
or UGV Perspectives
Authors: Lizhi Pan, Chengtian Song, Xiaozheng Gan, Keyu Xu, Yue Xie
First page: 421
Abstract: Low-altitude unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which boast high-resolution imaging and agile maneuvering capabilities, are widely utilized in military scenarios and generate a vast amount of image data that can be leveraged for textual intelligence generation to support military decision making. Military image captioning (MilitIC), as a visual-language learning task, provides innovative solutions for military image understanding and intelligence generation. However, the scarcity of military image datasets hinders the advancement of MilitIC methods, especially those based on deep learning. To overcome this limitation, we introduce an open-access benchmark dataset, which was termed the Military Objects in Real Combat (MOCO) dataset. It features real combat images captured from the perspective of low-altitude UAVs or UGVs, along with a comprehensive set of captions. Furthermore, we propose a novel encoder–augmentation–decoder image-captioning architecture with a map augmentation embedding (MAE) mechanism, MAE-MilitIC, which leverages both image and text modalities as a guiding prefix for caption generation and bridges the semantic gap between visual and textual data. The MAE mechanism maps both image and text embeddings onto a semantic subspace constructed by relevant military prompts, and augments the military semantics of the image embeddings with attribute-explicit text embeddings. Finally, we demonstrate through extensive experiments that MAE-MilitIC surpasses existing models in performance on two challenging datasets, which provides strong support for intelligence warfare based on military UAVs and UGVs.
Citation: Drones
PubDate: 2024-08-24
DOI: 10.3390/drones8090421
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 422: Enhancing Unmanned Aerial Vehicle Task
Assignment with the Adaptive Sampling-Based Task Rationality Review
Algorithm
Authors: Cheng Sun, Yuwen Yao, Enhui Zheng
First page: 422
Abstract: As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes.
Citation: Drones
PubDate: 2024-08-24
DOI: 10.3390/drones8090422
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 423: A Minimal Solution Estimating the Position of
Cameras with Unknown Focal Length with IMU Assistance
Authors: Kang Yan, Zhenbao Yu, Chengfang Song, Hongping Zhang, Dezhong Chen
First page: 423
Abstract: Drones are typically built with integrated cameras and inertial measurement units (IMUs). It is crucial to achieve drone attitude control through relative pose estimation using cameras. IMU drift can be ignored over short periods. Based on this premise, in this paper, four methods are proposed for estimating relative pose and focal length across various application scenarios: for scenarios where the camera’s focal length varies between adjacent moments and is unknown, the relative pose and focal length can be computed from four-point correspondences; for planar motion scenarios where the camera’s focal length varies between adjacent moments and is unknown, the relative pose and focal length can be determined from three-point correspondences; for instances of planar motion where the camera’s focal length is equal between adjacent moments and is unknown, the relative pose and focal length can be calculated from two-point correspondences; finally, for scenarios where multiple cameras are employed for image acquisition but only one is calibrated, a method proposed for estimating the pose and focal length of uncalibrated cameras can be used. The numerical stability and performance of these methods are compared and analyzed under various noise conditions using simulated datasets. We also assessed the performance of these methods on real datasets captured by a drone in various scenes. The experimental results demonstrate that the method proposed in this paper achieves superior accuracy and stability to classical methods.
Citation: Drones
PubDate: 2024-08-24
DOI: 10.3390/drones8090423
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 424: Performance Estimation of Fixed-Wing UAV
Propulsion Systems
Authors: Mohamed Etewa, Ahmed F. Hassan, Ehab Safwat, Mohammed A. H. Abozied, Mohamed M. El-Khatib, Alejandro Ramirez-Serrano
First page: 424
Abstract: The evaluation of propulsion systems used in UAVs is of paramount importance to enhance the flight endurance, increase the flight control performance, and minimize the power consumption. This evaluation, however, is typically performed experimentally after the preliminary hardware design of the UAV is completed, which tends to be expensive and time-consuming. In this paper, a comprehensive theoretical UAV propulsion system assessment is proposed to assess both static and dynamic performance characteristics via an integrated simulation model. The approach encompasses the electromechanical dynamics of both the motor and its controller. The proposed analytical model estimates the propeller and motor combination performance with the overarching goal of enhancing the overall efficiency of the aircraft propulsion system before expensive costs are incurred. The model embraces an advanced blade element momentum theory underpinned by the development of a novel mechanism to predict the propeller performance under low Reynolds number conditions. The propeller model utilizes XFOIL and various factors, including post-stall effects, 3D correction, Reynolds number fluctuations, and tip loss corrections to predict the corresponding aerodynamic loads. Computational fluid dynamics are used to corroborate the dynamic formulations followed by extensive experimental tests to validate the proposed estimation methodology.
Citation: Drones
PubDate: 2024-08-25
DOI: 10.3390/drones8090424
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 425: Robust Symbol and Frequency Synchronization
Method for Burst OFDM Systems in UAV Communication
Authors: Lintao Li, Yue Han, Zongru Li, Hua Li, Jiayi Lv, Yimin Li
First page: 425
Abstract: This paper introduces a robust synchronization method for orthogonal frequency division multiplexing (OFDM) in multi-unmanned aerial vehicle (UAV) communication systems, focusing on minimizing overhead while achieving reliable synchronization. The proposed synchronization scheme enhances both frame efficiency and implementation simplicity. Initially, a high-efficiency frame structure is designed without a guard time interval, utilizing a preamble sequence to simultaneously achieve both symbol synchronization and automatic gain control (AGC) before demodulation. Subsequently, a novel 2-bit non-uniform quantization method for the Zadoff–Chu sequences is developed, enabling the correlation operations in the traditional symbol synchronization algorithm to be implemented via bitwise exclusive OR (XOR) and addition operations. The complexity of hardware implementation and the energy consumption for symbol synchronization can be reduced significantly. Furthermore, the impact of AGC on frequency synchronization performance is examined, and an improved frequency synchronization method based on AGC gain compensation is proposed. Finally, the performance of the proposed method is rigorously analyzed and compared with that of the traditional method through computer simulations, demonstrating the effectiveness and superiority of the proposed approach.
Citation: Drones
PubDate: 2024-08-25
DOI: 10.3390/drones8090425
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 426: Characterization of Strategic Deconflicting
Service Impact on Very Low-Level Airspace Capacity
Authors: Zhiqiang Liu, Jose Luis Munoz-Gamarra, Juan José Ramos Gonzalez
First page: 426
Abstract: European airspace is poised for significant transformation as it prepares to accommodate a new class of unmanned traffic that will reshape the transport of people and goods. Unmanned aerial vehicle traffic will introduce a new level of services, but it remains unclear how safety and operators’ time flexibility in flight planning will impact capacity. This study focuses on the impact of strategic deconflicting services on the capacity of the very low-level airspace, a critical area in the future management of unmanned aerial vehicle traffic. The results validate the assumptions regarding the roles of airspace managers and drone operators through simulation studies; highlight the limitations of the first come, first served policy; and propose a batch policy as a potential optimization strategy for future airspace capacity management. The forecasting model developed using regression techniques provides a general method for predicting airspace capacity under specific conditions, contributing to the safe and efficient integration of unmanned aerial vehicles into European airspace.
Citation: Drones
PubDate: 2024-08-25
DOI: 10.3390/drones8090426
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 427: Formation Cooperative Intelligent Tactical
Decision Making Based on Bayesian Network Model
Authors: Junxiao Guo, Jiandong Zhang, Zihan Wang, Xiaoliang Liu, Shixi Zhou, Guoqing Shi, Zhuoyong Shi
First page: 427
Abstract: This paper proposes a method based on a Bayesian network model to study the intelligent tactical decision making of formation coordination. For the problem of formation coordinated attack target allocation, a coordinated attack target allocation model based on the dominance matrix is constructed, and a threat degree assessment model is constructed by calculating the minimum interception time. For the problem of real-time updating of the battlefield situation in the formation confrontation simulation, real-time communication between the UAV formation on the battlefield is realized, improving the efficiency of communication and target allocation between formations on the battlefield. For the problem of UAV autonomous air combat decision making, on the basis of the analysis of the advantage function calculation of the air combat decision-making model and a Bayesian network model analysis, the network model's nodes and states are determined, and the air combat decision-making model is constructed based on the Bayesian network. Our formation adopts the Bayesian algorithm strategy to fight against the blue side's UAVs, and the formation defeats the blue UAVs through coordinated attack, which proves the reasonableness of coordinated target allocation. An evaluation function is established, and the comprehensive scores of our formation are compared with those of other algorithms, which proves the accuracy and intelligibility of the decision making of the Bayesian network.
Citation: Drones
PubDate: 2024-08-25
DOI: 10.3390/drones8090427
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 428: Large-Scale Solar-Powered UAV Attitude Control
Using Deep Reinforcement Learning in Hardware-in-Loop Verification
Authors: Yongzhao Yan, Huazhen Cao, Boyang Zhang, Wenjun Ni, Bo Wang, Xiaoping Ma
First page: 428
Abstract: Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090428
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 429: Online Safe Flight Control Method Based on
Constraint Reinforcement Learning
Authors: Jiawei Zhao, Haotian Xu, Zhaolei Wang, Tao Zhang
First page: 429
Abstract: UAVs are increasingly prominent in the competition for space due to their multiple characteristics, such as strong maneuverability, long flight distance, and high survivability. A new online safe flight control method based on constrained reinforcement learning is proposed for the intelligent safety control of UAVs. This method adopts constrained policy optimization as the main reinforcement learning framework and develops a constrained policy optimization algorithm with extra safety budget, which introduces Lyapunov stability requirements and limits rudder deflection loss to ensure flight safety and improves the robustness of the controller. By efficiently interacting with the constructed simulation environment, a control law model for UAVs is trained. Subsequently, a condition-triggered meta-learning online learning method is used to adjust the control raw online ensuring successful attitude angle tracking. Simulation experimental results show that using online control laws to perform aircraft attitude angle control tasks has an overall score of 100 points. After introducing online learning, the adaptability of attitude control to comprehensive errors such as aerodynamic parameters and wind improved by 21% compared to offline learning. The control law can be learned online to adjust the control policy of UAVs, ensuring their safety and stability during flight.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090429
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 430: A Stochastic Drone-Scheduling Problem with
Uncertain Energy Consumption
Authors: Yandong He, Zhong Zheng, Huilin Li, Jie Deng
First page: 430
Abstract: In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, we developed a two-stage stochastic programming model with recourse cost, and we employed a fixed-sample sampling strategy based on Monte Carlo simulation to characterize uncertain variables, followed by the design of an efficient variable neighborhood search algorithm to solve the model. Case study results indicate the superiority of our algorithm over genetic algorithms. Additionally, a comparison between deterministic and stochastic models suggests that considering the uncertainty in energy consumption can significantly enhance the average returns of unmanned aerial vehicle scheduling systems.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090430
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 431: A Lightweight Insulator Defect Detection Model
Based on Drone Images
Authors: Yang Lu, Dahua Li, Dong Li, Xuan Li, Qiang Gao, Xiao Yu
First page: 431
Abstract: With the continuous development and construction of new power systems, using drones to inspect the condition of transmission line insulators has become an inevitable trend. To facilitate the deployment of drone hardware equipment, this paper proposes IDD-YOLO (Insulator Defect Detection-YOLO), a lightweight insulator defect detection model. Initially, the backbone network of IDD-YOLO employs GhostNet for feature extraction. However, due to the limited feature extraction capability of GhostNet, we designed a lightweight attention mechanism called LCSA (Lightweight Channel-Spatial Attention), which is combined with GhostNet to capture features more comprehensively. Secondly, the neck network of IDD-YOLO utilizes PANet for feature transformation and introduces GSConv and C3Ghost convolution modules to reduce redundant parameters and lighten the network. The head network employs the YOLO detection head, incorporating the EIOU loss function and Mish activation function to optimize the speed and accuracy of insulator defect detection. Finally, the model is optimized using TensorRT and deployed on the NVIDIA Jetson TX2 NX mobile platform to test the actual inference speed of the model. The experimental results demonstrate that the model exhibits outstanding performance on both the proprietary ID-2024 insulator defect dataset and the public SFID insulator dataset. After optimization with TensorRT, the actual inference speed of the IDD-YOLO model reached 20.83 frames per second (FPS), meeting the demands for accurate and real-time inspection of insulator defects by drones.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090431
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 432: Using the MSFNet Model to Explore the Temporal
and Spatial Evolution of Crop Planting Area and Increase Its Contribution
to the Application of UAV Remote Sensing
Authors: Gui Hu, Zhigang Ren, Jian Chen, Ni Ren, Xing Mao
First page: 432
Abstract: Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090432
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 433: Spatial-Temporal Contextual Aggregation Siamese
Network for UAV Tracking
Authors: Qiqi Chen, Xuan Wang, Faxue Liu, Yujia Zuo, Chenglong Liu
First page: 433
Abstract: In recent years, many studies have used Siamese networks (SNs) for UAV tracking. However, there are two problems with SNs for UAV tracking. Firstly, the information sources of the SNs are the invariable template patch and the current search frame. The static template information lacks the perception of dynamic feature information flow, and the shallow feature extraction and linear sequential mapping severely limit the mining of feature expressiveness. This makes it difficult for many existing SNs to cope with the challenges of UAV tracking, such as scale variation and viewpoint change caused by the change in height and angle of the UAV, and the challenges of background clutter and occlusion caused by complex aviation backgrounds. Secondly, the SNs trackers for UAV tracking still struggle with extracting lightweight and effective features. A tracker with a heavy-weighted backbone is not welcome due to the limited computing power of the UAV platform. Therefore, we propose a lightweight spatial-temporal contextual Siamese tracking system for UAV tracking (SiamST). The proposed SiamST improves the UAV tracking performance by augmenting the horizontal spatial information and introducing vertical temporal information to the Siamese network. Specifically, a high-order multiscale spatial module is designed to extract multiscale remote high-order spatial information, and a temporal template transformer introduces temporal contextual information for dynamic template updating. The evaluation and contrast results of the proposed SiamST with many state-of-the-art trackers on three UAV benchmarks show that the proposed SiamST is efficient and lightweight.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090433
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 434: Cooperative Drone Transportation of a
Cable-Suspended Load: Dynamics and Control
Authors: Elia Costantini, Emanuele Luigi de Angelis, Fabrizio Giulietti
First page: 434
Abstract: The cooperative transportation of a cable-suspended load by two unmanned rotorcraft is analyzed. Initially, the equations describing a system composed of three point masses and two rigid cables are derived. The model is then linearized about the hovering condition, and analytical expressions are derived to describe the eigenstructure of the open-loop system. Thanks to the specific parameterization of the problem, the different dynamic modes are outlined and discussed within an analytical framework. A novel controller is designed to enable the UAVs in the formation to perform trajectory tracking, maintain formation geometry, and stabilize payload swing simultaneously. A preliminary investigation of closed-loop stability is conducted using a linear approach. Validation is performed in a realistic simulation scenario where two drones are modeled as rigid bodies under the effect of external disturbances and rotor-generated forces and moments, as obtained by Blade Element Theory. The proposed method demonstrates relative simplicity and significantly improves the flying qualities of delivery operations while minimizing hazardous payload oscillations and reducing energy demand.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090434
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 435: Multi-UAV Path Planning Based on Cooperative
Co-Evolutionary Algorithms with Adaptive Decision Variable Selection
Authors: Qicheng Meng, Qingjun Qu, Kai Chen, Taihe Yi
First page: 435
Abstract: When dealing with UAV path planning problems, evolutionary algorithms demonstrate strong flexibility and global search capabilities. However, as the number of UAVs increases, the scale of the path planning problem grows exponentially, leading to a significant rise in computational complexity. The Cooperative Co-Evolutionary Algorithm (CCEA) effectively addresses this issue through its divide-and-conquer strategy. Nonetheless, the CCEA needs to find a balance between computational efficiency and algorithmic performance while also resolving convergence difficulties arising from the increased number of decision variables. Moreover, the complex interrelationships between the decision variables of each UAV add to the challenge of selecting appropriate decision variables. To tackle this problem, we propose a novel collaborative algorithm called CCEA-ADVS. This algorithm reduces the difficulty of the problem by decomposing high-dimensional variables into sub-variables for collaborative optimization. To improve the efficiency of decision variable selection in the collaborative algorithm and to accelerate the convergence speed, an adaptive decision variable selection strategy is introduced. This strategy selects decision variables according to the order of solving single-UAV constraints and multi-UAV constraints, reducing the cost of the optimization objective. Furthermore, to improve computational efficiency, a two-stage evolutionary optimization process from coarse to fine is adopted. Specifically, the Adaptive Differential Evolution with Optional External Archive algorithm (JADE) is first used to optimize the waypoints of the UAVs to generate a basic path, and then, the Dubins algorithm is combined to optimize the trajectory, yielding the final flight path. The experimental results show that in four different scenarios involving 40 UAVs, the CCEA-ADVS algorithm significantly outperforms the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and JADE algorithms in terms of path performance, running time, computational efficiency, and convergence speed. In addition, in large-scale experiments involving 500 UAVs, the algorithm also demonstrates good adaptability, stability, and scalability.
Citation: Drones
PubDate: 2024-08-26
DOI: 10.3390/drones8090435
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 436: An Improved YOLOv7 Model for Surface Damage
Detection on Wind Turbine Blades Based on Low-Quality UAV Images
Authors: Yongkang Liao, Mingyang Lv, Mingyong Huang, Mingwei Qu, Kehan Zou, Lei Chen, Liang Feng
First page: 436
Abstract: The efficient damage detection of the wind turbine blade (WTB), the core part of the wind power, is very improtant to wind power. In this paper, an improved YOLOv7 model is designed to enhance the performance of surface damage detection on WTBs based on the low-quality unmanned aerial vehicle (UAV) images. (1) An efficient channel attention (ECA) module is imbeded, which makes the network more sensitive to damage to decrease the false detection and missing detection caused by the low-quality image. (2) A DownSampling module is introduced to retain key feature information to enhance the detection speed and accuracy which are restricted by low-quality images with large amounts of redundant information. (3) The Multiple attributes Intersection over Union (MIoU) is applied to improve the inaccurate detection location and detection size of the damage region. (4) The dynamic group convolution shuffle transformer (DGST) is developed to improve the ability to comprehensively capture the contours, textures and potential damage information. Compared with YOLOv7, YOLOv8l, YOLOv9e and YOLOv10x, this experiment’s results show that the improved YOLOv7 has the optimal detection performance synthetically considering the detection accuracy, the detection speed and the robustness.
Citation: Drones
PubDate: 2024-08-27
DOI: 10.3390/drones8090436
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 437: A DDoS Tracking Scheme Utilizing Adaptive Beam
Search with Unmanned Aerial Vehicles in Smart Grid
Authors: Wei Guo, Zhi Zhang, Liyuan Chang, Yue Song, Liuguo Yin
First page: 437
Abstract: As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions.
Citation: Drones
PubDate: 2024-08-28
DOI: 10.3390/drones8090437
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 438: Categorical-Parallel Adversarial Defense for
Perception Models on Single-Board Embedded Unmanned Vehicles
Authors: Yilan Li, Xing Fan, Shiqi Sun, Yantao Lu, Ning Liu
First page: 438
Abstract: Significant advancements in robustness against input perturbations have been realized for deep neural networks (DNNs) through the application of adversarial training techniques. However, implementing these methods for perception tasks in unmanned vehicles, such as object detection and semantic segmentation, particularly on real-time single-board computing devices, encounters two primary challenges: the time-intensive nature of training large-scale models and performance degradation due to weight quantization in real-time deployments. To address these challenges, we propose Ca-PAT, an efficient and effective adversarial training framework designed to mitigate perturbations. Ca-PAT represents a novel approach by integrating quantization effects into adversarial defense strategies specifically for unmanned vehicle perception models on single-board computing platforms. Notably, Ca-PAT introduces an innovative categorical-parallel adversarial training mechanism for efficient defense in large-scale models, coupled with an alternate-direction optimization framework to minimize the adverse impacts of weight quantization. We conducted extensive experiments on various perception tasks using the Imagenet-te dataset and data collected from physical unmanned vehicle platforms. The results demonstrate that the Ca-PAT defense framework significantly outperforms state-of-the-art baselines, achieving substantial improvements in robustness across a range of perturbation scenarios.
Citation: Drones
PubDate: 2024-08-28
DOI: 10.3390/drones8090438
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 439: Unlicensed Spectrum Access and Performance
Analysis for NR-U/WiGig Coexistence in UAV Communication Systems
Authors: Zhenzhen Hu, Yong Xu, Yonghong Deng, Zhongpei Zhang
First page: 439
Abstract: Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%.
Citation: Drones
PubDate: 2024-08-28
DOI: 10.3390/drones8090439
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 440: Advancing mmWave Altimetry for Unmanned Aerial
Systems: A Signal Processing Framework for Optimized Waveform Design
Authors: Maaz Ali Awan, Yaser Dalveren, Ali Kara, Mohammad Derawi
First page: 440
Abstract: This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input–multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77–81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.
Citation: Drones
PubDate: 2024-08-28
DOI: 10.3390/drones8090440
Issue No: Vol. 8, No. 9 (2024)
- Drones, Vol. 8, Pages 342: A Distributed Task Allocation Method for
Multi-UAV Systems in Communication-Constrained Environments
Authors: Shaokun Yan, Jingxiang Feng, Feng Pan
First page: 342
Abstract: This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.
Citation: Drones
PubDate: 2024-07-23
DOI: 10.3390/drones8080342
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 343: Contextual Enhancement–Interaction and
Multi-Scale Weighted Fusion Network for Aerial Tracking
Authors: Bo Wang, Xuan Wang, Linglong Ma, Yujia Zuo, Chenglong Liu
First page: 343
Abstract: Siamese-based trackers have been widely utilized in UAV visual tracking due to their outstanding performance. However, UAV visual tracking encounters numerous challenges, such as similar targets, scale variations, and background clutter. Existing Siamese trackers face two significant issues: firstly, they rely on single-branch features, limiting their ability to achieve long-term and accurate aerial tracking. Secondly, current tracking algorithms treat multi-level similarity responses equally, making it difficult to ensure tracking accuracy in complex airborne environments. To tackle these challenges, we propose a novel UAV tracking Siamese network named the contextual enhancement–interaction and multi-scale weighted fusion network, which is designed to improve aerial tracking performance. Firstly, we designed a contextual enhancement–interaction module to improve feature representation. This module effectively facilitates the interaction between the template and search branches and strengthens the features of each branch in parallel. Specifically, a cross-attention mechanism within the module integrates the branch information effectively. The parallel Transformer-based enhancement structure improves the feature saliency significantly. Additionally, we designed an efficient multi-scale weighted fusion module that adaptively weights the correlation response maps across different feature scales. This module fully utilizes the global similarity response between the template and the search area, enhancing feature distinctiveness and improving tracking results. We conducted experiments using several state-of-the-art trackers on aerial tracking benchmarks, including DTB70, UAV123, UAV20L, and UAV123@10fps, to validate the efficacy of the proposed network. The experimental results demonstrate that our tracker performs effectively in complex aerial tracking scenarios and competes well with state-of-the-art trackers.
Citation: Drones
PubDate: 2024-07-24
DOI: 10.3390/drones8080343
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 344: Adaptive Factor Fuzzy Controller for Keeping
Multi-UAV Formation While Avoiding Dynamic Obstacles
Authors: Bangmin Gong, Yiyang Li, Li Zhang, Jianliang Ai
First page: 344
Abstract: The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness.
Citation: Drones
PubDate: 2024-07-25
DOI: 10.3390/drones8080344
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 345: Heterogeneous Multi-UAV Mission Reallocation
Based on Improved Consensus-Based Bundle Algorithm
Authors: Wenhao Bi, Junyi Shen, Jiuli Zhou, An Zhang
First page: 345
Abstract: In dynamic complex environments, it is inevitable for UAVs to be damaged due to their confrontational nature. The challenge to minimize the adverse effects of the damage and reallocate the mission is vital for achieving the operational goal. This paper proposes a distributed Multi-UAV mission reallocation method in the case of UAV damage based on the improved consensus-based bundle algorithm (CBBA). Firstly, a dynamic optimization model for Multi-UAV mission reallocation is established based on an improved resource update model. Secondly, a distributed damage inspection method based on the heartbeat hold mechanism is proposed for real-time monitoring of UAV conditions, which could enable the rapid response to UAV damage events. Furthermore, the CBBA is improved by introducing a timeliness parameter to adjust the bidding strategy and optimizing the mission selection strategy based on the time-order priority insertion principle to generate mission reallocation plans quickly. Through numerical examples, the results show that the proposed method can effectively reallocate Multi-UAV missions under damage events and has superior performance compared with original the CBBA, the particle swarm optimization (PSO) algorithm, and the performance impact (PI) algorithm. The proposed method has a faster solving speed, while the obtained solution has higher mission reallocation effectiveness.
Citation: Drones
PubDate: 2024-07-25
DOI: 10.3390/drones8080345
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 346: Power Transmission Lines Foreign Object
Intrusion Detection Method for Drone Aerial Images Based on Improved
YOLOv8 Network
Authors: Hongbin Sun, Qiuchen Shen, Hongchang Ke, Zhenyu Duan, Xi Tang
First page: 346
Abstract: With the continuous growth of electricity demand, the safety and stability of transmission lines have become increasingly important. To ensure the reliability of power supply, it is essential to promptly detect and address foreign object intrusions on transmission lines, such as tree branches, kites, and balloons. Addressing the issues where foreign objects can cause power outages and severe safety accidents, as well as the inefficiency, time consumption, and labor-intensiveness of traditional manual inspection methods, especially in large-scale power transmission lines, we propose an enhanced YOLOv8-based model for detecting foreign objects. This model incorporates the Swin Transformer, AFPN (Asymptotic Feature Pyramid Network), and a novel loss function, Focal SIoU, to improve both the accuracy and real-time detection of hazards. The integration of the Swin Transformer into the YOLOv8 backbone network significantly improves feature extraction capabilities. The AFPN enhances the multi-scale feature fusion process, effectively integrating information from different levels and improving detection accuracy, especially for small and occluded objects. The introduction of the Focal SIoU loss function optimizes the model’s training process, enhancing its ability to handle hard-to-classify samples and uncertain predictions. This method achieves efficient automatic detection of foreign objects by comprehensively utilizing multi-level feature information and optimized label matching strategies. The dataset used in this study consists of images of foreign objects on power transmission lines provided by a power supply company in Jilin, China. These images were captured by drones, offering a comprehensive view of the transmission lines and enabling the collection of detailed data on various foreign objects. Experimental results show that the improved YOLOv8 network has high accuracy and recall rates in detecting foreign objects such as balloons, kites, and bird nests, while also possessing good real-time processing capabilities.
Citation: Drones
PubDate: 2024-07-25
DOI: 10.3390/drones8080346
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 347: Integrated Low Electromagnetic Interference
Design Method for Small, Fixed-Wing UAVs for Magnetic Anomaly Detection
Authors: Jiahao Ge, Jinwu Xiang, Daochun Li
First page: 347
Abstract: Unmanned aerial vehicles (UAVs) equipped with magnetic airborne detectors (MADs) represent a new combination for underground or undersea magnetic anomaly detection. The electromagnetic interference (EMI) generated by a UAV platform affects the acquisition of weak magnetic signals by the MADs, which brings unique conceptual design difficulties. This paper proposes a systematic and integrated low-EMI design method for small, fixed-wing UAVs. First, the EMI at the MAD is analyzed. Second, sensor layout optimization for a single UAV is carried out, and the criteria for the sensor layout are given. To enhance UAV stability and resist atmospheric disturbances at sea, the configuration is optimized using an improved genetic algorithm. Then, three typical multi-UAV formations are analyzed. Finally, the trajectory is designed based on an analysis of its influence on EMI at the MAD. The simulation results show that the low-EMI design can keep MADs away from the EMI sources of UAVs and maintain flight stability. The thread-like formation is the best choice in terms of mutual interference and search width. The results also reveal the close relationship between the low-EMI design and flight trajectory. This research can provide a reference for the conceptual design and trajectory optimization of small, fixed-wing UAVs for magnetic anomaly detection.
Citation: Drones
PubDate: 2024-07-25
DOI: 10.3390/drones8080347
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 348: Path Planning for Autonomous Underwater
Vehicles (AUVs) Considering the Influences and Constraints of Ocean
Currents
Authors: Ziming Chen, Jinjin Yan, Ruen Huang, Yisong Gao, Xiuyan Peng, Weijie Yuan
First page: 348
Abstract: Ocean currents pose a significant challenge in the path planning of autonomous underwater vehicles (AUVs), with conventional path-planning algorithms often failing to effectively counter these influences. In response to this challenge, we propose a path-planning algorithm that can consider the influences and constraints of ocean currents, which leverages the strengths of two widely employed path-planning algorithms, A* and the genetic algorithm (GA), to account for the influences of ocean currents on the planned paths. Specifically, it enhances the initial population generation, formulates a fitness function tailored to ocean current conditions, and employs an adaptive mutation approach to enhance population diversity and stability. By utilizing simulated and real-world ocean current datasets, we validated the feasibility of the proposed algorithm with quantitative metrics. The results demonstrate that in comparison to conventional methods, the new algorithm can deal with the influences and constraints of ocean currents in AUV path planning, resulting in notable enhancements in path smoothness, energy efficiency, and safety.
Citation: Drones
PubDate: 2024-07-26
DOI: 10.3390/drones8080348
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 349: Multiple Object Tracking in Drone Aerial Videos
by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern
Authors: Yubin Yuan, Yiquan Wu, Langyue Zhao, Yaxuan Pang, Yuqi Liu
First page: 349
Abstract: Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions.
Citation: Drones
PubDate: 2024-07-28
DOI: 10.3390/drones8080349
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 350: Decentralized UAV Swarm Control: A
Multi-Layered Architecture for Integrated Flight Mode Management and
Dynamic Target Interception
Authors: Bingze Xia, Iraj Mantegh, Wenfang Xie
First page: 350
Abstract: Uncrewed Aerial Vehicles (UAVs) are increasingly deployed across various domains due to their versatility in navigating three-dimensional spaces. The utilization of UAV swarms further enhances the efficiency of mission execution through collaborative operation and shared intelligence. This paper introduces a novel decentralized swarm control strategy for multi-UAV systems engaged in intercepting multiple dynamic targets. The proposed control framework leverages the advantages of both learning-based intelligent algorithms and rule-based control methods, facilitating complex task control in unknown environments while enabling adaptive and resilient coordination among UAV swarms. Moreover, dual flight modes are introduced to enhance mission robustness and fault tolerance, allowing UAVs to autonomously return to base in case of emergencies or upon task completion. Comprehensive simulation scenarios are designed to validate the effectiveness and scalability of the proposed control system under various conditions. Additionally, a feasibility analysis is conducted to guarantee real-world UAV implementation. The results demonstrate significant improvements in tracking performance, scheduling efficiency, and overall success rates compared to traditional methods. This research contributes to the advancement of autonomous UAV swarm coordination and specific applications in complex environments.
Citation: Drones
PubDate: 2024-07-29
DOI: 10.3390/drones8080350
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 351: Finite Time-Adaptive Full-State Quantitative
Control of Quadrotor Aircraft and QDrone Experimental Platform
Verification
Authors: He Li, Peng Luo, Zhiwei Li, Guoqiang Zhu, Xiuyu Zhang
First page: 351
Abstract: This paper proposes a novel adaptive finite-time controller for a quadrotor unmanned aerial vehicle (UAV) model with stochastic perturbations and parameter-unknown terms, under the constraints of a state-constrained system. The controller is designed based on full-state quantization, where the error system is defined to be a function of the quantized error signal. An adaptive method is employed to address the quadrotor UAV system model with nonlinear terms and unknown perturbations. The controller utilizes Barrier Lyapunov function (BLF) bounds with adaptive effective time performance to ensure full-state constraint of the system. The stability of the system is proven using Lyapunov’s stability theorem. The effectiveness of the designed full-state constrained controller for quadrotor UAV based on full-state quantization is verified through a physical experimental simulation platform.
Citation: Drones
PubDate: 2024-07-29
DOI: 10.3390/drones8080351
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 352: Research on a Distributed Cooperative Guidance
Law for Obstacle Avoidance and Synchronized Arrival in UAV Swarms
Authors: Xinyu Liu, Dongguang Li, Yue Wang, Yuming Zhang, Xing Zhuang, Hanyu Li
First page: 352
Abstract: In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions.
Citation: Drones
PubDate: 2024-07-29
DOI: 10.3390/drones8080352
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 353: A Survey on Artificial-Intelligence-Based
Internet of Vehicles Utilizing Unmanned Aerial Vehicles
Authors: Syed Ammad Ali Shah, Xavier Fernando, Rasha Kashef
First page: 353
Abstract: As Autonomous Vehicles continue to advance and Intelligent Transportation Systems are implemented globally, vehicular ad hoc networks (VANETs) are increasingly becoming a part of the Internet, creating the Internet of Vehicles (IoV). In an IoV framework, vehicles communicate with each other, roadside units (RSUs), and the surrounding infrastructure, leveraging edge, fog, and cloud computing for diverse tasks. These networks must support dynamic vehicular mobility and meet strict Quality of Service (QoS) requirements, such as ultra-low latency and high throughput. Terrestrial wireless networks often fail to satisfy these needs, which has led to the integration of Unmanned Aerial Vehicles (UAVs) into IoV systems. UAV transceivers provide superior line-of-sight (LOS) connections with vehicles, offering better connectivity than ground-based RSUs and serving as mobile RSUs (mRSUs). UAVs improve IoV performance in several ways, but traditional optimization methods are inadequate for dynamic vehicular environments. As a result, recent studies have been incorporating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into UAV-assisted IoV systems to enhance network performance, particularly in complex areas like resource allocation, routing, and mobility management. This survey paper reviews the latest AI/ML research in UAV-IoV networks, with a focus on resource and trajectory management and routing. It analyzes different AI techniques, their training features, and architectures from various studies; addresses the limitations of AI methods, including the demand for computational resources, availability of real-world data, and the complexity of AI models in UAV-IoV contexts; and considers future research directions in UAV-IoV.
Citation: Drones
PubDate: 2024-07-29
DOI: 10.3390/drones8080353
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 354: Safety Risk Modelling and Assessment of Civil
Unmanned Aircraft System Operations: A Comprehensive Review
Authors: Sen Du, Gang Zhong, Fei Wang, Bizhao Pang, Honghai Zhang, Qingyu Jiao
First page: 354
Abstract: Safety concerns are progressively emerging regarding the adoption of Unmanned Aircraft Systems (UASs) in diverse civil applications, particularly within the booming air transportation system, such as in Advanced Air Mobility. The outcomes of risk assessment determine operation authorization and mitigation strategies. However, civil UAS operations bring novel safety issues distinct from traditional aviation, like ground impact risk, etc. Existing studies vary in their risk definitions, modelling mechanisms, and objectives. There remains an incomplete gap of challenges, opportunities, and future efforts needed to collaboratively address diverse safety risks. This paper undertakes a comprehensive review of the literature in the domain, providing a summative understanding of the risk assessment of civil UAS operations. Specifically, four basic modelling approaches utilized commonly are identified comprising the safety risk management process, causal model, collision risk model, and ground risk model. Then, this paper reviews the state of the art in each category and explores the practical applications they contribute to, the support offered to participants from multiple stakeholders, and the primary technical challenges encountered. Moreover, potential directions for future work are outlined based on the high-level common problems. We believe that this review from a holistic perspective contributes towards better implementation of risk assessment in civil UAS operations, thus facilitating safe integration into the airspace system.
Citation: Drones
PubDate: 2024-07-29
DOI: 10.3390/drones8080354
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 355: Determining Quasi-Static Load Carrying Capacity
of Composite Sandwich Rotor Blades for Copter-Type Drones
Authors: Chien Wei Jan, Tai Yan Kam
First page: 355
Abstract: The development of light composite rotor blades with acceptable load carrying capacity is an essential issue to be dealt with in the design of relatively large copter-type drones. In this paper, a method is established to determine the quasi-static blade load carrying capacity which is vital to drone reliability. The proposed method, which provides a systematic procedure to determine blade load carrying capacity, consists of three parts, namely, a procedure to determine the distributed quasi-static blade aerodynamic load via the Blade Element Momentum (BEM) approach, a finite element-based failure analysis method to identify the actual blade failure mode, and an optimization method to determine the actual blade load carrying capacity. The experimental failure characteristics (failure mode, failure thrust, failure location) of two types of composite sandwich rotor blades with different skin lamination arrangements have been used to verify the accuracy of the theoretical results obtained using the proposed load carrying capacity determination method. The skin lamination arrangement for attaining the optimal blade-specific load carrying capacity and the blade incipient rotational speed for safe drone operation has been determined using the proposed method.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080355
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 356: Improved PSO-Based Two-Phase Logistics UAV Path
Planning under Dynamic Demand and Wind Conditions
Authors: Guangfu Tang, Tingyue Xiao, Pengfei Du, Peiying Zhang, Kai Liu, Lizhuang Tan
First page: 356
Abstract: Unmanned aerial vehicles (UAVs) have increasingly become integral to logistics and distribution due to their flexibility and mobility. However, the existing studies often overlook the dynamic nature of customer demands and wind conditions, limiting the practical applicability of their proposed strategies. To tackle this challenge, we firstly construct a time-slicing-based UAV path planning model that incorporates dynamic customer demands and wind impacts. Based on this model, a two-stage logistics UAV path planning framework is developed according to the analysis of the customer pool updates and dynamic attitudes. Secondly, a dynamic demand and wind-aware logistics UAV path planning problem is formulated to minimize the weighted average of the energy consumption and the customer satisfaction penalty cost, which comprehensively takes the energy consumption constraints, load weight constraints, and hybrid time window constraints into consideration. To solve this problem, an improved particle swarm optimization (PSO)-based multiple logistics UAV path planning algorithm is developed, which has good performance with fast convergence and better solutions. Finally, extensive simulation results verify that the proposed algorithm can not only adhere to the UAV’s maximum load and battery power constraints but also significantly enhance the loading efficiency and battery utilization rate. Particularly, compared to the genetic algorithm (GA), simulated annealing (SA), and traditional PSO strategies, our proposed algorithm achieves satisfactory solutions within a reasonable time frame and reduces the distribution costs by up to 9.82%.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080356
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 357: MobileAmcT: A Lightweight Mobile Automatic
Modulation Classification Transformer in Drone Communication Systems
Authors: Hongyun Fei, Baiyang Wang, Hongjun Wang, Ming Fang, Na Wang, Xingping Ran, Yunxia Liu, Min Qi
First page: 357
Abstract: With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080357
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 358: Exploiting Cascaded Channel Signature for
PHY-Layer Authentication in RIS-Enabled UAV Communication Systems
Authors: Changjian Qin, Mu Niu, Pinchang Zhang, Ji He
First page: 358
Abstract: Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communications face a critical security threat from impersonation attacks, where adversaries impersonate legitimate entities to infiltrate networks to obtain private data or unauthorized access. To combat such security threats, this paper proposes a novel physical layer (PHY-layer) authentication scheme for validating UAV identity in RIS-enabled UAV wireless networks. Considering that most existing works focus on traditional communication systems such as IoT and millimeter wave multiple-input multiple-output (MIMO) systems, there is currently no mature PHY-layer authentication scheme to serve RIS-UAV communication systems. To this end, our scheme leverages the unique characteristics of cascaded channels related to RIS to verify the legitimacy of UAV transmitting signals to the base station (BS). To be more precise, we first use the least squares estimate method and coordinate a descent-based algorithm to extract the cascaded channel feature. Next, we explore a quantizer to quantize the fluctuations of the channel gain that are related to the extracted channel feature. The 1-bit quantizer’s output findings are exploited to generate the authentication decision criteria, which are then tested using a binary hypothesis. The statistical signal processing technique is utilized to obtain the analytical formulations for detection and false alarm probabilities. We also conduct a computational complexity analysis of the proposed scheme. Finally, the numerical results validate the effectiveness of the proposed performance metric models and show that our detection performance can reach over 90% accuracy at a low signal-to-noise ratio (e.g., −8 dB), with a 10% improvement in detection accuracy compared with existing schemes.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080358
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 359: A Quantum-Resistant Identity Authentication and
Key Agreement Scheme for UAV Networks Based on Kyber Algorithm
Authors: Tao Xia, Menglin Wang, Jun He, Gang Yang, Linna Fan, Guoheng Wei
First page: 359
Abstract: Unmanned aerial vehicles (UAVs) play a critical role in various fields, including logistics, agriculture, and rescue operations. Effective identity authentication and key agreement schemes are vital for UAV networks to combat threats. Current schemes often employ algorithms like elliptic curve cryptography (ECC) and Rivest–Shamir–Adleman (RSA), which are vulnerable to quantum attacks. To address this issue, we propose LIGKYX, a novel scheme combining the quantum-resistant Kyber algorithm with the hash-based message authentication code (HMAC) for enhanced security and efficiency. This scheme enables the mutual authentication between UAVs and ground stations and supports secure session key establishment protocols. Additionally, it facilitates robust authentication and key agreement among UAVs through control stations, addressing the critical challenge of quantum-resistant security in UAV networks. The proposed LIGKYX scheme operates based on the Kyber algorithm and elliptic curve Diffie–Hellman (ECDH) key exchange protocol, employing the HMAC and pre-computation techniques. Furthermore, a formal verification tool validated the security of LIGKYX under the Dolev–Yao threat model. Comparative analyses on security properties, communication overhead, and computational overhead indicate that LIGKYX not only matches or exceeds existing schemes but also uniquely counters quantum attacks effectively, ensuring the security of UAV communication networks with a lower time overhead for authentication and communication.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080359
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 360: Evolution of Secondary Periglacial Environment
Induced by Thawing Permafrost near China–Russia Crude Oil Pipeline
Based on Airborne LiDAR, Geophysics, and Field Observation
Authors: Kai Gao, Guoyu Li, Fei Wang, Yapeng Cao, Dun Chen, Qingsong Du, Mingtang Chai, Alexander Fedorov, Juncen Lin, Yunhu Shang, Shuai Huang, Xiaochen Wu, Luyao Bai, Yan Zhang, Liyun Tang, Hailiang Jia, Miao Wang, Xu Wang
First page: 360
Abstract: The China–Russia crude oil pipeline (CRCOP) operates at a temperature that continuously thaws the surrounding permafrost, leading to secondary periglacial phenomena along the route. However, the evolution and formation mechanisms of these phenomena are still largely unknown. We used multi-temporal airborne light detection and ranging (LiDAR), geophysical, and field observation data to quantify the scale of ponding and icing, capture their dynamic development process, and reveal their development mechanisms. The results show that the average depth of ponding within 5 m on both sides of the pipeline was about 31 cm. The volumes of three icings (A–C) above the pipeline were 133 m3, 440 m3, and 186 m3, respectively. Icing development can be divided into six stages: pipe trench settlement, water accumulation in the pipe trench, ponding pressure caused by water surface freezing, the formation of ice cracks, water overflow, and icing. This study revealed the advantages of airborne LiDAR in monitoring the evolution of periglacial phenomena and provided a new insight on the development mechanisms of the phenomena by combining LiDAR with geophysics and field observation. The results of our study are of great significance for developing disaster countermeasures and ensuring the safe operation of buried pipelines.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080360
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 361: Foreign Object Detection Network for
Transmission Lines from Unmanned Aerial Vehicle Images
Authors: Bingshu Wang, Changping Li, Wenbin Zou, Qianqian Zheng
First page: 361
Abstract: Foreign objects such as balloons and nests often lead to widespread power outages by coming into contact with transmission lines. The manual detection of these is labor-intensive work. Automatic foreign object detection on transmission lines is a crucial task for power safety and is becoming the mainstream method, but the lack of datasets is a restriction. In this paper, we propose an advanced model termed YOLOv8 Network with Bidirectional Feature Pyramid Network (YOLOv8_BiFPN) to detect foreign objects on power transmission lines. Firstly, we add a weighted cross-scale connection structure to the detection head of the YOLOv8 network. The structure is bidirectional. It provides interaction between low-level and high-level features, and allows information to spread across feature maps of different scales. Secondly, in comparison to the traditional concatenation and shortcut operations, our method integrates information between different scale features through weighted settings. Moreover, we created a dataset of Foreign Object detection on Transmission Lines from a Drone-view (FOTL_Drone). It consists of 1495 annotated images with six types of foreign object. To our knowledge, FOTL_Drone stands out as the most comprehensive dataset in the field of foreign object detection on transmission lines, which encompasses a wide array of geographic features and diverse types of foreign object. Experimental results showcase that YOLOv8_BiFPN achieves an average precision of 90.2% and an mAP@.50 of 0.896 across various categories of foreign objects, surpassing other models.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080361
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 362: Enhancing Mission Planning of Large-Scale UAV
Swarms with Ensemble Predictive Model
Authors: Guanglei Meng, Mingzhe Zhou, Tiankuo Meng, Biao Wang
First page: 362
Abstract: Target assignment and trajectory planning are two crucial components of mission planning for unmanned aerial vehicle (UAV) swarms. In large-scale missions, the significance of planning efficiency becomes more pronounced. However, existing planning algorithms based on evolutionary computation and swarm intelligence face formidable challenges in terms of both efficiency and effectiveness. Additionally, the extensive trajectory planning involved is a significant factor affecting efficiency. Therefore, this paper proposes a dedicated method for large-scale mission planning. Firstly, to avoid extensive trajectory planning operations, this paper suggests utilizing a machine learning algorithm to establish a predictive model of trajectory length. To ensure predictive accuracy, an ensemble algorithm based on Gaussian process regression (GPR) is proposed. Secondly, to ensure the efficiency and effectiveness of target assignments in large-scale missions, this paper draws inspiration from a greedy search and proposes a simple yet effective target assignment algorithm. This algorithm can effectively handle a large number of decision variables and constraints involved in large-scale missions. Finally, we validated the effectiveness of the proposed method through 15 simulated missions of different scales. Among the 10 medium- to large-scale missions, our method achieved the best results in 9 of them, demonstrating the competitive advantage of our method in large-scale missions. Comparative results demonstrate the advantage of the proposed methods from both prediction and mission planning perspectives.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080362
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 363: Addressing Launch and Deployment Uncertainties
in UAVs with ESO-Based Attitude Control
Authors: Chao Yang, Xiaoru Cai, Liaoni Wu, Zhiming Guo
First page: 363
Abstract: This paper describes the design and implementation of a novel three-axis attitude control autopilot scheme for tube-launched, air-deployed UAVs. In early flight tests, various factors, such as model uncertainties during launch, aerodynamic uncertainties, geometric parameter changes during deployment, and significant uncertainties in booster rocket installation, exceeded the control capabilities of the attitude autopilot, causing flight instability. In order to address these issues, a numerical simulation model of the full launch process considering deviations was established based on early flight tests. A cascade attitude controller was then designed using an extended state observer (ESO), and the boundedness of control errors under unknown bounded disturbances was theoretically proven, providing requirements for the parameter tuning of the cascade controller. Comparative experiments and a second flight test both demonstrate that the ESO-based cascade attitude controller exhibits strong feedforward disturbance compensation under high-uncertainty conditions, effectively achieving stable control within the flight envelope.
Citation: Drones
PubDate: 2024-07-30
DOI: 10.3390/drones8080363
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 364: Assessing the Impact of Clearing and Grazing on
Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial
Vehicle Multispectral Data
Authors: Luís Pádua, João P. Castro, José Castro, Joaquim J. Sousa, Marina Castro
First page: 364
Abstract: Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the herbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearing and grazing, the individual application of each method, and a control scenario that was neither cleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetation dynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazing pressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation (r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements. These practices proved to be efficient in fuel management, with cleared and grazed areas showing a lower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the other hand, areas not subjected to any of these treatments presented rapid vegetation growth.
Citation: Drones
PubDate: 2024-07-31
DOI: 10.3390/drones8080364
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 365: Application of Deep Reinforcement Learning to
Defense and Intrusion Strategies Using Unmanned Aerial Vehicles in a
Versus Game
Authors: Chieh-Li Chen, Yu-Wen Huang, Ting-Ju Shen
First page: 365
Abstract: Drones are used in complex scenes in different scenarios. Efficient and effective algorithms are required for drones to track targets of interest and protect allied targets in a versus game. This study used physical models of quadcopters and scene engines to investigate the resulting performance of attacker drones and defensive drones based on deep reinforcement learning. The deep reinforcement learning network soft actor-critic was applied in association with the proposed reward and penalty functions according to the design scenario. AirSim UAV physical modeling and mission scenarios based on Unreal Engine were used to simultaneously train attacking and defending gaming skills for both drones, such that the required combat strategies and flight skills could be improved through a series of competition episodes. After 500 episodes of practice experience, both drones could accelerate, detour, and evade to achieve reasonably good performance with a roughly tie situation. Validation scenarios also demonstrated that the attacker–defender winning ratio also improved from 1:2 to 1.2:1, which is reasonable for drones with equal flight capabilities. Although this showed that the attacker may have an advantage in inexperienced scenarios, it revealed that the strategies generated by deep reinforcement learning networks are robust and feasible.
Citation: Drones
PubDate: 2024-07-31
DOI: 10.3390/drones8080365
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 366: Methodology and Uncertainty Analysis of Methane
Flux Measurement for Small Sources Based on Unmanned Aerial Vehicles
Authors: Degang Xu, Hongju Da, Chen Wang, Zhihe Tang, Hui Luan, Jufeng Li, Yong Zeng
First page: 366
Abstract: The top–down emission rate retrieval algorithm (TERRA) method for calculating the net flux out of a box has been employed by other researchers to assess large sources of methane release. This usually requires a manned aircraft drone with powerful performance to fly over the boundary layer. Few studies have focused on low-altitude box sampling mass balance methods for small sources of methane release, such as at maximum flight altitudes of less than 100 m. The accuracy and sources of uncertainty in such a method still need to be determined as they differ from the conditions of large sources. Nineteen flights were conducted to detect methane emissions from Chinese oil field well sites using a measurement system consisting of a quadcopter and methane, wind speed, wind direction, air pressure, and temperature sensors. The accuracy and uncertainty of the method are discussed. The average absolute relative error of the measurement is 18.5%, with an average uncertainty of 55.75%. The uncertainty is mainly caused by the wind speed and direction, and the background CH4 concentration. The main paths to reduce uncertainty and improve accuracy for low-altitude box sampling include subtracting the background concentration during flux retrieval, enhancing the accuracy of methane measurements, selecting a period of downwind dominant or wind direction change of less than 30 degrees, and ensuring a maximum flight height greater than 50 m with a horizontal distance from the pollution source center of less than 75 m. The results show that TERRA-based low-altitude box sampling is suitable for quantifying methane release rates from small sources.
Citation: Drones
PubDate: 2024-07-31
DOI: 10.3390/drones8080366
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 367: ERRT-GA: Expert Genetic Algorithm with Rapidly
Exploring Random Tree Initialization for Multi-UAV Path Planning
Authors: Hong Xu, Zijing Niu, Bo Jiang, Yuhang Zhang, Siji Chen, Zhiqiang Li, Mingke Gao, Miankuan Zhu
First page: 367
Abstract: In unmanned aerial vehicle (UAV) path planning, evolutionary algorithms are commonly used due to their ability to handle high-dimensional spaces and wide generality. However, traditional evolutionary algorithms have difficulty with population initialization and may fall into local optima. This paper proposes an improved genetic algorithm (GA) based on expert strategies, including a novel rapidly exploring random tree (RRT) initialization algorithm and a cross-variation process based on expert guidance and the wolf pack search algorithm. Experimental results on baseline functions in different scenarios show that the proposed RRT initialization algorithm improves convergence speed and computing time for most evolutionary algorithms. The expert guidance strategy helps algorithms jump out of local optima and achieve suboptimal solutions that should have converged. The ERRT-GA is tested for task assignment, path planning, and multi-UAV conflict detection, and it shows faster convergence, better scalability to high-dimensional spaces, and a significant reduction in task computing time compared to other evolutionary algorithms. The proposed algorithm outperforms most other methods and shows great potential for UAV path planning problems.
Citation: Drones
PubDate: 2024-08-01
DOI: 10.3390/drones8080367
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 368: UAV Confrontation and Evolutionary Upgrade
Based on Multi-Agent Reinforcement Learning
Authors: Xin Deng, Zhaoqi Dong, Jishiyu Ding
First page: 368
Abstract: Unmanned aerial vehicle (UAV) confrontation scenarios play a crucial role in the study of agent behavior selection and decision planning. Multi-agent reinforcement learning (MARL) algorithms serve as a universally effective method guiding agents toward appropriate action strategies. They determine subsequent actions based on the state of the agents and the environmental information that the agents receive. However, traditional MARL settings often result in one party agent consistently outperforming the other party due to superior strategies, or both agents reaching a strategic stalemate with no further improvement. To solve this issue, we propose a semi-static deep deterministic policy gradient algorithm based on MARL. This algorithm employs a centralized training and decentralized execution approach, dynamically adjusting the training intensity based on the comparative strengths and weaknesses of both agents’ strategies. Experimental results show that during the training process, the strategy of the winning team drives the losing team’s strategy to upgrade continuously, and the relationship between the winning team and the losing team keeps changing, thus achieving mutual improvement of the strategies of both teams. The semi-static reinforcement learning algorithm improves the win-loss relationship conversion by 8% and reduces the training time by 40% compared with the traditional reinforcement learning algorithm.
Citation: Drones
PubDate: 2024-08-01
DOI: 10.3390/drones8080368
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 369: Predicting Apple Tree Macronutrients Using
Unmanned Aerial Vehicle-Based Hyperspectral Imagery to Manage Apple
Orchard Nutrients
Authors: Ye Seong Kang, Chan Seok Ryu, Jung Gun Cho, Ki Su Park
First page: 369
Abstract: Herein, the development of an estimation model to measure the chlorophyll (Ch) and macronutrients, such as the total nitrogen (T-N), phosphorus (P), potassium (K), carbon (C), calcium (Ca), and magnesium (Mg), in apples is detailed, using key band ratios selected from hyperspectral imagery acquired with an unmanned aerial vehicle, for the management of nutrients in an apple orchard. The k-nearest neighbors regression (KNR) model for Ch and all macronutrients was chosen as the best model through a comparison of calibration and validation R2 values. As a result of model development, a total of 13 band ratios (425/429, 682/686, 710/714, 714/718, 718/722, 750/754, 754/758, 758/762, 762/766, 894/898, 898/902, 906/911, and 963/967) were selected for Ch and all macronutrients. The estimation potential for the T-N and Mg concentrations was low, with an R2 ≤ 0.37. The estimation performance for the other macronutrients was as follows: R2 ≥ 0.70 and RMSE ≤ 1.43 μg/cm2 for Ch; R2 ≥ 0.44 and RMSE ≤ 0.04% for P; R2 ≥ 0.53 and RMSE ≤ 0.23% for K; R2 ≥ 0.85 and RMSE ≤ 6.18% for C; and R2 ≥ 0.42 and RMSE ≤ 0.25% for Ca. Through establishing a fertilization strategy using the macronutrients estimated through hyperspectral imagery and measured soil chemical properties, this study presents a nutrient management decision-making method for apple orchards.
Citation: Drones
PubDate: 2024-08-01
DOI: 10.3390/drones8080369
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 370: Frequency-Modulated Continuous-Wave Radar
Perspectives on Unmanned Aerial Vehicle Detection and Classification: A
Primer for Researchers with Comprehensive Machine Learning Review and
Emphasis on Full-Wave Electromagnetic Computer-Aided Design Tools
Authors: Ahmed N. Sayed, Omar M. Ramahi, George Shaker
First page: 370
Abstract: Unmanned Aerial Vehicles (UAVs) represent a rapidly increasing technology with profound implications for various domains, including surveillance, security, and commercial applications. Among the number of detection and classification methodologies, radar technology stands as a cornerstone due to its versatility and reliability. This paper presents a comprehensive primer written specifically for researchers starting on investigations into UAV detection and classification, with a distinct emphasis on the integration of full-wave electromagnetic computer-aided design (EM CAD) tools. Commencing with an elucidation of radar’s pivotal role within the UAV detection paradigm, this primer systematically navigates through fundamental Frequency-Modulated Continuous-Wave (FMCW) radar principles, elucidating their intricate interplay with UAV characteristics and signatures. Methodologies pertaining to signal processing, detection, and tracking are examined, with particular emphasis placed on the pivotal role of full-wave EM CAD tools in system design and optimization. Through an exposition of relevant case studies and applications, this paper underscores successful implementations of radar-based UAV detection and classification systems while elucidating encountered challenges and insights obtained. Anticipating future trajectories, the paper contemplates emerging trends and potential research directions, accentuating the indispensable nature of full-wave EM CAD tools in propelling radar techniques forward. In essence, this primer serves as an indispensable roadmap, empowering researchers to navigate the complex terrain of radar-based UAV detection and classification, thereby fostering advancements in aerial surveillance and security systems.
Citation: Drones
PubDate: 2024-08-02
DOI: 10.3390/drones8080370
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 371: Pseudospectral-Based Rapid Trajectory Planning
and Feedforward Linearization Guidance
Authors: Lingxia Mu, Shaowei Cao, Ban Wang, Youmin Zhang, Nan Feng, Xiao Li
First page: 371
Abstract: A trajectory-based guidance strategy is proposed for the three-dimensional terminal return task of an uncrewed space vehicle (USV). The overall guidance scheme consists of reference trajectory planning and robust trajectory tracking modules. The trajectory planning algorithm involves determining the motion of the USV to achieve a prescribed target under multiple constraints. The altitude-domain-based USV model is firstly proven to be differentially flat utilizing the dynamic pressure and position of the USV as flat outputs. The original trajectory planning problem is reformulated in a lower-dimensional flat output space. The discretization of the planning problem is then achieved using the pseudospectral method, based on which an initial guess technique is designed in order to accelerate the solving speed of the planning algorithm. Subsequently, a feedforward linearization-based trajectory tracking guidance law is designed using the differential flatness property of the altitude-domain model. Simulation results in different scenarios show that the proposed guidance strategy provides a satisfactory guidance solution.
Citation: Drones
PubDate: 2024-08-02
DOI: 10.3390/drones8080371
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 372: Non-Line-of-Sight Positioning Method for
Ultra-Wideband/Miniature Inertial Measurement Unit Integrated System Based
on Extended Kalman Particle Filter
Authors: Chengzhi Hou, Wanqing Liu, Hongliang Tang, Jiayi Cheng, Xu Zhu, Mailun Chen, Chunfeng Gao, Guo Wei
First page: 372
Abstract: In the field of unmanned aerial vehicle (UAV) control, high-precision navigation algorithms are a research hotspot. To address the problem of poor localization caused by non-line-of-sight (NLOS) errors in ultra-wideband (UWB) systems, an UWB/MIMU integrated navigation method was developed, and a particle filter (PF) algorithm for data fusion was improved upon. The extended Kalman filter (EKF) was used to improve the method of constructing the importance density function (IDF) in the traditional PF, so that the particle sampling process fully considers the real-time measurement information, increases the sampling efficiency, weakens the particle degradation phenomenon, and reduces the UAV positioning error. We compared the positioning accuracy of the proposed extended Kalman particle filter (EKPF) algorithm with that of the EKF and unscented Kalman filter (UKF) algorithm used in traditional UWB/MIMU data fusion through simulation, and the results proved the effectiveness of the proposed algorithm through outdoor experiments. We found that, in NLOS environments, compared with pure UWB positioning, the accuracy of the EKPF algorithm in the X- and Y-directions was increased by 35% and 39%, respectively, and the positioning error in the Z-direction was considerably reduced, which proved the practicability of the proposed algorithm.
Citation: Drones
PubDate: 2024-08-03
DOI: 10.3390/drones8080372
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 373: Drone Arc Routing Problems and Metaheuristic
Solution Approach
Authors: Islam Altin, Aydin Sipahioglu
First page: 373
Abstract: The drone arc routing problem (DARP) is one of the arc routing problems (ARPs) that has been studied by researchers recently. Unlike traditional ARPs, drones can travel directly between any two points in the graph. Due to the flexibility of drones, it is possible to use edges not defined in the graphs when deadheading the edges. This advantage of drones makes this problem more challenging than any other ARP. With this study, the energy capacities of drones are considered in a DARP. Thus, a novel DARP called the drone arc routing problem with deadheading demand (DARP-DD) is addressed in this study. Drone capacities are used both when servicing the edges and when deadheading the edges in the DARP-DD. A special case of the DARP-DD, called the multiple service drone arc routing problem with deadheading demand (MS-DARP-DD), is also discussed, where some critical required edges may need to be served more than once. To solve these challenging problems, a simulated annealing algorithm is used, and the components of the algorithm are designed. Additionally, novel neighbor search operators are developed in this study. The computational results show that the proposed algorithm and its components are effective and useful in solving the DARP-DD and MS-DARP-DD.
Citation: Drones
PubDate: 2024-08-03
DOI: 10.3390/drones8080373
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 374: SLAKA-IoD: A Secure and Lightweight
Authentication and Key Agreement Protocol for Internet of Drones
Authors: Yuelei Xiao, Yu Tao
First page: 374
Abstract: The existing authentication and key agreement (AKA) schemes for the internet of drones (IoD) still suffer from various security attacks and fail to ensure required security properties. Moreover, drones generally have limited memory and computation capability. Motivated by these issues, a secure and lightweight AKA protocol for IoD (SLAKA-IoD) is proposed based on physical unclonable function (PUF), “exclusive or” (XOR) operation and hash function, which are simple cryptographic operations and functions that can provide better performance. In the SLAKA-IoD protocol, a drone and the ground station (GS) perform mutual authentication and establish a secure session key between them, and any two drones can also perform mutual authentication and establish a secure session key between them. Via informal security analysis, formal security analysis using the strand space model, and security verification based on the Scyther tool, the SLAKA-IoD protocol is proven to resist various security attacks and ensure required security properties. Further comparative analysis shows that the SLAKA-IoD protocol can provide more security features, and is generally lightweight as compared with these related AKA protocols for IoD, so it is suitable for IoD.
Citation: Drones
PubDate: 2024-08-04
DOI: 10.3390/drones8080374
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 375: Aerial Map-Based Navigation by Ground Object
Pattern Matching
Authors: Youngjoo Kim, Seungho Back, Dongchan Song, Byung-Yoon Lee
First page: 375
Abstract: This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into labeled points, and the objects’ configuration is used to find the corresponding location in the map database. Using the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern-matching problem. The pattern-matching algorithm proposed in this paper does not require altitude information or a camera model to estimate the horizontal geographical coordinates of the vehicle. Moreover, it requires significantly less storage because the map database is represented as a set of tuples, each consisting of a label, latitude, and longitude. Probabilistic data fusion with the inertial measurements by the Kalman filter is incorporated to deliver a comprehensive navigational solution. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments. The map-based navigation system successfully provides the position estimates with RMSEs within 3.5 m at heights over 90 m without the aid of the GNSS.
Citation: Drones
PubDate: 2024-08-05
DOI: 10.3390/drones8080375
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 376: Improving Aerial Targeting Precision: A Study
on Point Cloud Semantic Segmentation with Advanced Deep Learning
Algorithms
Authors: Salih Bozkurt, Muhammed Enes Atik, Zaide Duran
First page: 376
Abstract: The integration of technological advancements has significantly impacted artificial intelligence (AI), enhancing the reliability of AI model outputs. This progress has led to the widespread utilization of AI across various sectors, including automotive, robotics, healthcare, space exploration, and defense. Today, air defense operations predominantly rely on laser designation. This process is entirely dependent on the capability and experience of human operators. Considering that UAV systems can have flight durations exceeding 24 h, this process is highly prone to errors due to the human factor. Therefore, the aim of this study is to automate the laser designation process using advanced deep learning algorithms on 3D point clouds obtained from different sources, thereby eliminating operator-related errors. As different data sources, dense 3D point clouds produced with photogrammetric methods containing color information, and point clouds produced with LiDAR systems were identified. The photogrammetric point cloud data were generated from images captured by the Akinci UAV’s multi-axis gimbal camera system within the scope of this study. For the point cloud data obtained from the LiDAR system, the DublinCity LiDAR dataset was used for testing purposes. The segmentation of point cloud data utilized the PointNet++ and RandLA-Net algorithms. Distinct differences were observed between the evaluated algorithms. The RandLA-Net algorithm, relying solely on geometric features, achieved an approximate accuracy of 94%, while integrating color features significantly improved its performance, raising its accuracy to nearly 97%. Similarly, the PointNet++ algorithm, relying solely on geometric features, achieved an accuracy of approximately 94%. Notably, the model developed as a unique contribution in this study involved enriching the PointNet++ algorithm by incorporating color attributes, leading to significant improvements with an approximate accuracy of 96%. The obtained results demonstrate a notable improvement in the PointNet++ algorithm with the proposed approach. Furthermore, it was demonstrated that the methodology proposed in this study can be effectively applied directly to data generated from different sources in aerial scanning systems.
Citation: Drones
PubDate: 2024-08-06
DOI: 10.3390/drones8080376
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 377: Enhanced Trajectory Forecasting for Hypersonic
Glide Vehicle via Physics-Embedded Neural ODE
Authors: Shaoning Lu, Yue Qian
First page: 377
Abstract: Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit limitations in predictive accuracy and long-term forecasting. Whereas, physics-informed neural networks (PINNs) offer a solution by incorporating physical laws, but they treat these laws as constraints rather than fully integrating them into the learning process. This paper presents PhysNODE, a novel physics-embedded neural ODE model for the precise forecasting of HGV trajectories, which directly integrates the equations of HGV motion into a neural ODE. PhysNODE leverages a neural network to estimate the hidden aerodynamic parameters within these equations. These parameters are then combined with observable physical quantities to form a derivative function, which is fed into an ODE solver to predict the future trajectory. Comprehensive experiments using simulated datasets of HGV trajectories demonstrate that PhysNODE outperforms the state-of-the-art data-driven and physics-informed methods, particularly when training data is limited. The results highlight the benefit of embedding the physics of the HGV motion into the neural ODE for improved accuracy and stability in trajectory predicting.
Citation: Drones
PubDate: 2024-08-06
DOI: 10.3390/drones8080377
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 378: Reinforcement-Learning-Based Multi-UAV
Cooperative Search for Moving Targets in 3D Scenarios
Authors: Yifei Liu, Xiaoshuai Li, Jian Wang, Feiyu Wei, Junan Yang
First page: 378
Abstract: Most existing multi-UAV collaborative search methods only consider scenarios of two-dimensional path planning or static target search. To be close to the practical scenario, this paper proposes a path planning method based on an action-mask-based multi-agent proximal policy optimization (AM-MAPPO) algorithm for multiple UAVs searching for moving targets in three-dimensional (3D) environments. In particular, a multi-UAV high–low altitude collaborative search architecture is introduced that not only takes into account the extensive detection range of high-altitude UAVs but also leverages the benefit of the superior detection quality of low-altitude UAVs. The optimization objective of the search task is to minimize the uncertainty of the search area while maximizing the number of captured moving targets. The path planning problem for moving target search in a 3D environment is formulated and addressed using the AM-MAPPO algorithm. The proposed method incorporates a state representation mechanism based on field-of-view encoding to handle dynamic changes in neural network input dimensions and develops a rule-based target capture mechanism and an action-mask-based collision avoidance mechanism to enhance the AM-MAPPO algorithm’s convergence speed. Experimental results demonstrate that the proposed algorithm significantly reduces regional uncertainty and increases the number of captured moving targets compared to other deep reinforcement learning methods. Ablation studies further indicate that the proposed action mask mechanism, target capture mechanism, and collision avoidance mechanism of the AM-MAPPO algorithm can improve the algorithm’s effectiveness, target capture capability, and UAVs’ safety, respectively.
Citation: Drones
PubDate: 2024-08-06
DOI: 10.3390/drones8080378
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 379: A Heuristic Routing Algorithm for Heterogeneous
UAVs in Time-Constrained MEC Systems
Authors: Long Chen, Guangrui Liu, Xia Zhu, Xin Li
First page: 379
Abstract: The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage closer to the data sources. However, UAV heterogeneity and the time window constraints for task execution pose a significant challenge. This paper addresses the multiple heterogeneity UAV routing problem in MEC environments, modeling it as a multi-traveling salesman problem (MTSP) with soft time constraints. We propose a two-stage heuristic algorithm, heterogeneous multiple UAV routing (HMUR). The approach first identifies task areas (TAs) and optimal hovering positions for the UAVs and defines an effective fitness measurement to handle UAV heterogeneity. A novel scoring function further refines the path determination, prioritizing real-time task compliance to enhance Quality of Service (QoS). The simulation results demonstrate that our proposed HMUR method surpasses the existing baseline algorithms on multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments.
Citation: Drones
PubDate: 2024-08-06
DOI: 10.3390/drones8080379
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 380: Wind Tunnel Investigation of the Icing of a
Drone Rotor in Forward Flight
Authors: Derek Harvey, Eric Villeneuve, Mathieu Béland, Maxime Lapalme
First page: 380
Abstract: The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been performed to study the effects of ice accretion on rotor performance through a parametric study of different parameters, namely MVD, LWC, rotor speed, and pitch angle. This paper presents the last experimentations of this campaign for the drone rotor operating in forward flight under simulated icing conditions in a refrigerated, closed-loop wind tunnel. Results demonstrated that the different parameters studied greatly impacted the collection efficiency of the blades and thus, the resulting ice accretion. Smaller droplets were more easily influenced by the streamlines around the rotating blades, resulting in less droplets impacting the surface and thus slower ice accumulations. Higher rotation speeds and pitch angles generated more energetic streamlines, which again transported more droplets around the airfoils instead of them impacting on the surface, which also led to slower accumulation. Slower ice accumulation resulted in slower thrust losses, since the loss in performances can be directly linked to the amount of ice accreted. This research has not only allowed the obtainment of very insightful results on the effect of each test parameter on the ice accumulation, but it has also conducted the development of a unique test bench for UAV propellers. The new circular test sections along with the new instrumentation installed in and around the tunnel will allow the laboratory to be able to generate icing on various type of UAV in forward flight under representative atmospheric conditions.
Citation: Drones
PubDate: 2024-08-07
DOI: 10.3390/drones8080380
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 381: Joint Optimization of Relay Communication Rates
in Clustered Drones under Interference Conditions
Authors: Xinglong Gu, Guifen Chen, Guowei Wu, Chenghua Wen
First page: 381
Abstract: To address the issues of communication failure and inefficiency in clustered drone relay communication due to external malicious interference, this paper proposes a joint optimization method for relay communication rates under interference conditions for clustered drones. This method employs the following two-step processing framework: Firstly, the Discrete Soft Actor-Critic (DSAC) algorithm is used to train the relay drones for dynamic channel selection, effectively avoiding various types of interference. Simultaneously, the Bayesian optimization algorithm is applied to optimize the hyperparameters of the DSAC algorithm, further enhancing its performance. Subsequently, the modulation order, transmission power, trajectory of the relay drones, and power allocation factors of the clustered drones are jointly optimized. This complex problem is transformed into a convex subproblem for determining a solution, aiming to maximize the communication rate of the clustered drones. The simulation’s results demonstrate that the proposed algorithm exhibits excellent performances in terms of anti-interference capability, solution convergence, and stability. It effectively improves the mission efficiency of clustered drones under interference conditions and enhances their adaptability to dynamic environments.
Citation: Drones
PubDate: 2024-08-07
DOI: 10.3390/drones8080381
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 382: Multi-Unmanned Aerial Vehicle Confrontation in
Intelligent Air Combat: A Multi-Agent Deep Reinforcement Learning Approach
Authors: Jianfeng Yang, Xinwei Yang, Tianqi Yu
First page: 382
Abstract: Multiple unmanned aerial vehicle (multi-UAV) confrontation is becoming an increasingly important combat mode in intelligent air combat. The confrontation highly relies on the intelligent collaboration and real-time decision-making of the UAVs. Thus, a decomposed and prioritized experience replay (PER)-based multi-agent deep deterministic policy gradient (DP-MADDPG) algorithm has been proposed in this paper for the moving and attacking decisions of UAVs. Specifically, the confrontation is formulated as a partially observable Markov game. To solve the problem, the DP-MADDPG algorithm is proposed by integrating the decomposed and PER mechanisms into the traditional MADDPG. To overcome the technical challenges of the convergence to a local optimum and a single dominant policy, the decomposed mechanism is applied to modify the MADDPG framework with local and global dual critic networks. Furthermore, to improve the convergence rate of the MADDPG training process, the PER mechanism is utilized to optimize the sampling efficiency from the experience replay buffer. Simulations have been conducted based on the Multi-agent Combat Arena (MaCA) platform, wherein the traditional MADDPG and independent learning DDPG (ILDDPG) algorithms are benchmarks. Simulation results indicate that the proposed DP-MADDPG improves the convergence rate and the convergent reward value. During confrontations against the vanilla distance-prioritized rule-empowered and intelligent ILDDPG-empowered blue parties, the DP-MADDPG-empowered red party can improve the win rate to 96% and 80.5%, respectively.
Citation: Drones
PubDate: 2024-08-07
DOI: 10.3390/drones8080382
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 383: Equivalent Spatial Plane-Based Relative Pose
Estimation of UAVs
Authors: Hangyu Wang, Shuangyi Gong, Chaobo Chen, Jichao Li
First page: 383
Abstract: The accuracy of relative pose estimation is an important foundation for ensuring the safety and stability of autonomous aerial refueling (AAR) of unmanned aerial vehicles (UAV), and in response to this problem, a relative pose estimation method of UAVs based on the spatial equivalent plane is proposed in this paper. The UAV is equivalent to a spatial polygonal plane, and according to the measurement information of the Global Navigation Satellite System (GNSS) receivers, the equivalent polygonal plane equation is solved through the three-point normal vector and the minimum sum of squares of the distance from the four points to the plane. The equations of the distance between the geometric centers of the two polygonal planes, the angle between planes, and the angle between lines are used to calculate the relative pose information of the UAVs. Finally, the simulation environment and initial parameters are utilized for numerical simulation and results analysis. The simulation results show that without considering the motion model of the UAV, the proposed method can accurately estimate the relative pose information of the UAVs. In addition, in the presence of measurement errors, the relative pose estimation method based on the equivalent triangle plane can identify the position of the measurement point with the error, and the relative pose estimation method based on the equivalent quadrilateral plane has good robustness. The simulation results verify the feasibility and effectiveness of the proposed method.
Citation: Drones
PubDate: 2024-08-08
DOI: 10.3390/drones8080383
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 384: Multi-Type Task Assignment Algorithm for
Heterogeneous UAV Cluster Based on Improved NSGA-Ⅱ
Authors: Yunchong Zhu, Yangang Liang, Yingjie Jiao, Haipeng Ren, Kebo Li
First page: 384
Abstract: Cluster warfare, as a disruptive technology, leverages its numerical advantage to overcome limitations such as restricted task execution types and the low resilience of single platforms, embodying a significant trend in future unmanned combat. In scenarios where only the number of known targets and their vague locations within the region are available, UAV clusters are tasked with performing missions including close-range scout, target attack, and damage assessment for each target. Consequently, taking into account constraints such as assignment, payload, task time window, task sequencing, and range, a multi-objective optimization model for task assignment was formulated. Initially, optimization objectives were set as total mission completion time, total mission revenue, and cluster damage level. Subsequently, the concept of constraint tolerance was introduced to enhance the non-dominant sorting mechanism of NSGA-II by distinguishing individuals that fail to meet constraints, thereby enabling those violating constraints with high tolerance to be retained in the next generation to participate in further evolution, thereby resolving the difficulty of achieving a convergent Pareto solution set under complex interdependent task constraints. Finally, through comparisons, the superiority of the improved NSGA-II algorithm has been verified.
Citation: Drones
PubDate: 2024-08-08
DOI: 10.3390/drones8080384
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 385: A Mission Planning Method for Long-Endurance
Unmanned Aerial Vehicles: Integrating Heterogeneous Ground Control
Resource Allocation
Authors: Kai Li, Cheng Zhu, Xiaogang Pan, Long Xu, Kai Liu
First page: 385
Abstract: Long-endurance unmanned aerial vehicles (LE-UAVs) are extensively used due to their vast coverage and significant payload capacities. However, their limited autonomous intelligence necessitates the intervention of ground control resources (GCRs), which include one or more operators, during mission execution. The performance of these missions is notably affected by the varying effectiveness of different GCRs and their fatigue levels. Current research on multi-UAV mission planning inadequately addresses these critical factors. To tackle this practical issue, we present an integrated optimization problem for multi-LE-UAV mission planning combined with heterogeneous GCR allocation. This problem extends traditional multi-UAV cooperative mission planning by incorporating GCR allocation decisions. The coupling of mission planning decisions with GCR allocation decisions increases the dimensionality of the decision space, rendering the problem more complex. By analyzing the problem’s characteristics, we develop a mixed-integer linear programming model. To effectively solve this problem, we propose a bilevel programming algorithm based on a hybrid genetic algorithm framework. Numerical experiments demonstrate that our proposed algorithm effectively solves the problem, outperforming the advanced optimization toolkit CPLEX. Remarkably, for larger-scale instances, our algorithm achieves superior solutions within 10 s compared with CPLEX’s 2 h runtime.
Citation: Drones
PubDate: 2024-08-08
DOI: 10.3390/drones8080385
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 386: A General Method for Pre-Flight Preparation in
Data Collection for Unmanned Aerial Vehicle-Based Bridge Inspection
Authors: Pouya Almasi, Yangjian Xiao, Roshira Premadasa, Jonathan Boyle, David Jauregui, Zhe Wan, Qianyun Zhang
First page: 386
Abstract: Unmanned Aerial Vehicles (UAVs) have garnered significant attention in recent years due to their unique features. Utilizing UAVs for bridge inspection offers a promising solution to overcome challenges associated with traditional methods. While UAVs present considerable advantages, there are challenges associated with their use in bridge inspection, particularly in ensuring effective data collection. The primary objective of this study is to tackle the challenges related to data collection in bridge inspection using UAVs. A comprehensive method for pre-flight preparation in data collection is proposed. A well-structured flowchart has been created, covering crucial steps, including identifying the inspection purpose, selecting appropriate hardware, planning and optimizing flight paths, and calibrating sensors. The method has been tested in two case studies of bridge inspections in the State of New Mexico. The results show that the proposed method represents a significant advancement in utilizing UAVs for bridge inspection. These results indicate improvements in accuracy from 7.19% to 21.57% in crack detection using the proposed data collection method. By tackling the data collection challenges, the proposed method serves as a foundation for the application of UAVs for bridge inspection.
Citation: Drones
PubDate: 2024-08-09
DOI: 10.3390/drones8080386
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 387: Improved Nonlinear Model Predictive Control
Based Fast Trajectory Tracking for a Quadrotor Unmanned Aerial Vehicle
Authors: Hongyue Ma, Yufeng Gao, Yongsheng Yang, Shoulin Xu
First page: 387
Abstract: This article studies a nonlinear model predictive control (NMPC) scheme for the trajectory tracking efficiency of a quadcopter UAV. A cost function is first proposed that incorporates weighted increments of control forces in each direction, followed by a weighted summation. Furthermore, a contraction constraint for the cost function is introduced based on the numerical convergence of the system for the sampling period of the UAV control force. Then, an NMPC scheme based on improved continuous/generalized minimum residuals (C/GMRES) is proposed to obtain acceptable control performance and reduce computational complexity. The proposed control scheme achieves efficient and smooth tracking control of the UAV while guaranteeing the closed-loop stability of the system. Finally, simulation results are presented to illustrate the effectiveness and superior performance of the proposed NMPC control scheme.
Citation: Drones
PubDate: 2024-08-09
DOI: 10.3390/drones8080387
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 388: Atmospheric Aircraft Conceptual Design Based on
Multidisciplinary Optimization with Differential Evolution Algorithm and
Neural Networks
Authors: Oleg Lukyanov, Van Hung Hoang, Evgenii Kurkin, Jose Gabriel Quijada-Pioquinto
First page: 388
Abstract: A methodology for selecting rational parameters of atmospheric aircraft during the initial design stages using a differential evolutionary optimization algorithm and numerical mathematical modeling of aerodynamics problems is proposed. The technique involves implementing weight and aerodynamic balance in the main flight modes, considering atmospheric aircraft with one or two lifting surfaces, applying parallel calculations, and auto-generating a three-dimensional geometric model of the aircraft’s appearance based on the optimization results. A method for accelerating the process of optimizing aircraft parameters in terms of takeoff weight by more than three times by introducing an objective function into the set of design variables is proposed and demonstrated. The reliability of mathematical models used in aerodynamics and the accuracy of the objective function calculation considering various constraints are explored. A comprehensive test of the performance and efficiency of the methodology is conducted by solving demonstration problems to optimize more than ten main design parameters for the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources.
Citation: Drones
PubDate: 2024-08-09
DOI: 10.3390/drones8080388
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 389: A Two-Step Controller for Vision-Based
Autonomous Landing of a Multirotor with a Gimbal Camera
Authors: Sangbaek Yoo, Jae-Hyeon Park, Dong Eui Chang
First page: 389
Abstract: This article presents a novel vision-based autonomous landing method utilizing a multirotor and a gimbal camera, which is designed to be applicable from any initial position within a broad space by addressing the problems of a field of view and singularity to ensure stable performance. The proposed method employs a two-step controller based on integrated dynamics for the multirotor and the gimbal camera, where the multirotor approaches the landing site horizontally in the first step and descends vertically in the second step. The multirotor and the camera converge simultaneously to the desired configuration because we design the stabilizing controller for the integrated dynamics of the multirotor and the gimbal camera. The controller requires only one feature point and decreases unnecessary camera rolling. The effectiveness of the proposed method is demonstrated through simulation and real environment experiments.
Citation: Drones
PubDate: 2024-08-09
DOI: 10.3390/drones8080389
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 390: Dark-SLAM: A Robust Visual Simultaneous
Localization and Mapping Pipeline for an Unmanned Driving Vehicle in a
Dark Night Environment
Authors: Jie Chen, Yan Wang, Pengshuai Hou, Xingquan Chen, Yule Shao
First page: 390
Abstract: Visual Simultaneous Localization and Mapping (VSLAM) is significant in unmanned driving, being is used to locate vehicles and create environmental maps, and provides a basis for navigation and decision making. However, in inevitable dark night environments, the SLAM system still suffers from a decline in robustness and accuracy. In this regard, this paper proposes a VSLAM pipeline called DarkSLAM. The pipeline comprises three modules: Camera Attribute Adjustment (CAA), Image Quality Enhancement (IQE), and Pose Estimation (PE). The CAA module carefully studies the strategies used for setting the camera parameters in low-illumination environments, thus improving the quality of the original images. The IQE module performs noise-suppressed image enhancement for the purpose of improving image contrast and texture details. In the PE module, a lightweight feature extraction network is constructed and performs pseudo-supervised training on low-light datasets to achieve efficient and robust data association to obtain the pose. Through experiments on low-light public datasets and real-world experiments in the dark, the necessity of the CAA and IQE modules and the parameter coupling between these modules are verified, and the feasibility of DarkSLAM is finally verified. In particular, the scene in the experiment NEU-4am has no artificial light (the illumination in this scene is between 0.01 and 0.08 lux) and the DarkSLAM achieved an accuracy of 5.2729 m at a distance of 1794.33 m.
Citation: Drones
PubDate: 2024-08-12
DOI: 10.3390/drones8080390
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 391: Convolutional Neural Network and Ensemble
Learning-Based Unmanned Aerial Vehicles Radio Frequency Fingerprinting
Identification
Authors: Yunfei Zheng, Xuejun Zhang, Shenghan Wang, Weidong Zhang
First page: 391
Abstract: With the rapid development of the unmanned aerial vehicles (UAVs) industry, there is increasing demand for UAV surveillance technology. Automatic Dependent Surveillance-Broadcast (ADS-B) provides accurate monitoring of UAVs. However, the system cannot encrypt messages or verify identity. To address the issue of identity spoofing, radio frequency fingerprinting identification (RFFI) is applied for ADS-B transmitters to determine the true identities of UAVs through physical layer security technology. This paper develops an ensemble learning ADS-B radio signal recognition framework. Firstly, the research analyzes the data content characteristics of the ADS-B signal and conducts segment processing to eliminate the possible effects of the signal content. To extract features from different signal segments, a method merging end-to-end and non-end-to-end data processing is approached in a convolutional neural network. Subsequently, these features are fused through EL to enhance the robustness and generalizability of the identification system. Finally, the proposed framework’s effectiveness is evaluated using collected ADS-B data. The experimental results indicate that the recognition accuracy of the proposed ELWAM-CNN method can reach up to 97.43% and have better performance at different signal-to-noise ratios compared to existing methods using machine learning.
Citation: Drones
PubDate: 2024-08-13
DOI: 10.3390/drones8080391
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 392: Novel Twist Morphing Aileron and Winglet Design
for UAS Control and Performance
Authors: Mir Hossein Negahban, Musavir Bashir, Clovis Priolet, Ruxandra Mihaela Botez
First page: 392
Abstract: This study introduces a novel “twist morphing aileron and winglet” design for the Unmanned Aircraft System UAS-S45. Improving rolling efficiency through twist morphing ailerons and reducing induced drag through twist morphing winglets are the two main objectives of this study. A novel wing design is introduced, and a high-fidelity gradient-based aerodynamic shape optimization is performed for twist morphing ailerons and twist morphing winglets, separately, with specified objective functions. The twist morphing aileron is then compared to the conventional hinged aileron configuration in terms of rolling efficiency and other aerodynamic properties, in particular aircraft maneuverability. The results for twist morphing ailerons show that the novel morphing design increases the aileron efficiency by 34% compared to the conventional design and reduces induced drag by 61%. Next, twist morphing winglets are studied regarding the induced drag in cruise and climb flight conditions. The results for twist morphing winglets indicate that the novel design reduces induced drag by 25.7% in cruise flight and up to 16.51% in climb; it also decreases the total drag by up to 7.5% and increases aerodynamic efficiency by up to 9%.
Citation: Drones
PubDate: 2024-08-13
DOI: 10.3390/drones8080392
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 393: Multiple Unmanned Aerial Vehicle (multi-UAV)
Reconnaissance and Search with Limited Communication Range Using Semantic
Episodic Memory in Reinforcement Learning
Authors: Boquan Zhang, Tao Wang, Mingxuan Li, Yanru Cui, Xiang Lin, Zhi Zhu
First page: 393
Abstract: Unmanned Aerial Vehicles (UAVs) have garnered widespread attention in reconnaissance and search operations due to their low cost and high flexibility. However, when multiple UAVs (multi-UAV) collaborate on these tasks, a limited communication range can restrict their efficiency. This paper investigates the problem of multi-UAV collaborative reconnaissance and search for static targets with a limited communication range (MCRS-LCR). To address communication limitations, we designed a communication and information fusion model based on belief maps and modeled MCRS-LCR as a multi-objective optimization problem. We further reformulated this problem as a decentralized partially observable Markov decision process (Dec-POMDP). We introduced episodic memory into the reinforcement learning framework, proposing the CNN-Semantic Episodic Memory Utilization (CNN-SEMU) algorithm. Specifically, CNN-SEMU uses an encoder–decoder structure with a CNN to learn state embedding patterns influenced by the highest returns. It extracts semantic features from the high-dimensional map state space to construct a smoother memory embedding space, ultimately enhancing reinforcement learning performance by recalling the highest returns of historical states. Extensive simulation experiments demonstrate that in reconnaissance and search tasks of various scales, CNN-SEMU surpasses state-of-the-art multi-agent reinforcement learning methods in episodic rewards, search efficiency, and collision frequency.
Citation: Drones
PubDate: 2024-08-14
DOI: 10.3390/drones8080393
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 394: Quality and Efficiency of Coupled Iterative
Coverage Path Planning for the Inspection of Large Complex 3D Structures
Authors: Xiaodi Liu, Minnan Piao, Haifeng Li, Yaohua Li, Biao Lu
First page: 394
Abstract: To enable unmanned aerial vehicles to generate coverage paths that balance inspection quality and efficiency when performing three-dimensional inspection tasks, we propose a quality and efficiency coupled iterative coverage path planning (QECI-CPP) method. First, starting from a cleaned and refined mesh model, this was segmented into narrow and normal spaces, each with distinct constraint settings. During the initialization phase of viewpoint generation, factors such as image resolution and orthogonality degree were considered to enhance the inspection quality along the path. Then, the optimization objective was designed to simultaneously consider inspection quality and efficiency, with the relative importance of these factors adjustable according to specific task requirements. Through iterative adjustments and optimizations, the coverage path was continuously refined. In numerical simulations, the proposed method was compared with three other classic methods, evaluated across five aspects: image resolution, orthogonality degree, path distance, computation time, and total path cost. The comparative simulation results show that the QECI-CPP achieves maximum image resolution and orthogonality degree while maintaining inspection efficiency within a moderate computation time, demonstrating the effectiveness of the proposed method. Additionally, the flexibility of the planned path is validated by adjusting the weight coefficient in the optimized objective function.
Citation: Drones
PubDate: 2024-08-14
DOI: 10.3390/drones8080394
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 395: Studies on V-Formation and Echelon Flight
Utilizing Flapping-Wing Drones
Authors: Joseph Martinez-Ponce, Brenden Herkenhoff, Ahmed Aboelezz, Cameron Urban, Sophie Armanini, Elie Raphael, Mostafa Hassanalian
First page: 395
Abstract: V-Formation and echelon formation flights can be seen used by migratory birds throughout the year and have left many scientists wondering why they choose very specific formations. Experiments and analytical studies have been completed on the topic of the formation flight of birds and have shown that migratory birds benefit aerodynamically by using these formations. However, many of these studies were completed using fixed-wing models, while migratory birds both flap and glide while in formation. This paper reports the design of and experiments with a flapping-wing model rather than only a fixed-wing model. In order to complete this study, two different approaches were used to generate a flapping-wing model. The first was a computational study using an unsteady vortex–lattice (UVLM) solver to simulate flapping bodies. The second was an experimental design using both custom-built flapping mechanisms and commercially bought flapping drones. The computations and various experimental trials confirmed that there is an aerodynamic benefit from flying in either V-formation or echelon flight while flapping. It is shown that each row of birds experiences an increase in aerodynamic performance based on positioning within the formation.
Citation: Drones
PubDate: 2024-08-15
DOI: 10.3390/drones8080395
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 396: A Mass, Fuel, and Energy Perspective on
Fixed-Wing Unmanned Aerial Vehicle Scaling
Authors: Carlos M. A. Diogo, Edgar C. Fernandes
First page: 396
Abstract: Fixed-Wing Unmanned Aerial Vehicles (UAVs) have been improving significantly in application and versatility, sharing design similarities with airplanes, particularly at the design stage, when the take-off mass is used to estimate other characteristics. In this work, an internal database of UAVs is built to allow their comparison with airplanes under different parameters and assess key differences in patterns across UAV powertrains. The existing literature on speed vs. take-off mass is updated with 534 UAV entries, and a range vs. take-off mass diagram is created with 503 UAVs and 193 airplanes. Additionally, different transportation efficiency metrics are compared between UAVs and airplanes, highlighting scenarios advantageous for UAVs. A new paradigm focused on useful energy is then used to understand the underlying effectiveness of UAV implementations. Increasing useful energy is more effective in increasing the speed, transport work, and surveying work of internal combustion UAVs and more effective in increasing the range and endurance of battery-electric UAVs. Finally, it is observed that the mass of all fixed-wing aerial vehicles, both UAVs and airplanes, except for battery electric and solar, adheres to a well-defined scaling law based on useful energy. A parallel to this scaling law is suggested to describe future battery-electric UAVs and airplanes.
Citation: Drones
PubDate: 2024-08-15
DOI: 10.3390/drones8080396
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 397: Unmanned Aerial Vehicle Obstacle Avoidance
Based Custom Elliptic Domain
Authors: Yong Liao, Yuxin Wu, Shichang Zhao, Dan Zhang
First page: 397
Abstract: The velocity obstacles (VO) method is widely employed in real-time obstacle avoidance research for UAVs due to its succinct mathematical foundation and rapid, dynamic planning abilities. Traditionally, VO assumes a circle protection domain with a fixed radius, leading to issues such as excessive conservatism of obstacle avoidance areas, longer detour paths, and unnecessary avoidance angles. To overcome these challenges, this paper firstly reviews the fundamentals and pre-existing defects of the VO methodology. Next, we explore a scenario involving UAVs in head-on conflicts and introduce an elliptic velocity obstacle method tailored to the UAV’s current flight state. This method connects the protection domain size directly to the UAV’s flight state, transitioning from the conventional circle domain to a more efficient elliptic domain. Additionally, to manage the computational demands of Minkowski sums and velocity obstacle cones, an approximation algorithm for discretizing elliptic boundary points is introduced. A strategy to mitigate unilateral velocity oscillation had is developed. Comparative validation simulations in MATLAB R2022a confirm that, based on the experimental results for the first 10 s, the apex angle of the velocity obstacle cone for the elliptical domain is, on average, reduced by 0.1733 radians compared to the circular domain per unit simulation time interval, saving an airspace area of 13,292 square meters and reducing the detour distance by 14.92 m throughout the obstacle avoidance process, facilitating navigation in crowded situations and improving airspace utilization.
Citation: Drones
PubDate: 2024-08-15
DOI: 10.3390/drones8080397
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 398: Combining Drone LiDAR and Virtual Reality
Geovisualizations towards a Cartographic Approach to Visualize Flooding
Scenarios
Authors: Ermioni Eirini Papadopoulou, Apostolos Papakonstantinou
First page: 398
Abstract: This study aims to create virtual reality (VR) geovisualizations using 3D point clouds obtained from airborne LiDAR technology. These visualizations were used to map the current state of river channels and tributaries in the Thessalian Plain, Greece, following severe flooding in the summer of 2023. The study area examined in this paper is the embankments enclosing the tributaries of the Pineios River in the Thessalian Plain region, specifically between the cities of Karditsa and Trikala in mainland Greece. This area was significantly affected in the summer of 2023 when flooding the region’s rivers destroyed urban elements and crops. The extent of the impact across the entire Thessalian Plain made managing the event highly challenging to the authorities. High-resolution 3D mapping and VR geovisualization of the embarkments encasing the main rivers and the tributaries of the Thessalian Plain essentially provides information for planning the area’s restoration processes and designing prevention and mitigation measures for similar disasters. The proposed methodology consists of four stages. The first and second stages of the methodology present the design of the data acquisition process with airborne LiDAR, aiming at the high-resolution 3D mapping of the sites. The third stage focuses on data processing, cloud point classification, and thematic information creation. The fourth stage is focused on developing the VR application. The VR application will allow users to immerse themselves in the study area, observe, and interact with the existing state of the embankments in high resolution. Additionally, users can interact with the 3D point cloud, where thematic information is displayed describing the classification of the 3D cloud, the altitude, and the RGB color. Additional thematic information in vector form, providing qualitative characteristics, is also illustrated in the virtual space. Furthermore, six different scenarios were visualized in the 3D space using a VR app. Visualizing these 3D scenarios using digital twins of the current antiflood infrastructure provides scenarios of floods at varying water levels. This study aims to explore the efficient visualization of thematic information in 3D virtual space. The goal is to provide an innovative VR tool for managing the impact on anthropogenic infrastructures, livestock, and the ecological capital of various scenarios of a catastrophic flood.
Citation: Drones
PubDate: 2024-08-15
DOI: 10.3390/drones8080398
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 399: Risk Assessment and Distribution Estimation for
UAV Operations with Accurate Ground Feature Extraction Based on a
Multi-Layer Method in Urban Areas
Authors: Suyu Zhou, Yang Liu, Xuejun Zhang, Hailong Dong, Weizheng Zhang, Hua Wu, Hao Li
First page: 399
Abstract: In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk map is constructed with a multi-layer method considering the comprehensive risk imposed by UAV operations. The urban ground feature extraction is first implemented by employing a K-Means clustering method to an actual satellite image. Five main categories of the ground features are classified, each of which is composed of several sub-categories. Three more layers are then obtained, which are a population density layer, a sheltering factor layer, and a ground obstacle layer. As a result, a three-dimensional (3D) risk map is formed with a high resolution of 1 m × 1 m × 5 m. For each unit in this risk map, three kinds of risk imposed by UAV operations are taken into account and calculated, which include the risk to pedestrians, risk to ground vehicles, and risk to ground properties. This paper also develops a method of the resolution conversion to accommodate different UAV operation requirements. Case study results indicate that the risk levels between the fifth and tenth layers of the generated 3D risk map are relatively low, making these altitudes quite suitable for UAV operations.
Citation: Drones
PubDate: 2024-08-15
DOI: 10.3390/drones8080399
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 400: DFLM-YOLO: A Lightweight YOLO Model with
Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery
Authors: Chen Sun, Yihong Zhang, Shuai Ma
First page: 400
Abstract: Object detection algorithms for open water aerial images present challenges such as small object size, unsatisfactory detection accuracy, numerous network parameters, and enormous computational demands. Current detection algorithms struggle to meet the accuracy and speed requirements while being deployable on small mobile devices. This paper proposes DFLM-YOLO, a lightweight small-object detection network based on the YOLOv8 algorithm with multiscale feature fusion. Firstly, to solve the class imbalance problem of the SeaDroneSee dataset, we propose a data augmentation algorithm called Small Object Multiplication (SOM). SOM enhances dataset balance by increasing the number of objects in specific categories, thereby improving model accuracy and generalization capabilities. Secondly, we optimize the backbone network structure by implementing Depthwise Separable Convolution (DSConv) and the newly designed FasterBlock-CGLU-C2f (FC-C2f), which reduces the model’s parameters and inference time. Finally, we design the Lightweight Multiscale Feature Fusion Network (LMFN) to address the challenges of multiscale variations by gradually fusing the four feature layers extracted from the backbone network in three stages. In addition, LMFN incorporates the Dilated Re-param Block structure to increase the effective receptive field and improve the model’s classification ability and detection accuracy. The experimental results on the SeaDroneSee dataset indicate that DFLM-YOLO improves the mean average precision (mAP) by 12.4% compared to the original YOLOv8s, while reducing parameters by 67.2%. This achievement provides a new solution for Unmanned Aerial Vehicles (UAVs) to conduct object detection missions in open water efficiently.
Citation: Drones
PubDate: 2024-08-16
DOI: 10.3390/drones8080400
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 401: Optimizing AoI in IoT Networks: UAV-Assisted
Data Processing Framework Integrating Cloud–Edge Computing
Authors: Mingfang Ma, Zhengming Wang
First page: 401
Abstract: Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios.
Citation: Drones
PubDate: 2024-08-16
DOI: 10.3390/drones8080401
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 402: Unveiling Potential Industry Analytics Provided
by Unmanned Aircraft System Remote Identification: A Case Study Using
Aeroscope
Authors: Ryan J. Wallace, Stephen Rice, Sang-A Lee, Scott R. Winter
First page: 402
Abstract: The rapid proliferation of unmanned aircraft systems (UAS), commonly known as drones, across various industries, government applications, and recreational use necessitates a deeper understanding of their utilization and market trends. This research leverages UAS detection technology—specifically DJI’s Aeroscope—to track serial numbers and predict product usage, market penetration, and population estimation. By analyzing three years of data from Aeroscope sensors deployed around a major airport in the Southern United States, this study provides valuable insights into UAS operational patterns and platform lifespans. The findings reveal trends in platform utilization, the impact of new product releases, and the decline in legacy platform use. This offers critical data for informed decision-making in market trends, product development, and resource allocation.
Citation: Drones
PubDate: 2024-08-16
DOI: 10.3390/drones8080402
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 403: Meta Surface-Based Multiband MIMO Antenna for
UAV Communications at mm-Wave and Sub-THz Bands
Authors: Tale Saeidi, Sahar Saleh, Nick Timmons, Ahmed Jamal Abdullah Al-Gburi, Saeid Karamzadeh, Ayman A. Althuwayb, Nasr Rashid, Khaled Kaaniche, Ahmed Ben Atitallah, Osama I. Elhamrawy
First page: 403
Abstract: Unmanned aerial vehicles (UAVs) need high data rate connectivity, which is achievable through mm-waves and sub-THz bands. The proposed two-port leaky wave MIMO antenna, employing a coplanar proximity technique that combines capacitive and inductive loading, addresses this need. Featuring mesh-like slots and a vertical slot to mitigate open-stopband (OSB) issues, the antenna radiates broadside and bidirectionally. H-shaped slots on a strip enhance port isolation, and a coffee bean metasurface (MTS) boosts radiation efficiency and gain. Simulations and experiments considering various realistic scenarios, each at varying vertical and horizontal distances, show steered beam patterns, circular polarization (CP), and high-gain properties, with a maximum gain of 13.8 dBi, an axial ratio (AR) <2.9, a diversity gain (DG) >9.98 dB, and an envelope correlation coefficient (ECC) <0.003. This design supports drones-to-ground (D2G), drone-to-drone (D2D), and drone-to-satellite (D2S) communications.
Citation: Drones
PubDate: 2024-08-16
DOI: 10.3390/drones8080403
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 404: Identification of Pine Wilt-Diseased Trees
Using UAV Remote Sensing Imagery and Improved PWD-YOLOv8n Algorithm
Authors: Jianyi Su, Bingxi Qin, Fenggang Sun, Peng Lan, Guolin Liu
First page: 404
Abstract: Pine wilt disease (PWD) is one of the most destructive diseases for pine trees, causing a significant effect on ecological resources. The identification of PWD-infected trees is an effective approach for disease control. However, the effects of complex environments and the multi-scale features of PWD trees hinder detection performance. To address these issues, this study proposes a detection model based on PWD-YOLOv8 by utilizing aerial images. In particular, the coordinate attention (CA) and convolutional block attention module (CBAM) mechanisms are combined with YOLOv8 to enhance feature extraction. The bidirectional feature pyramid network (BiFPN) structure is used to strengthen feature fusion and recognition capability for small-scale diseased trees. Meanwhile, the lightweight FasterBlock structure and efficient multi-scale attention (EMA) mechanism are employed to optimize the C2f module. In addition, the Inner-SIoU loss function is introduced to seamlessly improve model accuracy and reduce missing rates. The experiment showed that the proposed PWD-YOLOv8n algorithm outperformed conventional target-detection models on the validation set (mAP@0.5 = 94.3%, precision = 87.9%, recall = 87.0%, missing rate = 6.6%; model size = 4.8 MB). Therefore, the proposed PWD-YOLOv8n model demonstrates significant superiority in diseased-tree detection. It not only enhances detection efficiency and accuracy but also provides important technical support for forest disease control and prevention.
Citation: Drones
PubDate: 2024-08-18
DOI: 10.3390/drones8080404
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 405: Multi-Unmanned Aerial Vehicles Cooperative
Trajectory Optimization in the Approaching Stage Based on the Attitude
Correction Algorithm
Authors: Haoran Shi, Junyong Lu, Kai Li, Pengfei Wu, Yun Guo
First page: 405
Abstract: This study investigated the problem of multi-UAVs cooperative trajectory optimization for remote maritime targets in the approach phase. First, based on the precise location information of the cooperative target, a real-time algorithm for correcting UAV attitude angles is proposed to reduce the impact of UAV attitude angle errors and observation system errors on target positioning accuracy. Then, the attitude correction algorithm is integrated into the interacting multiple model-cubature information filter (IMM-CIF) algorithm to achieve the fusion of multi-UAVs observation information. Furthermore, an improved receding horizon optimization (RHO) method is employed to plan the cooperative observation trajectories for UAVs in real time at the target approaching stage. Finally, numerical simulations are conducted to examine the proposed attitude correction and trajectory optimization algorithm, verifying the effectiveness of the proposed method and enhancing the tracking accuracy of the remote target.
Citation: Drones
PubDate: 2024-08-19
DOI: 10.3390/drones8080405
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 406: A Robust Hybrid Iterative Learning Formation
Strategy for Multi-Unmanned Aerial Vehicle Systems with Multi-Operating
Modes
Authors: Song Yang, Wenshuai Yu, Zhou Liu, Fei Ma
First page: 406
Abstract: This paper investigates the formation control problem of multi-unmanned aerial vehicle (UAV) systems with multi-operating modes. While mode switching enhances the flexibility of multi-UAV systems, it also introduces dynamic model switching behaviors in UAVs. Moreover, obtaining an accurate dynamic model for a multi-UAV system is challenging in practice. In addition, communication link failures and time-varying unknown disturbances are inevitable in multi-UAV systems. Hence, to overcome the adverse effects of the above challenges, a hybrid iterative learning formation control strategy is proposed in this paper. The proposed controller does not rely on precise modeling and exhibits its learning ability by utilizing historical input–output data to update the current control input. Furthermore, two convergence theorems are proven to guarantee the convergence of state, disturbance estimation, and formation tracking errors. Finally, three simulation examples are conducted for a multi-UAV system consisting of four quadrotor UAVs under multi-operating modes, switching topologies, and external disturbances. The results of the simulations show the strategy’s effectiveness and superiority in achieving the desired formation control objectives.
Citation: Drones
PubDate: 2024-08-19
DOI: 10.3390/drones8080406
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 407: TriNet: Exploring More Affordable and
Generalisable Remote Phenotyping with Explainable Deep Models
Authors: Lorenzo Beltrame, Jules Salzinger, Lukas J. Koppensteiner, Phillipp Fanta-Jende
First page: 407
Abstract: In this study, we propose a scalable deep learning approach to automated phenotyping using UAV multispectral imagery, exemplified by yellow rust detection in winter wheat. We adopt a high-granularity scoring method (1 to 9 scale) to align with international standards and plant breeders’ needs. Using a lower spatial resolution (60 m flight height at 2.5 cm GSD), we reduce the data volume by a factor of 3.4, making large-scale phenotyping faster and more cost-effective while obtaining results comparable to those of the state-of-the-art. Our model incorporates explainability components to optimise spectral bands and flight schedules, achieving top-three accuracies of 0.87 for validation and 0.67 and 0.70 on two separate test sets. We demonstrate that a minimal set of bands (EVI, Red, and GNDVI) can achieve results comparable to more complex setups, highlighting the potential for cost-effective solutions. Additionally, we show that high performance can be maintained with fewer time steps, reducing operational complexity. Our interpretable model components improve performance through regularisation and provide actionable insights for agronomists and plant breeders. This scalable and explainable approach offers an efficient solution for yellow rust phenotyping and can be adapted for other phenotypes and species, with future work focusing on optimising the balance between spatial, spectral, and temporal resolutions.
Citation: Drones
PubDate: 2024-08-21
DOI: 10.3390/drones8080407
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 408: Olive Tree Segmentation from UAV Imagery
Authors: Konstantinos Prousalidis, Stavroula Bourou, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Aikaterini Zachariadi, Vassilios Zachariadis
First page: 408
Abstract: This paper addresses the challenge of olive tree segmentation using drone imagery, which is crucial for precision agriculture applications. We tackle the data scarcity issue by augmenting existing detection datasets. Additionally, lightweight model variations of state-of-the-art models like YOLOv8n, RepViT-SAM, and EdgeSAM are combined into two proposed pipelines to meet computational constraints while maintaining segmentation accuracy. Our multifaceted approach successfully achieves an equilibrium among model size, inference time, and accuracy, thereby facilitating efficient olive tree segmentation in precision agriculture scenarios with constrained datasets. Following comprehensive evaluations, YOLOv8n appears to surpass the other models in terms of inference time and accuracy, albeit necessitating a more intricate fine-tuning procedure. Conversely, SAM-based pipelines provide a significantly more streamlined fine-tuning process, compatible with existing detection datasets for olive trees. However, this convenience incurs the disadvantages of a more elaborate inference architecture that relies on dual models, consequently yielding lower performance metrics and prolonged inference durations.
Citation: Drones
PubDate: 2024-08-21
DOI: 10.3390/drones8080408
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 409: Validation in X-Plane of Control Schemes for
Taking off and Landing Manoeuvres of Quadrotors
Authors: Ricardo Y. Almazan-Arvizu, Octavio Gutiérrez-Frías, Yair Lozano-Hernández, Hugo Rodríguez-Cortes, José A. Aguirre-Anaya
First page: 409
Abstract: This paper shows the results obtained by using MATLAB/Simulink and X-Plane as co-simulation tools for the comparison of control schemes for takeoff and landing maneuvers of a quadrotor. Two control schemes based on nested saturations are compared to ensure the convergence of θ and ϕ angles to the equilibrium point, each with its own specific characteristics in its design and tuning procedure. Furthermore, in both proposals, a Generalized Proportional Integral (GPI) control is used for the height part, while a feedforward PID control is used for the ψ angle. The control schemes are proposed from a local geodetic coordinate system East, North, Up (ENU). Feedback data for the control schemes are obtained from X-Plane via User Datagram Protocol (UDP)-based interface; they are used in MATLAB/Simulink for the calculation of the control actions; the control actions are then entered into a transformation matrix that converts the actions into rotor angular velocities, which are sent to X-Plane. Several numerical simulations are presented to demonstrate the effectiveness and robustness of the proposed schemes, considering the presence of disturbances mainly due to wind speed. Finally, different performance indices are used to evaluate the schemes based on error; in this way, the use of X-Plane as a Model-in-Loop (MIL) environment is validated, which helps to identify errors or problems of the proposed controllers before their coding and physical implementation.
Citation: Drones
PubDate: 2024-08-21
DOI: 10.3390/drones8080409
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 410: DFly: A Publicly Auditable and
Privacy-Preserving UAS Traffic Management System on Blockchain
Authors: Frederico Baptista, Marina Dehez-Clementi, Jonathan Detchart
First page: 410
Abstract: The integration of Unmanned Aircraft Systems (UASs) into the current airspace poses significant challenges in terms of safety, security, and operability. As an example, in 2019, the European Union defined a set of rules to support the digitalization of UAS traffic management (UTM) systems and services, namely the U-Space regulations. Current propositions opted for a centralized and private model, concentrated around governmental authorities (e.g., AlphaTango provides the Registration service and depends on the French government). In this paper, we advocate in favor of a more decentralized and transparent model in order to improve safety, security, operability among UTM stakeholders, and legal compliance. As such, we propose DFly, a publicly auditable and privacy-preserving UAS traffic management system on Blockchain, with two initial services: Registration and Flight Authorization. We demonstrate that the use of a blockchain guarantees the public auditability of the two services and corresponding service providers’ actions. In addition, it facilitates the comprehensive and distributed monitoring of airspace occupation and the integration of additional functionalities (e.g., the creation of a live UAS tracker). The combination with zero-knowledge proofs enables the deployment of an automated, distributed, transparent, and privacy-preserving Flight Authorization service, performed on-chain thanks to the blockchain logic. In addition to its construction, this paper details the instantiation of the proposed UTM system with the Ethereum Sepolia’s testnet and the Groth16 ZK-SNARK protocol. On-chain (gas cost) and off-chain (execution time) performance analyses confirm that the proposed solution is a viable and efficient alternative in the spirit of digitalization and offers additional security guarantees.
Citation: Drones
PubDate: 2024-08-21
DOI: 10.3390/drones8080410
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 411: LOFF: LiDAR and Optical Flow Fusion Odometry
Authors: Junrui Zhang, Zhongbo Huang, Xingbao Zhu, Fenghe Guo, Chenyang Sun, Quanxi Zhan, Runjie Shen
First page: 411
Abstract: Simultaneous Location and Mapping (SLAM) is a common algorithm for position estimation in GNSS-denied environments. However, the high structural consistency and low lighting conditions in tunnel environments pose challenges for traditional visual SLAM and LiDAR SLAM. To this end, this paper presents LiDAR and optical flow fusion odometry (LOFF), which uses a direction-separated data fusion method to fuse optical flow odometry into the degenerate direction of the LiDAR SLAM without sacrificing the accuracy. Moreover, LOFF incorporates detectors and a compensator, allowing for a smooth transition between general environments and degeneracy environments. This capability facilitates the stable flight of unmanned aerial vehicles (UAVs) in GNSS-denied tunnel environments, including corners and long-distance consistency. Through real-world experiments conducted in a GNSS-denied pedestrian tunnel, we demonstrate the superior position accuracy and trajectory smoothness of LOFF compared to state-of-the-art visual SLAM and LiDAR SLAM.
Citation: Drones
PubDate: 2024-08-22
DOI: 10.3390/drones8080411
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 412: A Systematic Survey of Transformer-Based 3D
Object Detection for Autonomous Driving: Methods, Challenges and Trends
Authors: Minling Zhu, Yadong Gong, Chunwei Tian, Zuyuan Zhu
First page: 412
Abstract: In recent years, with the continuous development of autonomous driving technology, 3D object detection has naturally become a key focus in the research of perception systems for autonomous driving. As the most crucial component of these systems, 3D object detection has gained significant attention. Researchers increasingly favor the deep learning framework Transformer due to its powerful long-term modeling ability and excellent feature fusion advantages. A large number of excellent Transformer-based 3D object detection methods have emerged. This article divides the methods based on data sources. Firstly, we analyze different input data sources and list standard datasets and evaluation metrics. Secondly, we introduce methods based on different input data and summarize the performance of some methods on different datasets. Finally, we summarize the limitations of current research, discuss future directions and provide some innovative perspectives.
Citation: Drones
PubDate: 2024-08-22
DOI: 10.3390/drones8080412
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 413: Polar AUV Challenges and Applications: A Review
Authors: Shuangshuang Fan, Neil Bose, Zeming Liang
First page: 413
Abstract: This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, identifying and addressing the key technological challenges these vehicles face. These include advanced navigation techniques, strategic path planning, efficient obstacle avoidance, robust communication, stable energy supply, reliable launch and recovery, and thorough risk analysis. Furthermore, this study categorizes the typical capabilities and applications of AUVs in polar contexts, such as under-ice mapping and measurement, water sampling, ecological investigation, seafloor mapping, and surveillance networking. We also briefly highlight existing research gaps and potential future challenges in this evolving field.
Citation: Drones
PubDate: 2024-08-22
DOI: 10.3390/drones8080413
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 414: A Multi-Waypoint Motion Planning Framework for
Quadrotor Drones in Cluttered Environments
Authors: Delong Shi, Jinrong Shen, Mingsheng Gao, Xiaodong Yang
First page: 414
Abstract: In practical missions, quadrotor drones frequently face the challenge of navigating through multiple predetermined waypoints in cluttered environments where the sequence of the waypoints is not specified. This study presents a comprehensive multi-waypoint motion planning framework for quadrotor drones, comprising multi-waypoint trajectory planning and waypoint sequencing. To generate a trajectory that follows a specified sequence of waypoints, we integrate uniform B-spline curves with a bidirectional A* search to produce a safe, kinodynamically feasible initial trajectory. Subsequently, we model the optimization problem as a quadratically constrained quadratic program (QCQP) to enhance the trackability of the trajectory. Throughout this process, a replanning strategy is designed to ensure the traversal of multiple waypoints. To accurately determine the shortest flight time waypoint sequence, the fast marching (FM) method is utilized to efficiently establish the cost matrix between waypoints, ensuring consistency with the constraints and objectives of the planning method. Ant colony optimization (ACO) is then employed to solve this variant of the traveling salesman problem (TSP), yielding the sequence with the lowest temporal cost. The framework’s performance was validated in various complex simulated environments, demonstrating its efficacy as a robust solution for autonomous quadrotor drone navigation.
Citation: Drones
PubDate: 2024-08-22
DOI: 10.3390/drones8080414
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 415: Control Barrier Function-Based Collision
Avoidance Guidance Strategy for Multi-Fixed-Wing UAV Pursuit-Evasion
Environment
Authors: Xinyuan Lv, Chi Peng, Jianjun Ma
First page: 415
Abstract: In order to address the potential collision issue arising from multiple fixed-wing unmanned aerial vehicles (UAVs) intercepting targets in n-on-n and n-on-1 pursuit-evasion scenarios, we propose a collision-avoidance guidance strategy for UAVs based on high-order control barrier functions (HOCBFs). Initially, a two-dimensional model of multiple UAVs and targets is established, and the interaction between UAVs is determined. Subsequently, the collision-avoidance problem within a UAV swarm is formulated as a mathematical problem involving multiple constraints in the form of higher-order control obstacle functions. Multiple HOCBF constraints are then simplified into a single linear constraint for computational convenience. By integrating HOCBF constraints with quadratic programming problems, we obtain a closed-form solution for UAVs that incorporates collision-avoidance guidance terms alongside nominal guidance terms. Simulations with different numbers of pursuers and different target motion states are conducted. The results demonstrate an excellent experimental effect, ensuring that the multi-UAVs consistently remain above the minimum safe distance and ultimately hit the targets accurately.
Citation: Drones
PubDate: 2024-08-22
DOI: 10.3390/drones8080415
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 416: Cross-Correlation Characteristic Measurements
and Analysis for Multi-Link A2G Channels Based on a Multi-UAV System
Authors: Xuchao Ye, Qiuming Zhu, Hangang Li, Kai Mao, Hanpeng Li, Xiaomin Chen, Boyu Hua, Weizhi Zhong
First page: 416
Abstract: With the rapid development of unmanned aerial vehicles (UAVs), UAV-based communications have shown promising application prospects in beyond-fifth-generation (B5G) and sixth-generation (6G) communication. Air-to-ground (A2G) channel characteristics are significant for UAV-based wireless communications. In this paper, a multi-UAV channel measurement system is developed, which can realize cooperative, accurate, and real-time channel measurements. Measurement campaigns are performed in the campus scenario at the 3.6 GHz frequency band. Based on the measurement data, cross-correlation properties of some typical large-scale channel parameters are extracted and analyzed, including the power delay profile (PDP), path loss (PL), and shadow fading (SF). The analysis results reveal that the cross-correlation of PDP remains larger than 0.6 during the whole measurement, and the decorrelation distance is 14.765 m. The cross-correlation of SF is relatively low, and the decorrelation distance is found to be 4.628 m. These results can provide valuable references for optimizing multi-link UAV communications and node placements.
Citation: Drones
PubDate: 2024-08-22
DOI: 10.3390/drones8080416
Issue No: Vol. 8, No. 8 (2024)
- Drones, Vol. 8, Pages 341: UAV-Embedded Sensors and Deep Learning for
Pathology Identification in Building Façades: A Review
Authors: Gabriel de Sousa Meira, João Victor Ferreira Guedes, Edilson de Souza Bias
First page: 341
Abstract: The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has stimulated the search for new data processing and validation methods, considering the magnitude of the data collected during fieldwork and the absence of specific methodologies for each type of sensor. Regarding data processing, the use of deep learning techniques has become widespread, especially for the automation of processes that involve a great amount of data. However, just as with the increasing use of embedded sensors, deep learning necessitates the development of studies, particularly those focusing on neural networks that better represent the data to be analyzed. It also requires the enhancement of practices to be used in fieldwork, especially regarding data processing. In this context, the objective of this study is to review the existing literature on the use of embedded technologies in UAVs and deep learning for the identification and characterization of pathological manifestations present in building façades in order to develop a robust knowledge base that is capable of contributing to new investigations in this field of research.
Citation: Drones
PubDate: 2024-07-22
DOI: 10.3390/drones8070341
Issue No: Vol. 8, No. 7 (2024)
