Drones Pub Date : 2024-05-16 DOI: 10.3390/drones8050203

Dimitris Perikleous, George Koustas, Spyros Velanas, Katerina Margariti, Pantelis Velanas, Diego Gonzalez-Aguilera

{"title":"A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management","authors":"Dimitris Perikleous, George Koustas, Spyros Velanas, Katerina Margariti, Pantelis Velanas, Diego Gonzalez-Aguilera","doi":"10.3390/drones8050203","DOIUrl":"https://doi.org/10.3390/drones8050203","url":null,"abstract":"Our study introduces a new approach, leveraging robotics technology and remote sensing for multifaceted applications in forest and wildfire management. Presented in this paper is PULSAR, an innovative UAV with reconfigurable capabilities, able of operating as a quadcopter, a co-axial quadcopter, and a standalone octocopter. Tailored to diverse operational requirements, PULSAR accommodates multiple payloads, showcasing its adaptability and versatility. This paper meticulously details material selection and design methods, encompassing both initial and detailed design, while the electronics design section seamlessly integrates essential avionic components. The 3D drone layout design, accomplished using SOLIDWORKS, enhances understanding by showcasing all three different configurations of PULSAR’s structure. Serving a dual purpose, this study highlights UAV applications in forest and wildfire management, particularly in detailed forest mapping, edge computing, and cartographic product generation, as well as detection and tracking of elements, illustrating how a UAV can be a valuable tool. Following the analysis of applications, this paper presents the selection and integration of payloads onto the UAV. Simultaneously, each of the three distinct UAV configurations is matched with a specific forest application, ensuring optimal performance and efficiency. Lastly, computational validation of the UAV’s main components’ structural integrity is achieved through finite element analysis (FEA), affirming the absence of issues regarding stress and displacement. In conclusion, this research underscores the efficacy of PULSAR, marking a significant leap forward in applying robotics technology for wildfire science.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prescribed Performance Fault-Tolerant Attitude Tracking Control for UAV with Actuator Faults 针对有推杆故障的无人飞行器的规定性能容错姿态跟踪控制

Drones Pub Date : 2024-05-16 DOI: 10.3390/drones8050204

Qilong Wu, Qidan Zhu

引用次数: 0

A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density 基于多种策略的无人机自主路径规划新方法，用于高速、高密度避障

Drones Pub Date : 2024-05-16 DOI: 10.3390/drones8050205

Tongyao Yang, Fengbao Yang, Dingzhu Li

{"title":"A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density","authors":"Tongyao Yang, Fengbao Yang, Dingzhu Li","doi":"10.3390/drones8050205","DOIUrl":"https://doi.org/10.3390/drones8050205","url":null,"abstract":"Path planning is one of the most essential parts of autonomous navigation. Most existing works are based on the strategy of adjusting angles for planning. However, drones are susceptible to collisions in environments with densely distributed and high-speed obstacles, which poses a serious threat to flight safety. To handle this challenge, we propose a new method based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density (MSAO2H). Firstly, we propose to extend the obstacle avoidance decisions of drones into angle adjustment, speed adjustment, and obstacle clearance. Hybrid action space is adopted to model each decision. Secondly, the state space of the obstacle environment is constructed to provide effective features for learning decision parameters. The instant reward and the ultimate reward are designed to balance the learning efficiency of decision parameters and the ability to explore optimal solutions. Finally, we innovatively introduced the interferometric fluid dynamics system into the parameterized deep Q-network to guide the learning of angle parameters. Compared with other algorithms, the proposed model has high success rates and generates high-quality planned paths. It can meet the requirements for autonomously planning high-quality paths in densely dynamic obstacle environments.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Improved YOLOv5 Vehicle Target Detection Algorithm in Aerial Images 关于改进航空图像中 YOLOv5 车辆目标检测算法的研究

Drones Pub Date : 2024-05-16 DOI: 10.3390/drones8050202

Xue Yang, Jihong Xiu, Xiaojia Liu

{"title":"Research on Improved YOLOv5 Vehicle Target Detection Algorithm in Aerial Images","authors":"Xue Yang, Jihong Xiu, Xiaojia Liu","doi":"10.3390/drones8050202","DOIUrl":"https://doi.org/10.3390/drones8050202","url":null,"abstract":"Aerial photoelectric imaging payloads have become an important means of reconnaissance and surveillance in recent years. However, aerial images are easily affected by external conditions and have unclear edges, which greatly reduces the accuracy of imaging target recognition. This paper proposes the M-YOLOv5 model, which uses a shallow feature layer. The RFBs module is introduced to improve the receptive field and detection effect of small targets. In the neck network part, the BiFPN structure is used to reuse the underlying features to integrate more features, and a CBAM attention mechanism is added to improve detection accuracy. The experimental results show that the detection effect of this method on the DroneVehicle dataset is better than that of the original network, with the precision rate increased by 2.8%, the recall rate increased by 16%, and the average precision increased by 2.3%. Considering the real-time problem of target detection, based on the improved model, the Clight-YOLOv5 model is proposed, by lightweighting the network structure and using the depth-separable convolution optimization module. After lightweighting, the number of model parameters is decreased by 71.3%, which provides a new idea for lightweight target detection and proves the model’s effectiveness in aviation scenarios.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"84 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV 基于深度强化学习的蜂窝连接无人机 3D 轨迹规划

Drones Pub Date : 2024-05-15 DOI: 10.3390/drones8050199

Xiang Liu, Weizhi Zhong, Xin Wang, Hongtao Duan, Zhenxiong Fan, Haowen Jin, Yang Huang, Zhipeng Lin

{"title":"Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV","authors":"Xiang Liu, Weizhi Zhong, Xin Wang, Hongtao Duan, Zhenxiong Fan, Haowen Jin, Yang Huang, Zhipeng Lin","doi":"10.3390/drones8050199","DOIUrl":"https://doi.org/10.3390/drones8050199","url":null,"abstract":"To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"48 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Target Optimization Strategy for Unmanned Aerial Vehicle Formation in Forest Fire Monitoring Based on Deep Q-Network Algorithm 基于深度 Q 网络算法的林火监测无人机编队多目标优化策略

Drones Pub Date : 2024-05-15 DOI: 10.3390/drones8050201

Wenjia Liu, Sung-Ki Lyu, Tao Liu, Yu-Ting Wu, Zhen Qin

{"title":"Multi-Target Optimization Strategy for Unmanned Aerial Vehicle Formation in Forest Fire Monitoring Based on Deep Q-Network Algorithm","authors":"Wenjia Liu, Sung-Ki Lyu, Tao Liu, Yu-Ting Wu, Zhen Qin","doi":"10.3390/drones8050201","DOIUrl":"https://doi.org/10.3390/drones8050201","url":null,"abstract":"Forest fires often pose serious hazards, and the timely monitoring and extinguishing of residual forest fires using unmanned aerial vehicles (UAVs) can prevent re-ignition and mitigate the damage caused. Due to the urgency of forest fires, drones need to respond quickly during firefighting operations, while traditional drone formation deployment requires a significant amount of time. This paper proposes a pure azimuth passive positioning strategy for circular UAV formations and utilizes the Deep Q-Network (DQN) algorithm to effectively adjust the formation within a short timeframe. Initially, a passive positioning model for UAVs based on the relationships between the sides and angles of a triangle is established, with the closest point to the ideal position being selected as the position for the UAV to be located. Subsequently, a multi-target optimization model is developed, considering 10 UAVs as an example, with the objective of minimizing the number of adjustments while minimizing the deviation between the ideal and adjusted UAV positions. The DQN algorithm is employed to solve and design experiments for validation, demonstrating that the deviation between the UAV positions and the ideal positions, as well as the number of adjustments, are within acceptable ranges. In comparison to genetic algorithms, it saves approximately 120 s.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Device Security Application for Unmanned Surface and Aerial Systems 无人水面和航空系统的多设备安全应用

Drones Pub Date : 2024-05-15 DOI: 10.3390/drones8050200

Andre Leon, Christopher Britt, Britta Hale

{"title":"Multi-Device Security Application for Unmanned Surface and Aerial Systems","authors":"Andre Leon, Christopher Britt, Britta Hale","doi":"10.3390/drones8050200","DOIUrl":"https://doi.org/10.3390/drones8050200","url":null,"abstract":"The use of autonomous and unmanned systems continues to increase, with uses spanning from package delivery to simple automation of tasks and from factory usage to defense industries and agricultural applications. With the proliferation of unmanned systems comes the question of how to secure the command-and-control communication links among such devices and their operators. In this work, we look at the use of the Messaging Layer Security (MLS) protocol, designed to support long-lived continuous sessions and group communication with a high degree of security. We build out MAUI—an MLS API for UxS Integration that provides an interface for the secure exchange of data between a ScanEagle unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) in a multi-domain ad-hoc network configuration, and experiment on system limits such as the ciphersuite set-up time and message handling rates. The experiments in this work were conducted in virtual and physical environments between the UAV, USV, and a controller device (all of different platforms). Our results demonstrate the viability of capitalizing on MLS’s capabilities to securely and efficiently transmit data for distributed communication among various unmanned system platforms.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"43 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning 基于无人机 RGB 图像和深度学习的多高度玉米穗检测与计数

Drones Pub Date : 2024-05-14 DOI: 10.3390/drones8050198

Shanwei Niu, Zhigang Nie, Guang Li, Wenyu Zhu

{"title":"Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning","authors":"Shanwei Niu, Zhigang Nie, Guang Li, Wenyu Zhu","doi":"10.3390/drones8050198","DOIUrl":"https://doi.org/10.3390/drones8050198","url":null,"abstract":"In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs) and the YOLOv8 model. The model incorporates the Pconv local convolution module, enabling a lightweight design and rapid detection speed. The ACmix module is added to the backbone section to improve feature extraction capabilities for corn tassels. Moreover, the CTAM module is integrated into the neck section to enhance semantic information exchange between channels, allowing for precise and efficient positioning of corn tassels. To optimize the learning rate strategy, the sparrow search algorithm (SSA) is utilized. Significant improvements in recognition accuracy, detection efficiency, and robustness are observed across various UAV flight altitudes. Experimental results show that, compared to the original YOLOv8 model, the proposed model exhibits an increase in accuracy of 3.27 percentage points to 97.59% and an increase in recall of 2.85 percentage points to 94.40% at a height of 5 m. Furthermore, the model optimizes frames per second (FPS), parameters (params), and GFLOPs (giga floating point operations per second) by 7.12%, 11.5%, and 8.94%, respectively, achieving values of 40.62 FPS, 14.62 MB, and 11.21 GFLOPs. At heights of 10, 15, and 20 m, the model maintains stable accuracies of 90.36%, 88.34%, and 84.32%, respectively. This study offers technical support for the automated detection of corn tassels, advancing the intelligence and precision of agricultural production and significantly contributing to the development of modern agricultural technology.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"70 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Radar Inertial Odometry in Dynamic 3D Environments 动态三维环境中的鲁棒性雷达惯性测距仪

Drones Pub Date : 2024-05-13 DOI: 10.3390/drones8050197

Yang Lyu, Lin Hua, Jiaming Wu, Xinkai Liang, Chunhui Zhao

引用次数: 0

A Novel UAV Air-to-Air Channel Model Incorporating the Effect of UAV Vibrations and Diffuse Scattering 包含无人机振动和漫散射效应的新型无人机空对空信道模型

Drones Pub Date : 2024-05-12 DOI: 10.3390/drones8050194

Wenzhe Qi, Ji Bian, Zili Wang, Wenzhao Liu

{"title":"A Novel UAV Air-to-Air Channel Model Incorporating the Effect of UAV Vibrations and Diffuse Scattering","authors":"Wenzhe Qi, Ji Bian, Zili Wang, Wenzhao Liu","doi":"10.3390/drones8050194","DOIUrl":"https://doi.org/10.3390/drones8050194","url":null,"abstract":"In this paper, we propose a geometric channel model for air-to-air (A2A) unmanned aerial vehicle (UAV) communication scenarios. The model is established by incorporating line-of-sight, specular reflection, and diffuse scattering components, and it can capture the impacts of UAV vibrations induced by the propeller’s rotation. Based on UAV heights and ground scatterer density, a closed-form expression is derived to jointly capture the zenith and azimuth angular distributions of diffuse rays. The power of diffuse rays is modeled according to the grazing angle of the rays and the electrical properties and roughness of the ground materials. Key statistics, including the temporal autocorrelation function, spatial cross-correlation function, Doppler power spectrum density, and coherence time are derived, providing an in-depth understanding of the time-variant characteristics of the channel. The results indicate that the presented model is capable of capturing certain A2A channel characteristics, which align with the corresponding theoretical analysis. The findings suggest that the scattering effect of the A2A channel is significantly influenced by the altitude of the UAV. Additionally, it is shown that UAV vibrations can introduce extra Doppler frequencies, notably decreasing the temporal correlation and coherence time of the channel. This effect is more prominent when the system operates at high-frequency bands. The effectiveness of the presented model is confirmed through a comparison of its statistics with those of an existing model and with available measurement data.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"103 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140987235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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