Journal Description
Drones
Drones
is an international, peer-reviewed, open access journal published monthly online by MDPI. The journal focuses on design and applications of drones, including unmanned aerial vehicle (UAV), Unmanned Aircraft Systems (UAS), and Remotely Piloted Aircraft Systems (RPAS), etc. Likewise, contributions based on unmanned water/underwater drones and unmanned ground vehicles are also welcomed.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Remote Sensing) / CiteScore - Q1 (Aerospace Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.9 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
4.8 (2022);
5-Year Impact Factor:
5.5 (2022)
Latest Articles
UAS Photogrammetry and TLS Technology: A Novel Approach to Predictive Maintenance in Industrial Tank Systems
Drones 2024, 8(6), 215; https://doi.org/10.3390/drones8060215 - 22 May 2024
Abstract
This paper explores the integration of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for the diagnosis and evaluation of deformations in industrial tanks, demonstrating their significant contribution to preventive maintenance. TLS accurately measures distances to the tank’s surface, generating detailed
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This paper explores the integration of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for the diagnosis and evaluation of deformations in industrial tanks, demonstrating their significant contribution to preventive maintenance. TLS accurately measures distances to the tank’s surface, generating detailed 3D point clouds, while UAS photogrammetry captures high-resolution images from various angles and altitudes. By combining TLS and UAS data into comprehensive 3D models, engineers can identify subtle deformations and anticipate structural failures. The study results revealed significant deviations in tank shell verticality and roundness using TLS and notable roof unevenness using UAS. Comparing 3D models before and after corrective measures showed improved structural integrity. This approach enhances safety, optimizes resources, and enables targeted interventions. The findings highlight the potential of TLS and UAS technologies to revolutionize preventive maintenance, offering an efficient, precise, and less intrusive methodology for critical infrastructure inspection. Adopting these technologies can improve safety, reduce operational risks, and optimize asset management in various industrial sectors.
Full article
Open AccessArticle
UAV Swarm Cooperative Dynamic Target Search: A MAPPO-Based Discrete Optimal Control Method
by
Dexing Wei, Lun Zhang, Quan Liu, Hao Chen and Jian Huang
Drones 2024, 8(6), 214; https://doi.org/10.3390/drones8060214 - 22 May 2024
Abstract
Unmanned aerial vehicles (UAVs) are commonly employed in pursuit and rescue missions, where the target’s trajectory is unknown. Traditional methods, such as evolutionary algorithms and ant colony optimization, can generate a search route in a given scenario. However, when the scene changes, the
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Unmanned aerial vehicles (UAVs) are commonly employed in pursuit and rescue missions, where the target’s trajectory is unknown. Traditional methods, such as evolutionary algorithms and ant colony optimization, can generate a search route in a given scenario. However, when the scene changes, the solution needs to be recalculated. In contrast, more advanced deep reinforcement learning methods can train an agent that can be directly applied to a similar task without recalculation. Nevertheless, there are several challenges when the agent learns how to search for unknown dynamic targets. In this search task, the rewards are random and sparse, which makes learning difficult. In addition, because of the need for the agent to adapt to various scenario settings, interactions required between the agent and the environment are more comparable to typical reinforcement learning tasks. These challenges increase the difficulty of training agents. To address these issues, we propose the OC-MAPPO method, which combines optimal control (OC) and Multi-Agent Proximal Policy Optimization (MAPPO) with GPU parallelization. The optimal control model provides the agent with continuous and stable rewards. Through parallelized models, the agent can interact with the environment and collect data more rapidly. Experimental results demonstrate that the proposed method can help the agent learn faster, and the algorithm demonstrated a 26.97% increase in the success rate compared to genetic algorithms.
Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones)
Open AccessArticle
Probabilistic Chain-Enhanced Parallel Genetic Algorithm for UAV Reconnaissance Task Assignment
by
Jiaze Tang, Dan Liu, Qisong Wang, Junbao Li and Jinwei Sun
Drones 2024, 8(6), 213; https://doi.org/10.3390/drones8060213 - 21 May 2024
Abstract
With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough
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With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.
Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
Open AccessReview
Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms
by
Takashi Sonam Tashi Tanaka, Sheng Wang, Johannes Ravn Jørgensen, Marco Gentili, Armelle Zaragüeta Vidal, Anders Krogh Mortensen, Bharat Sharma Acharya, Brittany Deanna Beck and René Gislum
Drones 2024, 8(6), 212; https://doi.org/10.3390/drones8060212 - 21 May 2024
Abstract
The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate
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The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate the advantages and challenges of UAV-based phenotyping techniques. This is a comprehensive overview summarizing the UAV platforms, sensors, and data processing while also introducing recent technological developments. Recently developed software and sensors greatly enhance the accessibility of UAV-based phenotyping, and a summary of recent research (publications 2019–2024) provides implications for future research. Researchers have focused on integrating multiple sensing data or utilizing machine learning algorithms, such as ensemble learning and deep learning, to enhance the prediction accuracies of crop physiological traits. However, this approach will require big data alongside laborious destructive measurements in the fields. Future research directions will involve standardizing the process of merging data from multiple field experiments and data repositories. Previous studies have focused mainly on UAV technology in major crops, but there is a high potential in minor crops or cropping systems for future sustainable crop production. This review can guide new practitioners who aim to implement and utilize UAV-based phenotyping.
Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
Open AccessArticle
Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment
by
Matilde Gelli, Luca Bigazzi, Enrico Boni and Michele Basso
Drones 2024, 8(6), 211; https://doi.org/10.3390/drones8060211 - 21 May 2024
Abstract
In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D
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In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D localization using computer vision or advanced sensors. Consequently, much of the existing research focuses on semi-autonomous systems, which rely on ground assistance through the use of external sensors (motion capture systems) and remote computing power. This study addresses the challenge by proposing a fully autonomous trajectory planning solution. By introducing a real-time path planning algorithm based on the minimization of the snap, the optimal trajectory is dynamically recalculated as needed. Evaluation of the algorithm’s performance is conducted in an unknown real-world scenario, utilizing both simulations and experimental data. The algorithm was implemented in MATLAB and subsequently translated to C++ for onboard execution on the drone.
Full article
(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)
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Open AccessArticle
The Challenges of Blood Sample Delivery via Drones in Urban Environment: A Feasibility Study through Specific Operation Risk Assessment Methodology
by
Sara Molinari, Riccardo Patriarca and Marco Ducci
Drones 2024, 8(5), 210; https://doi.org/10.3390/drones8050210 - 20 May 2024
Abstract
In recent years, Unmanned Aircraft System (UAS) usage in the medical sector as an alternative to traditional means of goods transport has grown significantly. Even though the reduced response time achieved with UASs can be lifesaving in critical situations, their usage must comply
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In recent years, Unmanned Aircraft System (UAS) usage in the medical sector as an alternative to traditional means of goods transport has grown significantly. Even though the reduced response time achieved with UASs can be lifesaving in critical situations, their usage must comply with technological constraints such as range, speed and capacity, while minimizing potential risks. In this paper, the feasibility of a drone operation dedicated to the transport of blood samples in an urban area is studied through a safety risk analysis. The assessment utilizes the Specific Operation Risk Assessment (SORA) framework, in line with current European regulations, and extends it to define flight trajectories with minimal risk. A case study in the Helsinki urban area is used as a reference, with an exemplary case of commercial drone transportation of blood samples between the Töölö and Malmi Hospitals. By leveraging the drone performance capabilities and minimizing the risk for people on the ground, this approach demonstrates that medical delivery using drones in densely populated urban environments remains challenging. Nonetheless, it argues that the proposed method can enhance risk awareness and support the planning of feasible operations.
Full article
(This article belongs to the Special Issue Flight Control and Collision Avoidance of UAVs)
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Open AccessCorrection
Correction: Bianchi et al. Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator. Drones 2024, 8, 29
by
Domenico Bianchi, Alessandro Borri, Federico Cappuzzo and Stefano Di Gennaro
Drones 2024, 8(5), 209; https://doi.org/10.3390/drones8050209 - 20 May 2024
Abstract
Figure corrections [...]
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Open AccessArticle
A Framework of Grasp Detection and Operation for Quadruped Robot with a Manipulator
by
Jiamin Guo, Hui Chai, Qin Zhang, Haoning Zhao, Meiyi Chen, Yueyang Li and Yibin Li
Drones 2024, 8(5), 208; https://doi.org/10.3390/drones8050208 - 19 May 2024
Abstract
Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg
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Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg Joint Controller (LJC), and a Manipulator Joint Controller (MJC). In the GDC, we proposed a lightweight grasp detection CNN based on DenseBlock called DES-LGCNN, which reduced algorithm complexity while maintaining accuracy by incorporating UP and DOWN modules with DenseBlock. For JTP, we optimized the model based on quadruped robot kinematics to enhance wrist camera visibility in dynamic environments. We integrated the network and model into our homemade robot control system and verified our framework through multiple experiments. First, we evaluated the accuracy of the grasp detection algorithm using the Cornell and Jacquard datasets. On the Jacquard dataset, we achieved a detection accuracy of 92.49% for grasp points within 6 ms. Second, we verified its visibility through simulation. Finally, we conducted dynamic scene experiments which consisted of a dynamic target scenario (DTS), a dynamic base scenario (DBS), and a dynamic target and base scenario (DTBS) using an SDU-150 physical robot. In all three scenarios, the object was successfully grasped. The results demonstrate the effectiveness of our framework in managing dynamic environments throughout task execution.
Full article
(This article belongs to the Special Issue Advanced Unmanned System Control and Data Processing)
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Open AccessArticle
Chaff Cloud Integrated Communication and TT&C: An Integrated Solution for Single-Station Emergency Communications and TT&C in a Denied Environment
by
Lvyang Ye, Yikang Yang, Binhu Chen, Deng Pan, Fan Yang, Shaojun Cao, Yangdong Yan and Fayu Sun
Drones 2024, 8(5), 207; https://doi.org/10.3390/drones8050207 - 18 May 2024
Abstract
In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and
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In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and non-line-of-sight environments that may cause service degradation or even failure. This paper presents a single-station emergency solution that integrates communication and TT&C (IC&T) functions based on radar chaff cloud technology. Firstly, a suitable selection of frequency bands and modulation methods is provided for the emergency IC&T system to ensure compatibility with existing communication and TT&C systems while catering to the future needs of IC&T. Subsequently, theoretical analyses are conducted on the communication link transmission loss, data transmission, code tracking accuracy, and anti-multipath model of the emergency IC&T system based on the chosen frequency band and modulation mode. This paper proposes a dual-way asynchronous precision ranging and time synchronization (DWAPR&TS) system employing dual one-way ranging (DOWR) measurement, a dual-way asynchronous incoherent Doppler velocity measurement (DWAIDVM) system, and a single baseline angle measurement system. Next, we analyze the physical characteristics of the radar chaff and establish a dynamic model of spherical chaff cloud clusters based on free diffusion. Additionally, we provide the optimal strategy for deploying chaff cloud. Finally, the emergency IC&T application based on the radar chaff cloud relay is simulated, and the results show that for severe interference, taking drones as an example, under a measurement baseline of 100 km, the emergency IC&T solution proposed in this paper can achieve an accuracy range of approximately 100 m, a velocity accuracy of 0.1 m/s, and an angle accuracy of 0.1°. In comparison with existing TT&C system solutions, the proposed system possesses unique and potential advantages that the others do not have. It can serve as an emergency IC&T reference solution in denial environments, offering significant value for both civilian and military applications.
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(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)
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Open AccessArticle
Incorporating Symbolic Discrete Controller Synthesis into a Virtual Robot Experimental Platform: An Implementation with Collaborative Unmanned Aerial Vehicle Robots
by
Mete Özbaltan and Serkan Çaşka
Drones 2024, 8(5), 206; https://doi.org/10.3390/drones8050206 - 17 May 2024
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We introduce a modeling framework aimed at incorporating symbolic discrete controller synthesis (DCS) into a virtual robot experimental platform. This framework involves symbolically representing the behaviors of robotic systems along with their control objectives using synchronous programming techniques. We employed DCS algorithms through
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We introduce a modeling framework aimed at incorporating symbolic discrete controller synthesis (DCS) into a virtual robot experimental platform. This framework involves symbolically representing the behaviors of robotic systems along with their control objectives using synchronous programming techniques. We employed DCS algorithms through the reactive synchronous environment ReaX to generate controllers that fulfill specified objectives. These resulting controllers were subsequently deployed on the virtual robot experimental platform Simscape. To demonstrate and validate our approach, we provide an implementation example involving collaborative UAV robots.
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Open AccessArticle
A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density
by
Tongyao Yang, Fengbao Yang and Dingzhu Li
Drones 2024, 8(5), 205; https://doi.org/10.3390/drones8050205 - 16 May 2024
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
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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.
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(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems)
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Open AccessArticle
Prescribed Performance Fault-Tolerant Attitude Tracking Control for UAV with Actuator Faults
by
Qilong Wu and Qidan Zhu
Drones 2024, 8(5), 204; https://doi.org/10.3390/drones8050204 - 16 May 2024
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This paper proposes a prescribed performance fault-tolerant control based on a fixed-time extended state observer (FXTESO) for a carrier-based unmanned aerial vehicle (UAV). First, the attitude motion model of the UAV is introduced. Secondly, the proposed FXTESO is designed to estimate the total
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This paper proposes a prescribed performance fault-tolerant control based on a fixed-time extended state observer (FXTESO) for a carrier-based unmanned aerial vehicle (UAV). First, the attitude motion model of the UAV is introduced. Secondly, the proposed FXTESO is designed to estimate the total disturbances including coupling, actuator faults and external disturbances. By using the barrier Lyapunov function (BLF), it is proved that under prescribed performance control (PPC), the attitude tracking error is stable within the prescribed range. The simulation results for tracking the desired attitude angle show that the average overshoot and stabilization time of PPC-FXTESO is and . Comparatively, the average overshoots of BSC-ESO and BSC-FTESO are and , with stabilization times of and , respectively. Therefore, the control scheme proposed in this paper outperforms other control schemes.
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Open AccessArticle
A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
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Dimitris Perikleous, George Koustas, Spyros Velanas, Katerina Margariti, Pantelis Velanas and Diego Gonzalez-Aguilera
Drones 2024, 8(5), 203; https://doi.org/10.3390/drones8050203 - 16 May 2024
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
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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.
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(This article belongs to the Special Issue Drones for Wildfire and Prescribed Fire Science)
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Open AccessArticle
Research on Improved YOLOv5 Vehicle Target Detection Algorithm in Aerial Images
by
Xue Yang, Jihong Xiu and Xiaojia Liu
Drones 2024, 8(5), 202; https://doi.org/10.3390/drones8050202 - 16 May 2024
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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
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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.
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Open AccessArticle
Multi-Target Optimization Strategy for Unmanned Aerial Vehicle Formation in Forest Fire Monitoring Based on Deep Q-Network Algorithm
by
Wenjia Liu, Sung-Ki Lyu, Tao Liu, Yu-Ting Wu and Zhen Qin
Drones 2024, 8(5), 201; https://doi.org/10.3390/drones8050201 - 15 May 2024
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
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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.
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(This article belongs to the Topic Application of Remote Sensing in Forest Fire)
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Open AccessArticle
Multi-Device Security Application for Unmanned Surface and Aerial Systems
by
Andre Leon, Christopher Britt and Britta Hale
Drones 2024, 8(5), 200; https://doi.org/10.3390/drones8050200 - 15 May 2024
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
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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.
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(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
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Open AccessArticle
Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV
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Xiang Liu, Weizhi Zhong, Xin Wang, Hongtao Duan, Zhenxiong Fan, Haowen Jin, Yang Huang and Zhipeng Lin
Drones 2024, 8(5), 199; https://doi.org/10.3390/drones8050199 - 15 May 2024
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
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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.
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(This article belongs to the Special Issue Technologies and Applications of UAV Channel Models in Communications and Spectrum Awareness)
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Open AccessArticle
Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning
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Shanwei Niu, Zhigang Nie, Guang Li and Wenyu Zhu
Drones 2024, 8(5), 198; https://doi.org/10.3390/drones8050198 - 14 May 2024
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,
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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.
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(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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Open AccessArticle
Robust Radar Inertial Odometry in Dynamic 3D Environments
by
Yang Lyu, Lin Hua, Jiaming Wu, Xinkai Liang and Chunhui Zhao
Drones 2024, 8(5), 197; https://doi.org/10.3390/drones8050197 - 13 May 2024
Abstract
Millimeter-Wave Radar is one promising sensor to achieve robust perception against challenging observing conditions. In this paper, we propose a Radar Inertial Odometry (RIO) pipeline utilizing a long-range 4D millimeter-wave radar for autonomous vehicle navigation. Initially, we develop a perception frontend based on
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Millimeter-Wave Radar is one promising sensor to achieve robust perception against challenging observing conditions. In this paper, we propose a Radar Inertial Odometry (RIO) pipeline utilizing a long-range 4D millimeter-wave radar for autonomous vehicle navigation. Initially, we develop a perception frontend based on radar point cloud filtering and registration to estimate the relative transformations between frames reliably. Then an optimization-based backbone is formulated, which fuses IMU data, relative poses, and point cloud velocities from radar Doppler measurements. The proposed method is extensively tested in challenging on-road environments and in-the-air environments. The results indicate that the proposed RIO can provide a reliable localization function for mobile platforms, such as automotive vehicles and Unmanned Aerial Vehicles (UAVs), in various operation conditions.
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(This article belongs to the Special Issue UAV Positioning: From Ground to Sky)
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Open AccessReview
A Review of Real-Time Implementable Cooperative Aerial Manipulation Systems
by
Stamatina C. Barakou, Costas S. Tzafestas and Kimon P. Valavanis
Drones 2024, 8(5), 196; https://doi.org/10.3390/drones8050196 - 12 May 2024
Abstract
This review paper focuses on quadrotor- and multirotor-based cooperative aerial manipulation. Emphasis is first given to comparing and evaluating prototype systems that have been implemented and tested in real-time in diverse application environments. The underlying modeling and control approaches are also discussed and
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This review paper focuses on quadrotor- and multirotor-based cooperative aerial manipulation. Emphasis is first given to comparing and evaluating prototype systems that have been implemented and tested in real-time in diverse application environments. The underlying modeling and control approaches are also discussed and compared. The outcome of this review allows for understanding the motivation and rationale to develop such systems, their applicability and implementability in diverse applications and also challenges that need to be addressed and overcome. Moreover, this paper provides a guide to develop the next generation of prototype systems based on preferred characteristics, functionality, operability, and application domain.
Full article
(This article belongs to the Special Issue Selected Papers from the 2023 International Conference on Unmanned Aircraft Systems (ICUAS 2023))
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