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
An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery
Drones 2024, 8(1), 21; https://doi.org/10.3390/drones8010021 - 15 Jan 2024
Abstract
The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic
[...] Read more.
The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming via a single onboard camera and Global Positioning System (GPS) information. A two-stage processing procedure is proposed based on image segmentation and classification. Relevant parameters such as camera parameters, light parameters, dropping zone dimensions, and drone height from the ground are taken into account in the classification. The experimental results indicate that the proposed approach provides a fast method with reliable accuracy based on low-order calculations.
Full article
(This article belongs to the Special Issue The Applications of Drones in Logistics 2nd Edition)
►
Show Figures
Open AccessArticle
Extracting Micro-Doppler Features from Multi-Rotor Unmanned Aerial Vehicles Using Time-Frequency Rotation Domain Concentration
Drones 2024, 8(1), 20; https://doi.org/10.3390/drones8010020 - 12 Jan 2024
Abstract
This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing
[...] Read more.
This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing competitive learning particle swarm optimization (CLPSO), our approach divides population dynamics into three subgroups, each employing unique optimization mechanisms to enhance local search capabilities. This method overcomes limitations in traditional Particle Swarm Optimization (PSO) algorithms, specifically in achieving global optimal solutions. Our simulation and experimental results demonstrate the method’s efficiency and accuracy in extracting micro-Doppler features of rotary-wing UAVs. This advancement not only facilitates UAV detection and identification but also significantly contributes to the fields of UAV monitoring and airspace security.
Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
►▼
Show Figures
Figure 1
Open AccessArticle
IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform
Drones 2024, 8(1), 19; https://doi.org/10.3390/drones8010019 - 12 Jan 2024
Abstract
Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field
[...] Read more.
Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities.
Full article
(This article belongs to the Special Issue Resilient UAV Autonomy and Remote Sensing)
►▼
Show Figures
Figure 1
Open AccessArticle
Multi-UAV Path Planning and Following Based on Multi-Agent Reinforcement Learning
Drones 2024, 8(1), 18; https://doi.org/10.3390/drones8010018 - 11 Jan 2024
Abstract
►▼
Show Figures
Dedicated to meeting the growing demand for multi-agent collaboration in complex scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning and the following approach. Current multi-agent path planning predominantly relies on grid-based maps, whereas our proposed approach utilizes laser scan data as
[...] Read more.
Dedicated to meeting the growing demand for multi-agent collaboration in complex scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning and the following approach. Current multi-agent path planning predominantly relies on grid-based maps, whereas our proposed approach utilizes laser scan data as input, providing a closer simulation of real-world applications. In this approach, the unmanned aerial vehicle (UAV) uses the soft actor–critic (SAC) algorithm as a planner and trains its policy to converge. This policy enables end-to-end processing of laser scan data, guiding the UAV to avoid obstacles and reach the goal. At the same time, the planner incorporates paths generated by a sampling-based method as following points. The following points are continuously updated as the UAV progresses. Multi-UAV path planning tasks are facilitated, and policy convergence is accelerated through sharing experiences among agents. To address the challenge of UAVs that are initially stationary and overly cautious near the goal, a reward function is designed to encourage UAV movement. Additionally, a multi-UAV simulation environment is established to simulate real-world UAV scenarios to support training and validation of the proposed approach. The simulation results highlight the effectiveness of the presented approach in both the training process and task performance. The presented algorithm achieves an 80% success rate to guarantee that three UAVs reach the goal points.
Full article
Figure 1
Open AccessArticle
Firefighting Drone Configuration and Scheduling for Wildfire Based on Loss Estimation and Minimization
Drones 2024, 8(1), 17; https://doi.org/10.3390/drones8010017 - 10 Jan 2024
Abstract
Drones have been increasingly used in firefighting to improve the response speed and reduce the dangers to human firefighters. However, few studies simultaneously consider fire spread prediction, drone scheduling, and the configuration of supporting staff and supplies. This paper presents a mathematical model
[...] Read more.
Drones have been increasingly used in firefighting to improve the response speed and reduce the dangers to human firefighters. However, few studies simultaneously consider fire spread prediction, drone scheduling, and the configuration of supporting staff and supplies. This paper presents a mathematical model that estimates wildfire spread and economic losses simultaneously. The model can also help us to determine the minimum number of firefighting drones in preparation for wildfire in a given wild area. Next, given a limited number of firefighting drones, we propose a method for scheduling the drones in response to wildfire occurrence to minimize the expected loss using metaheuristic optimization. We demonstrate the performance advantages of water wave optimization over a set of other metaheuristic optimization algorithms on 72 test instances simulated on selected suburb areas of Hangzhou, China. Based on the optimization results, we can pre-define a comprehensive plan of scheduling firefighting drone and configuring support staff in response to a set of scenarios of wildfire occurrences, significantly improving the emergency response efficiency and reducing the potential losses.
Full article
(This article belongs to the Special Issue Drones in the Wild)
►▼
Show Figures
Figure 1
Open AccessArticle
Wind Tunnel Balance Measurements of Bioinspired Tails for a Fixed Wing MAV
by
, , , and
Drones 2024, 8(1), 16; https://doi.org/10.3390/drones8010016 - 10 Jan 2024
Abstract
►▼
Show Figures
Bird tails play a significant role in aerodynamics and stability during flight. This paper investigates the use of bioinspired horizontal stabilizers for Micro Air Vehicles (MAVs) with Zimmerman wing-body geometry. Five configurations of bioinspired horizontal stabilizers are presented. Then, 3-component external balance force
[...] Read more.
Bird tails play a significant role in aerodynamics and stability during flight. This paper investigates the use of bioinspired horizontal stabilizers for Micro Air Vehicles (MAVs) with Zimmerman wing-body geometry. Five configurations of bioinspired horizontal stabilizers are presented. Then, 3-component external balance force measurements of each horizontal stabilizer are performed in the wind tunnel. The Squared-Fan-Shaped Horizontal Stabilizer (HSF-tail) is selected as the optimal horizontal stabilizer that provides the highest aerodynamic efficiency during cruise flight while maintaining high longitudinal stability on the vehicle. The integration of the HSF-tail increases the aerodynamic efficiency by more than up to a maximum of compared to the other alternatives while maintaining the lowest aerodynamic drag value during the cruise phase. Furthermore, balance measurements to analyze the influence of the HSF-tail deflection on the aerodynamic coefficients are conducted, resulting in increased lift force and reduced aerodynamic drag with negative tail deflections. Lastly, the experimental data is validated with CFD-RANS steady simulations for low angles of attack, obtaining a relative difference on the measurement around for the aerodynamic drag coefficient and around for the lift coefficient during the cruise flight that demonstrates a high degree of accuracy in the aerodynamic coefficients obtained by external balance in the wind tunnel. This work represents a novel approach through the implementation of a horizontal stabilizer inspired by the structure of the tails of birds that is expected to yield significant advancements in both stability and aerodynamic efficiency, with the potential to revolutionize MAV technology.
Full article
Figure 1
Open AccessArticle
Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier
Drones 2024, 8(1), 15; https://doi.org/10.3390/drones8010015 - 09 Jan 2024
Abstract
Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments.
[...] Read more.
Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies.
Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
►▼
Show Figures
Figure 1
Open AccessArticle
Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism
by
and
Drones 2024, 8(1), 14; https://doi.org/10.3390/drones8010014 - 07 Jan 2024
Abstract
The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable
[...] Read more.
The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.
Full article
(This article belongs to the Special Issue The Applications of Drones in Logistics 2nd Edition)
►▼
Show Figures
Figure 1
Open AccessArticle
Drone Multiline Light Detection and Ranging Data Filtering in Coastal Salt Marshes Using Extreme Gradient Boosting Model
Drones 2024, 8(1), 13; https://doi.org/10.3390/drones8010013 - 04 Jan 2024
Abstract
Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and
[...] Read more.
Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.
Full article
(This article belongs to the Special Issue Resilient UAV Autonomy and Remote Sensing)
►▼
Show Figures
Figure 1
Open AccessArticle
Joint Trajectory Design and Resource Optimization in UAV-Assisted Caching-Enabled Networks with Finite Blocklength Transmissions
by
and
Drones 2024, 8(1), 12; https://doi.org/10.3390/drones8010012 - 04 Jan 2024
Abstract
In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote
[...] Read more.
In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote base station (BS), and there are no direct connections between the BS and the GUEs. To address this, we employ a UAV with a limited caching capacity to assist the BS in completing the communication. The UAV can either request uncached content from the BS and then serve the GUEs or directly transmit cached content to the GUEs. In this paper, we first introduce the decoding error rate within the FBL regime and explore caching policies for the UAV. Subsequently, we formulate an optimization problem aimed at minimizing the average maximum end-to-end decoding error rate across all GUEs while considering the coding length and maximum UAV transmission power constraints. We propose a two-step alternating optimization scheme embedded within a deep deterministic policy gradient (DDPG) algorithm to jointly determine the UAV trajectory and transmission power allocations, as well as blocklength of downloading phase, and our numerical results show that the combined learning-optimization algorithm efficiently addresses the considered problem. In particular, it is shown that a well-designed UAV trajectory, relaxing the FBL constraint, increasing the cache size, and providing a higher UAV transmission power budget all lead to improved performance.
Full article
(This article belongs to the Special Issue Next Generation UAV-Assisted Wireless Networks)
►▼
Show Figures
Figure 1
Open AccessArticle
Optimal Configuration of Heterogeneous Swarm for Cooperative Detection with Minimum DOP Based on Nested Cones
Drones 2024, 8(1), 11; https://doi.org/10.3390/drones8010011 - 02 Jan 2024
Abstract
When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configuration design method focused on the heterogeneous unmanned cooperative swarm based
[...] Read more.
When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configuration design method focused on the heterogeneous unmanned cooperative swarm based on the nested cone model. The proposed method first divides the swarm into different groups according to the performances of platforms and then uses a conical nested configuration to arrange the placement of each node independently. The paper considers the problem of the inaccurate prior position of the target and replaces the single-point DOP with the average DOP on the prior region of the target as the optimization objective. Considering the unavoidable positioning errors in engineering practice, this paper provides the optimal configuration of the detection group (DG) and anchor group (AG) in the swarm to reduce the impact caused by positioning errors of detection nodes. We set a certain swarm consisting of 3 types of platforms to design the configuration by simulation experiments and find the optimal parameters for nested cones to realize accurate detection.
Full article
(This article belongs to the Special Issue Cooperation of Drones and Other Manned/Unmanned Systems)
►▼
Show Figures
Figure 1
Open AccessFeature PaperReview
Multi-Robot Coverage Path Planning for the Inspection of Offshore Wind Farms: A Review
Drones 2024, 8(1), 10; https://doi.org/10.3390/drones8010010 - 31 Dec 2023
Abstract
►▼
Show Figures
Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation
[...] Read more.
Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation in inspections can reduce human effort and financial costs. Despite the benefits, research on automating inspection is sparse. This work proposes that OWT inspection can be described as a multi-robot coverage path planning problem. Reviews of multi-robot coverage exist, but to the best of our knowledge, none captures the domain-specific aspects of an OWT inspection. In this paper, we present a review on the current state of the art of multi-robot coverage to identify gaps in research relating to coverage for OWT inspection. To perform a qualitative study, the PICo (population, intervention, and context) framework was used. The retrieved works are analysed according to three aspects of coverage approaches: environmental modelling, decision making, and coordination. Based on the reviewed studies and the conducted analysis, candidate approaches are proposed for the structural coverage of an OWT. Future research should involve the adaptation of voxel-based ray-tracing pose generation to UAVs and exploration, applying semantic labels to tasks to facilitate heterogeneous coverage and semantic online task decomposition to identify the coverage target during the run time.
Full article
Figure 1
Open AccessArticle
Automated Flight Technology for Integral Path Planning and Trajectory Tracking of the UAV
Drones 2024, 8(1), 9; https://doi.org/10.3390/drones8010009 - 30 Dec 2023
Abstract
►▼
Show Figures
In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path
[...] Read more.
In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.
Full article
Figure 1
Open AccessFeature PaperArticle
Evaluation of Fissures and Cracks in Bridges by Applying Digital Image Capture Techniques Using an Unmanned Aerial Vehicle
Drones 2024, 8(1), 8; https://doi.org/10.3390/drones8010008 - 30 Dec 2023
Abstract
The evaluation of cracks and fissures in bridge structures is essential to ensure the long-term safety, durability, and functionality of these infrastructures. In this sense, processing grayscale images and adjusting brightness and contrast levels can improve the visibility of cracks and fissures in
[...] Read more.
The evaluation of cracks and fissures in bridge structures is essential to ensure the long-term safety, durability, and functionality of these infrastructures. In this sense, processing grayscale images and adjusting brightness and contrast levels can improve the visibility of cracks and fissures in bridge structures. These techniques, complemented by professional expertise and efficient inspection tools such as Unmanned Aerial Vehicles (UAVs), allow for a comprehensive and accurate structural integrity assessment. This study used the edge detection technique to analyze photographs obtained with a low-cost UAV as a means of image capture. This tool was used to reach hard-to-reach areas where there could be damage, thus making it easier to detect fissures or cracks. To capture the failures, two case studies, a small bridge and a large bridge, were selected, both located in Concepción City in southern Chile. During both inspections, cracks were detected that could affect the structure of the bridges in the future. To analyze these findings, ImageJ software 1.54h was used, which allowed the length and thickness of the cracks to be measured and evaluated. In addition, to validate the procedure proposed, real values manually measured on-site were compared with those delivered by the software analyses, where no statistically significant differences were found. With the method presented in this study, it was possible to quantify the damage, following the bridge maintenance standards established by the Ministry of Public Works of Chile, whose inspection criteria can be applied to other projects worldwide.
Full article
(This article belongs to the Special Issue Application of UAS in Construction)
►▼
Show Figures
Figure 1
Open AccessArticle
Enhancing Urban Mobility through Traffic Management with UAVs and VLC Technologies
Drones 2024, 8(1), 7; https://doi.org/10.3390/drones8010007 - 29 Dec 2023
Abstract
This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities
[...] Read more.
This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities poses significant challenges to the daily mobility of citizens, rendering traditional ground-based traffic management methods increasingly inadequate. In this context, UAVs provide a distinctive perspective for real-time traffic monitoring and congestion detection using the YOLO algorithm. Through image capture and processing, UAVs can rapidly identify congested areas and transmit this information to ground-based traffic lights, facilitating dynamic traffic control adjustments. Moreover, VLC establishes a communication link between UAVs and traffic lights that complements existing RF-based solutions, underscoring visible light’s potential as a reliable and energy-efficient communication medium. In addition to integrating UAVs and VLC, we propose a new communication protocol and messaging system for this framework, enhancing its adaptability to varying traffic flows. This research represents a significant stride toward developing more efficient, sustainable, and resilient urban transportation systems.
Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
►▼
Show Figures
Figure 1
Open AccessFeature PaperArticle
Paving the Way for Last-Mile Delivery in Greece: Data-Driven Performance Analysis with a Customized Quadrotor
by
, , , , and
Drones 2024, 8(1), 6; https://doi.org/10.3390/drones8010006 - 29 Dec 2023
Abstract
Cargo drones are a cutting-edge solution that is becoming increasingly popular as flight times extend and regulatory frameworks evolve to accommodate new delivery methods. The aim of this paper was to comprehensively understand cargo drone dynamics and guide their effective deployment in Greece.
[...] Read more.
Cargo drones are a cutting-edge solution that is becoming increasingly popular as flight times extend and regulatory frameworks evolve to accommodate new delivery methods. The aim of this paper was to comprehensively understand cargo drone dynamics and guide their effective deployment in Greece. A 5 kg payload quadrotor with versatile loading mechanisms, including a cable-suspended system and an ultra-light box, was manufactured and tested in five Greek cities. A comprehensive performance evaluation and analysis of flight range, energy consumption, altitude-related data accuracy, cost-effectiveness, and environmental were conducted. Based on hands-on experimentation and real-world data collection, the study proposes a novel data-driven methodology for strategically locating charging stations and addressing uncertainties like weather conditions and battery discharge during flights. Results indicate significant operational cost savings (89.44%) and a maximum emissions reduction (77.42%) compared to conventional transportation. The proposed strategic placement of charging stations led to substantial reductions in travel distance (41.03%) and energy consumption (56.73%) across five case studies in Greek cities.
Full article
(This article belongs to the Special Issue Advances of Drones in Logistics)
►▼
Show Figures
Figure 1
Open AccessArticle
Comparison of Multiple Models in Decentralized Target Estimation by a UAV Swarm
Drones 2024, 8(1), 5; https://doi.org/10.3390/drones8010005 - 27 Dec 2023
Abstract
The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form
[...] Read more.
The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form is employed to provide an estimate of the target state. In the prediction step of the filter, we adopt and compare three different models for the target motion with increasing levels of complexity, namely, a constant velocity (CV), a constant turn (CT), and a full-state (FS) model. Software-in-the-loop (SITL) simulations are conducted in ROS/Gazebo to compare the performance of the three models. The coupling between the formation and estimation tasks is evaluated since the tracking task is affected by the outcome of the estimation process.
Full article
(This article belongs to the Special Issue Emerging Technologies and Innovations in Unmanned Aerial Vehicle Control Systems)
►▼
Show Figures
Figure 1
Open AccessArticle
A Co-Adaptation Method for Resilience Rebound in Unmanned Aerial Vehicle Swarms in Surveillance Missions
Drones 2024, 8(1), 4; https://doi.org/10.3390/drones8010004 - 26 Dec 2023
Abstract
►▼
Show Figures
An unmanned aerial vehicle (UAV) swarm is a fast-moving system where self-adaption is necessary when conducting a mission. The major causative factors of mission failures are inevitable disruptive events and uncertain threats. Given the unexpected disturbances of events and threats, it is important
[...] Read more.
An unmanned aerial vehicle (UAV) swarm is a fast-moving system where self-adaption is necessary when conducting a mission. The major causative factors of mission failures are inevitable disruptive events and uncertain threats. Given the unexpected disturbances of events and threats, it is important to study how a UAV swarm responds and enable the swarm to enhance resilience and alleviate negative influences. Cooperative adaptation must be established between the swarm’s structure and dynamics, such as communication links and UAV states. Thus, based on previous structural adaptation and dynamic adaptation models, we provide a co-adaptation model for UAV swarms that combines a swarm’s structural characteristics with its dynamic characteristics. The improved model can deal with malicious events and contribute to a rebound in the swarm’s performance. Based on the proposed co-adaptation model, an improved resilience metric revealing the discrepancy between the minimum performance and the standard performance is proposed. The results from our simulation experiments show that the surveillance performance of a UAV swarm bounces back to its initial state after disruptions happen in co-adaptation cases. This metric demonstrates that our model can contribute towards the swarm’s overall systemic resiliency by withstanding and resisting unpredictable threats and disruptions. The model and metric proposed in this article can help identify best practices in improving swarm resilience.
Full article
Figure 1
Open AccessArticle
Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs
Drones 2024, 8(1), 3; https://doi.org/10.3390/drones8010003 - 25 Dec 2023
Abstract
►▼
Show Figures
Accurate tracking and predicting unmanned aerial vehicle (UAV) trajectories are essential to ensure mission success, equipment safety, and data accuracy. Maneuverable UAVs exhibit complex and dynamic motion, and conventional tracking algorithms that rely on predefined models perform poorly when unknown parameters are used.
[...] Read more.
Accurate tracking and predicting unmanned aerial vehicle (UAV) trajectories are essential to ensure mission success, equipment safety, and data accuracy. Maneuverable UAVs exhibit complex and dynamic motion, and conventional tracking algorithms that rely on predefined models perform poorly when unknown parameters are used. To address this issue, this paper introduces a hybrid dual-scale neural network model based on the generalized regression multi-model and cubature information filter (GRMM-CIF) framework. We have established the GRMM-CIF filtering structure to differentiate motion modes and reduce measurement noise. Furthermore, considering trajectory datasets and rates of motion change, a neural network at different scales will be designed. We propose the dual-scale bidirectional long short-term memory (DS-Bi-LSTM) algorithm to address prediction delays in a multi-model context. Additionally, we employ scale sliding windows and threshold-based decision-making to achieve dual-scale trajectory reconstruction, ultimately enhancing tracking accuracy. Simulation results confirm the effectiveness of our approach in handling the uncertainty of UAV motion and achieving precise estimations.
Full article
Figure 1
Open AccessFeature PaperArticle
Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring
Drones 2024, 8(1), 2; https://doi.org/10.3390/drones8010002 - 24 Dec 2023
Abstract
Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus),
[...] Read more.
Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.
Full article
(This article belongs to the Special Issue Advances of Drones in Wildlife Research)
►▼
Show Figures
Figure 1
Journal Menu
► ▼ Journal Menu-
- Drones Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Agriculture, Applied Sciences, Drones, Remote Sensing, Sensors
Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming
Topic Editors: Monica Herrero-Huerta, Jose A. Jiménez-Berni, Shangpeng Sun, Ittai Herrmann, Diego González-AguileraDeadline: 31 January 2024
Topic in
Aerospace, Applied Sciences, Automation, Drones, Remote Sensing, Sensors
Target Tracking, Guidance, and Navigation for Autonomous Systems
Topic Editors: Won-Sang Ra, Shaoming He, Ivan MasmitjaDeadline: 20 February 2024
Topic in
Drones, Fire, Forests, Remote Sensing, Sustainability
Application of Remote Sensing in Forest Fire
Topic Editors: Aqil Tariq, Na ZhaoDeadline: 31 March 2024
Topic in
Drones, IJGI, Land, Remote Sensing
Advances in Earth Observation and Geosciences
Topic Editors: Diego González-Aguilera, Pablo Rodríguez-GonzálvezDeadline: 30 April 2024
Conferences
Special Issues
Special Issue in
Drones
Application and Challenges of UAV in Space-Air-Ground Integrated Communication Network
Guest Editors: Peiying Zhang, Sheng Wu, Zakarya Muhammad, Guanjun XuDeadline: 16 January 2024
Special Issue in
Drones
Recent Advances in UAVs for Wireless Networks
Guest Editors: Shiva Raj Pokhrel, Hai Vu, Jinho ChoiDeadline: 31 January 2024
Special Issue in
Drones
Application of UAS in Construction
Guest Editors: Sungjin Kim, Javier IrizarryDeadline: 15 February 2024
Special Issue in
Drones
Unmanned Aerial Vehicles in Atmospheric Research
Guest Editors: Miroslaw Zimnoch, Paweł ĆwiąkałaDeadline: 20 February 2024
Topical Collections
Topical Collection in
Drones
Feature Papers Collection of the World’s Top 2% Scientists in Drones
Collection Editors: Diego González-Aguilera, Pablo Rodríguez-Gonzálvez
Topical Collection in
Drones
Feature Papers of Drones Volume II
Collection Editors: Diego González-Aguilera, Pablo Rodríguez-Gonzálvez
Topical Collection in
Drones
Editorial Board Members’ Collection Series: Drones in Emergencies Operations
Collection Editors: Gino Lim, Houbing Song, Israel Quintanilla García