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
Research on Vision-Based Servoing and Trajectory Prediction Strategy for Capturing Illegal Drones
Drones 2024, 8(4), 127; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040127 - 28 Mar 2024
Abstract
A proposed strategy for managing airspace and preventing illegal drones from compromising security involves the use of autonomous drones equipped with three key functionalities. Firstly, the implementation of YOLO-v5 technology allows for the identification of illegal drones and the establishment of a visual-servo
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A proposed strategy for managing airspace and preventing illegal drones from compromising security involves the use of autonomous drones equipped with three key functionalities. Firstly, the implementation of YOLO-v5 technology allows for the identification of illegal drones and the establishment of a visual-servo system to determine their relative position to the autonomous drone. Secondly, an extended Kalman filter algorithm predicts the flight trajectory of illegal drones, enabling the autonomous drone to compensate in advance and significantly enhance the capture success rate. Lastly, to ensure system robustness and suppress interference from illegal drones, an adaptive fast nonsingular terminal sliding mode technique is employed. This technique achieves finite time convergence of the system state and utilizes delay estimation technology for the real-time compensation of unknown disturbances. The stability of the closed-loop system is confirmed through Lyapunov theory, and a model-based hardware-in-the-loop simulation strategy is adopted to streamline system development and improve efficiency. Experimental results demonstrate that the designed autonomous drone accurately predicts the trajectory of illegal drones, effectively captures them using a robotic arm, and maintains stable flight throughout the process.
Full article
(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems)
Open AccessArticle
HiFly-Dragon: A Dragonfly Inspired Flapping Flying Robot with Modified, Resonant, Direct-Driven Flapping Mechanisms
by
He Ma, Peiyi Gong, Yuqiang Tian, Qingnan Wu, Min Pan, Hao Yin, Youjiang Liu and Chilai Chen
Drones 2024, 8(4), 126; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040126 - 28 Mar 2024
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This paper describes a dragonfly-inspired Flapping Wing Micro Air Vehicle (FW-MAV), named HiFly-Dragon. Dragonflies exhibit exceptional flight performance in nature, surpassing most of the other insects, and benefit from their abilities to independently move each of their four wings, including adjusting the flapping
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This paper describes a dragonfly-inspired Flapping Wing Micro Air Vehicle (FW-MAV), named HiFly-Dragon. Dragonflies exhibit exceptional flight performance in nature, surpassing most of the other insects, and benefit from their abilities to independently move each of their four wings, including adjusting the flapping amplitude and the flapping amplitude offset. However, designing and fabricating a flapping robot with multi-degree-of-freedom (multi-DOF) flapping driving mechanisms under stringent size, weight, and power (SWaP) constraints poses a significant challenge. In this work, we propose a compact microrobot dragonfly with four tandem independently controllable wings, which is directly driven by four modified resonant flapping mechanisms integrated on the Printed Circuit Boards (PCBs) of the avionics. The proposed resonant flapping mechanism was tested to be able to enduringly generate 10 gf lift at a frequency of 28 Hz and an amplitude of 180° for a single wing with an external DC power supply, demonstrating the effectiveness of the resonance and durability improvement. All of the mechanical parts were integrated on two PCBs, and the robot demonstrates a substantial weight reduction. The latest prototype has a wingspan of 180 mm, a total mass of 32.97 g, and a total lift of 34 gf. The prototype achieved lifting off on a balance beam, demonstrating that the directly driven robot dragonfly is capable of overcoming self-gravity with onboard batteries.
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Open AccessArticle
Research on A Global Path-Planning Algorithm for Unmanned Arial Vehicle Swarm in Three-Dimensional Space Based on Theta*–Artificial Potential Field Method
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Wen Zhao, Liqiao Li, Yingqi Wang, Hanwen Zhan, Yiqi Fu and Yunfei Song
Drones 2024, 8(4), 125; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040125 - 27 Mar 2024
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The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the
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The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the robustness of drone formations under the leader–follower mode is low, and effectively handling obstacles within the environment is challenging. To address these issues, this study proposes a virtual leader mode for drone formation flight and introduces a new Theta*–APF method for three-dimensional space drone swarm path planning. This algorithm optimizes the A* algorithm by transforming it into an omnidirectional forward Theta* algorithm. It also enhances the heuristic function by incorporating artificial potential field methods in a three-dimensional environment. Formation organization and control of UAVs is achieved using speed-control modes. Compared to the conventional A* algorithm, the Theta*–APF algorithm reduces the search time by about 60% and the trip length by 10%, in addition to the safer flight of the UAV formation, which is subject to artificial potential field repulsion by about 42%.
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Open AccessArticle
Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
by
Ziyu Cao, Zhihui Du and Jianhua Yang
Drones 2024, 8(4), 124; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040124 - 27 Mar 2024
Abstract
Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many
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Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many indoor and outdoor scenes usually have a prior topological map, such as road navigation maps, satellite road network maps, indoor computer-aided design (CAD) maps, etc., this paper incorporated this information into the autonomous exploration framework and proposed an innovative topological map-based autonomous exploration method for large-scale scenes. The key idea of the proposed method is to plan exploration paths with long-term benefits by tightly merging the information between robot-collected and prior topological maps. The exploration path follows a global exploration strategy but prioritizes exploring scenes outside the prior information, thereby preventing the robot from revisiting explored areas and avoiding the duplication of any effort. Furthermore, to improve the stability of exploration efficiency, the exploration path is further refined by assessing the cost and reward of each candidate viewpoint through a fast method. Simulation experimental results demonstrated that the proposed method outperforms state-of-the-art autonomous exploration methods in efficiency and stability and is more suitable for exploration in large-scale scenes. Real-world experimentation has also proven the effectiveness of our proposed method.
Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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Open AccessArticle
New Concept of Smart UAS-GCP: A Tool for Precise Positioning in Remote-Sensing Applications
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Nicola Angelo Famiglietti, Pietro Miele, Antonino Memmolo, Luigi Falco, Angelo Castagnozzi, Raffaele Moschillo, Carmine Grasso, Robert Migliazza, Giulio Selvaggi and Annamaria Vicari
Drones 2024, 8(4), 123; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040123 - 26 Mar 2024
Abstract
Today, ground control points (GCPs) represent indispensable tools for products’ georeferencing in all the techniques concerning remote sensing (RS), particularly in monitoring activities from unmanned aircraft system (UAS) platforms. This work introduces an innovative tool, smart GCPs, which combines different georeferencing procedures, offering
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Today, ground control points (GCPs) represent indispensable tools for products’ georeferencing in all the techniques concerning remote sensing (RS), particularly in monitoring activities from unmanned aircraft system (UAS) platforms. This work introduces an innovative tool, smart GCPs, which combines different georeferencing procedures, offering a range of advantages. It can serve three fundamental purposes concurrently: (1) as a drone takeoff platform; (2) as a base station, allowing the acquisition of raw global navigation satellite system (GNSS) data for post-processed kinematic (PPK) surveys or by providing real-time GNSS corrections for precision positioning; (3) as a rover in the network real-time kinematic (NRTK) mode, establishing its position in real time with centimetric precision. The prototype has undergone testing in a dedicated study area, yielding good results for all three geodetic correction techniques: PPK, RTK, and GCP, achieving centimeter-level accuracy. Nowadays, this versatile prototype represents a unique external instrument, which is also easily transportable and able to connect to the GNSS RING network, obtaining real-time positioning corrections for a wide range of applications that require precise positioning. This capability is essential for environmental applications that require a multitemporal UAS-based study. When the real-time RING data are accessible to the scientific community operating in RS surveying, this work could be a helpful guide for researchers approaching such investigations.
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(This article belongs to the Special Issue UAV Positioning: From Ground to Sky)
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Energy-Efficient Device-to-Device Communications for Green Internet of Things Using Unmanned Aerial Vehicle-Mounted Intelligent Reflecting Surface
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Fangqing Tan, Shuo Pang, Yashuai Cao, Hongbin Chen and Tiejun Lv
Drones 2024, 8(4), 122; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040122 - 26 Mar 2024
Abstract
The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of
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The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance.
Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
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Open AccessArticle
Optimization-Based Control for a Large-Scale Electrical Vertical Take-Off and Landing during an Aircraft’s Vertical Take-Off and Landing Phase with Variable-Pitch Propellers
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Luyuhang Duan, Yunhan He, Li Fan, Wei Qiu, Guangwei Wen and Yun Xu
Drones 2024, 8(4), 121; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040121 - 26 Mar 2024
Abstract
The UAV industry has witnessed an unprecedented boom in recent years. Among various kinds of UAV platforms, the vertical take-off and landing (VTOL) aircraft with fixed-wing configurations has received more and more attention due to its flexibility and long-distance flying abilities. However, due
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The UAV industry has witnessed an unprecedented boom in recent years. Among various kinds of UAV platforms, the vertical take-off and landing (VTOL) aircraft with fixed-wing configurations has received more and more attention due to its flexibility and long-distance flying abilities. However, due to the fact that the advance ratio of regular propeller systems during the cruise phase is significantly higher than that during the VTOL phase, a variable-pitch propeller system is proposed and designed which can be applied without additional propulsion mechanisms during both flying stages. Thus, a VTOL aircraft platform is proposed based on the propulsion system constructed of variable-pitch propellers, and appropriate control manners are precisely analyzed, especially during its VTOL phase. As a basic propulsion system, a nonlinear model for variable-pitch propellers is constructed, and an optimization-based control allocation module is developed because of its multi-solution and high-order characteristics. Finally, the objective function is designed according to the stability and energy consumption requirements. Simulation experiments demonstrate that the proposed controller is able to lower energy consumption and maintain the stability of the aircraft while tracking aggressive trajectories for large-scale VTOLs with noises at the same time.
Full article
(This article belongs to the Special Issue A UAV Platform for Flight Dynamics and Control System)
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Open AccessFeature PaperArticle
Intelligent Scheduling Technology of Swarm Intelligence Algorithm for Drone Path Planning
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Zhipeng Meng, Dongze Li, Yong Zhang and Haoquan Yan
Drones 2024, 8(4), 120; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040120 - 26 Mar 2024
Abstract
Different kinds of swarm intelligence algorithm obtain superior performances in solving complex optimization problems and have been widely used in path planning of drones. Due to their own characteristics, the optimization results may vary greatly in different dynamic environments. In this paper, a
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Different kinds of swarm intelligence algorithm obtain superior performances in solving complex optimization problems and have been widely used in path planning of drones. Due to their own characteristics, the optimization results may vary greatly in different dynamic environments. In this paper, a scheduling technology for swarm intelligence algorithms based on deep Q-learning is proposed to intelligently select algorithms to realize 3D path planning. It builds a unique path point database and two basic principles are proposed to guide model training. Path planning and network learning are separated by the proposed separation principle and the optimal selection principle ensures convergence of the model. Aiming at the problem of reward sparsity, the comprehensive cost of each path point in the whole track sequence is regarded as a dynamic reward. Through the investigation of dynamic environment conditions such as different distances and threats, the effectiveness of the proposed method is validated.
Full article
(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)
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Open AccessArticle
Influence of VF and SOR-Filtering Methods on Tree Height Inversion Using Unmanned Aerial Vehicle LiDAR Data
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Di Duan, Yuncheng Deng, Jianpeng Zhang, Jinliang Wang and Pinliang Dong
Drones 2024, 8(4), 119; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040119 - 23 Mar 2024
Abstract
Forests, as the main body of the terrestrial ecosystem, have long been focal points for accurate structural parameter extraction. Among these parameters, tree height is a fundamental measurement factor that plays an important role in monitoring forest structure and biomass. The emergence of
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Forests, as the main body of the terrestrial ecosystem, have long been focal points for accurate structural parameter extraction. Among these parameters, tree height is a fundamental measurement factor that plays an important role in monitoring forest structure and biomass. The emergence of unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology has provided a strong guarantee of the acquisition of forest tree height parameters. However, UAV-LiDAR point cloud data have problems such as a large volume and data redundancy, and different point cloud data processing methods have different effects. Based on voxel filtering (VF) and statistical outlier removal (SOR)point cloud data processing experimental analysis, this study explored the influence of different filtering methods on the forest tree height inversion efficiency and accuracy. First, the point cloud data processed by VF is significantly better than that of SOR in terms of point cloud number, file size, running time, etc. The number of point clouds for VF decreased by an average of 96.91% compared with the original point clouds. Second, the VF tree height inversion accuracy was better than the tree height inversion data using SOR. The average accuracy of VF was 96.24%, while that of SOR was 94.17%. In summary, VF can effectively reduce data redundancy and improve tree height inversion accuracy.
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(This article belongs to the Section Drones in Agriculture and Forestry)
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Open AccessArticle
Ground Effect on the Thrust Performance of Staggered Rotor System
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He Zhu, Shaoxiong Wei, Hong Nie, Yuhao Du and Xiaohui Wei
Drones 2024, 8(4), 118; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040118 - 23 Mar 2024
Abstract
In this study, the thrust performance of a staggered rotor system in-ground effect (IGE) and out-of-ground effect (OGE) while considering the interaction on wake characteristics were investigated experimentally. A thorough comprehension of their performance holds significant importance for trajectory planning, aircraft design, landing
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In this study, the thrust performance of a staggered rotor system in-ground effect (IGE) and out-of-ground effect (OGE) while considering the interaction on wake characteristics were investigated experimentally. A thorough comprehension of their performance holds significant importance for trajectory planning, aircraft design, landing safety, and energy-efficient landings. The complex interactions within staggered rotor systems and the impact of ground effects make rotor distance and ground interactions critical factors influencing near-ground flight performance. The study investigated the influence and enhancements of rotor thrust performance through an examination of rotor speed, lateral distance, and altitude. Experimental tests were conducted on two small-scale rotor models to assess the effects of these parameters. These experiments compared the performance of staggered rotor systems with isolated rotors, analyzing the competition mechanism between the thrust loss caused by interference and the thrust gain of rotors IGE. Furthermore, emphasis was placed on analyzing the thrust gain issues exhibited by staggered rotor systems under the condition of H = 2R. Additionally, the analysis was focused on identifying prominent relative positions for thrust performance and parameters for improving thrust performance in ground effects in staggered rotor systems.
Full article
(This article belongs to the Special Issue Dynamics Modeling and Conceptual Design of UAVs)
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Open AccessArticle
Research on Service Function Chain Embedding and Migration Algorithm for UAV IoT
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Xi Wang, Shuo Shi and Chenyu Wu
Drones 2024, 8(4), 117; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040117 - 22 Mar 2024
Abstract
This paper addresses the challenge of managing service function chaining (SFC) in an unmanned aerial vehicle (UAV) IoT, a dynamic network that integrates UAVs and IoT devices for various scenarios. To enhance the service quality and user experience of the UAV IoT, network
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This paper addresses the challenge of managing service function chaining (SFC) in an unmanned aerial vehicle (UAV) IoT, a dynamic network that integrates UAVs and IoT devices for various scenarios. To enhance the service quality and user experience of the UAV IoT, network functions must be flexibly configured and adjusted based on varying service demands and network situations. This paper presents a model for calculating benefits and an agile algorithm for embedding and migrating SFC based on particle swarm optimization (PSO). The model takes into account multiple factors such as SFC quality, resource utilization, and migration cost. It aims to maximize the SFC benefit and minimize the migration times. The algorithm leverages PSO’s global search and fast convergence to identify the optimal or near-optimal SFC placement and update it when the network state changes. Simulation experiments demonstrate that the proposed method improves network resource efficiency and outperforms existing methods. This paper presents a new idea and method for managing SFC in UAV IoT.
Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
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Open AccessReview
A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs
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Serhat Sönmez, Matthew J. Rutherford and Kimon P. Valavanis
Drones 2024, 8(4), 116; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040116 - 22 Mar 2024
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Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying
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Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.
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Open AccessArticle
Estimating Total Length of Partially Submerged Crocodylians from Drone Imagery
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Clément Aubert, Gilles Le Moguédec, Alvaro Velasco, Xander Combrink, Jeffrey W. Lang, Phoebe Griffith, Gualberto Pacheco-Sierra, Etiam Pérez, Pierre Charruau, Francisco Villamarín, Igor J. Roberto, Boris Marioni, Joseph E. Colbert, Asghar Mobaraki, Allan R. Woodward, Ruchira Somaweera, Marisa Tellez, Matthew Brien and Matthew H. Shirley
Drones 2024, 8(3), 115; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030115 - 21 Mar 2024
Abstract
Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer
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Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer alternative for classification. We evaluated the effectiveness of drone photos combined with head length allometric relationships to estimate total length, and propose a standardized method for drone-based crocodylian demographic classification. We evaluated error sources related to drone flight parameters using standardized targets. An allometric framework correlating head to total length for 17 crocodylian species was developed, incorporating confidence intervals to account for imprecision sources (e.g., allometric accuracy, head inclination, observer bias, terrain variability). This method was applied to wild crocodylians through drone photography. Target measurements from drone imagery, across various resolutions and sizes, were consistent with their actual dimensions. Terrain effects were less impactful than Ground-Sample Distance (GSD) errors from photogrammetric software. The allometric framework predicted lengths within ≃11–18% accuracy across species, with natural allometric variation among individuals explaining much of this range. Compared to traditional methods that can be subjective and risky, our drone-based approach is objective, efficient, fast, cheap, non-invasive, and safe. Nonetheless, further refinements are needed to extend survey times and better include smaller size classes.
Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management)
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Open AccessArticle
Modeling of the Flight Performance of a Plasma-Propelled Drone: Limitations and Prospects
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Sylvain Grosse, Eric Moreau and Nicolas Binder
Drones 2024, 8(3), 114; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030114 - 21 Mar 2024
Abstract
The resurgence in interest in aircraft electro-aerodynamic (EAD) propulsion has been sparked due to recent advancements in EAD thrusters, which generate thrust by employing a plasma generated through electrical discharge. With potentially quieter propulsion that could contribute to the generation of lift or
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The resurgence in interest in aircraft electro-aerodynamic (EAD) propulsion has been sparked due to recent advancements in EAD thrusters, which generate thrust by employing a plasma generated through electrical discharge. With potentially quieter propulsion that could contribute to the generation of lift or the control of attitude, it is important to determine the feasibility of an EAD-propelled airplane. First, the main propulsive characteristics (thrust generation and power consumption) of EAD thrusters were drawn from the literature and compared with existing technologies. Second, an algorithm was developed to couple standard equations of flight with EAD propulsion performance and treat the first-order interactions. It fairly replicated the performance of the only available autonomous EAD-propelled drone. A test case based on an existing commercial UAV of 10 kg equipped with current-generation EAD thrusters anticipated a flight of less than 10 min, lower than 30 m in height, and below 8 m · s −1 in velocity. Achieving over 2 h of flight at 30 m of height at 10 m · s −1 requires the current EAD thrust to be doubled without altering the power consumption. For the same flight performance as the baseline UAV, the prediction asked for a tenfold increase in the thrust at the same power consumption.
Full article
(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)
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Open AccessArticle
UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification
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Robert Van Alphen, Kai C. Rains, Mel Rodgers, Rocco Malservisi and Timothy H. Dixon
Drones 2024, 8(3), 113; https://doi.org/10.3390/drones8030113 - 21 Mar 2024
Abstract
As sea levels rise and temperatures increase, vegetation communities in tropical and sub-tropical coastal areas will be stressed; some will migrate northward and inland. The transition from coastal marshes and scrub–shrubs to woody mangroves is a fundamental change to coastal community structure and
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As sea levels rise and temperatures increase, vegetation communities in tropical and sub-tropical coastal areas will be stressed; some will migrate northward and inland. The transition from coastal marshes and scrub–shrubs to woody mangroves is a fundamental change to coastal community structure and species composition. However, this transition will likely be episodic, complicating monitoring efforts, as mangrove advances are countered by dieback from increasingly impactful storms. Coastal habitat monitoring has traditionally been conducted through satellite and ground-based surveys. Here we investigate the use of UAV-LiDAR (unoccupied aerial vehicle–light detection and ranging) and multispectral photogrammetry to study a Florida coastal wetland. These data have higher resolution than satellite-derived data and are cheaper and faster to collect compared to crewed aircraft or ground surveys. We detected significant canopy change in the period between our survey (2020–2022) and a previous survey (2015), including loss at the scale of individual buttonwood trees (Conocarpus erectus), a woody mangrove associate. The UAV-derived data were collected to investigate the utility of simplified processing and data inputs for habitat classification and were validated with standard metrics and additional ground truth. UAV surveys combined with machine learning can streamline coastal habitat monitoring, facilitating repeat surveys to assess the effects of climate change and other change agents.
Full article
(This article belongs to the Special Issue Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring)
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Open AccessArticle
MFMG-Net: Multispectral Feature Mutual Guidance Network for Visible–Infrared Object Detection
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Fei Zhao, Wenzhong Lou, Hengzhen Feng, Nanxi Ding and Chenglong Li
Drones 2024, 8(3), 112; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030112 - 21 Mar 2024
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Drones equipped with visible and infrared sensors play a vital role in urban road supervision. However, conventional methods using RGB-IR image pairs often struggle to extract effective features. These methods treat these spectra independently, missing the potential benefits of their interaction and complementary
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Drones equipped with visible and infrared sensors play a vital role in urban road supervision. However, conventional methods using RGB-IR image pairs often struggle to extract effective features. These methods treat these spectra independently, missing the potential benefits of their interaction and complementary information. To address these challenges, we designed the Multispectral Feature Mutual Guidance Network (MFMG-Net). To prevent learning bias between spectra, we have developed a Data Augmentation (DA) technique based on the mask strategy. The MFMG module is embedded between two backbone networks, promoting the exchange of feature information between spectra to enhance extraction. We also designed a Dual-Branch Feature Fusion (DBFF) module based on attention mechanisms, enabling deep feature fusion by emphasizing correlations between the two spectra in both the feature channel and space dimensions. Finally, the fused features feed into the neck network and detection head, yielding ultimate inference results. Our experiments, conducted on the Aerial Imagery (VEDAI) dataset and two other public datasets (M3FD and LLVIP), showcase the superior performance of our method and the effectiveness of MFMG in enhancing multispectral feature extraction for drone ground detection.
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Open AccessArticle
Hybrid Encryption for Securing and Tracking Goods Delivery by Multipurpose Unmanned Aerial Vehicles in Rural Areas Using Cipher Block Chaining and Physical Layer Security
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Elias Yaacoub, Khalid Abualsaud and Mohamed Mahmoud
Drones 2024, 8(3), 111; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030111 - 21 Mar 2024
Abstract
This paper investigated the use of unmanned aerial vehicles (UAVs) for the delivery of critical goods to remote areas in the absence of network connectivity. Under such conditions, it is important to track the delivery process and record the transactions in a delay-tolerant
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This paper investigated the use of unmanned aerial vehicles (UAVs) for the delivery of critical goods to remote areas in the absence of network connectivity. Under such conditions, it is important to track the delivery process and record the transactions in a delay-tolerant fashion so that this information can be recovered after the UAV’s return to base. We propose a novel framework that combines the strengths of cipher block chaining, physical layer security, and symmetric and asymmetric encryption techniques in order to safely encrypt the transaction logs of remote delivery operations. The proposed approach is shown to provide high security levels, making the keys undetectable, in addition to being robust to attacks. Thus, it is very useful in drone systems used for logistics and autonomous goods delivery to multiple destinations. This is particularly important in health applications, e.g., for vaccine transmissions, or in relief and rescue operations.
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(This article belongs to the Special Issue Advances of Drones in Logistics)
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Open AccessArticle
Air–Ground Collaborative Multi-Target Detection Task Assignment and Path Planning Optimization
by
Tianxiao Ma, Ping Lu, Fangwei Deng and Keke Geng
Drones 2024, 8(3), 110; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030110 - 21 Mar 2024
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Collaborative exploration in environments involving multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) represents a crucial research direction in multi-agent systems. However, there is still a lack of research in the areas of multi-target detection task assignment and swarm path planning,
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Collaborative exploration in environments involving multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) represents a crucial research direction in multi-agent systems. However, there is still a lack of research in the areas of multi-target detection task assignment and swarm path planning, both of which play a vital role in enhancing the efficiency of environment exploration and reducing energy consumption. In this paper, we propose an air–ground collaborative multi-target detection task model based on Mixed Integer Linear Programming (MILP). In order to make the model more suitable for real situations, kinematic constraints of the UAVs and UGVs, dynamic collision avoidance constraints, task allocation constraints, and obstacle avoidance constraints are added to the model. We also establish an objective function that comprehensively considers time consumption, energy consumption, and trajectory smoothness to improve the authenticity of the model and achieve a more realistic purpose. Meanwhile, a Branch-and-Bound method combined with the Improved Genetic Algorithm (IGA-B&B) is proposed to solve the objective function, and the optimal task assignment and optimal path of air–ground collaborative multi-target detection can be obtained. A simulation environment with multi-agents, multi-obstacles, and multi-task points is established. The simulation results show that the proposed IGA-B&B algorithm can reduce the computation time cost by 30% compared to the traditional Branch-and-Bound (B&B) method. In addition, an experiment is carried out in an outdoor environment, which further validates the effectiveness and feasibility of the proposed method.
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Open AccessArticle
Global Navigation Satellite Systems Signal Vulnerabilities in Unmanned Aerial Vehicle Operations: Impact of Affordable Software-Defined Radio
by
Andrej Novák, Kristína Kováčiková, Branislav Kandera and Alena Novák Sedláčková
Drones 2024, 8(3), 109; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030109 - 20 Mar 2024
Abstract
Spoofing, alongside jamming of the Global Navigation Satellite System signal, remains a significant hazard during general aviation or Unmanned Aerial Vehicle operations. As aircraft utilize various support systems for navigation, such as INS, an insufficient Global Navigation Satellite System signal renders Unmanned Aerial
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Spoofing, alongside jamming of the Global Navigation Satellite System signal, remains a significant hazard during general aviation or Unmanned Aerial Vehicle operations. As aircraft utilize various support systems for navigation, such as INS, an insufficient Global Navigation Satellite System signal renders Unmanned Aerial Vehicles nearly uncontrollable, thereby posing increased danger to operations within airspace and to individuals on the ground. This paper primarily focuses on assessing the impact of the budget friendly Software-Defined Radio, HackRF One 1.0, on the safety of Unmanned Aerial Vehicles operations. Considering the widespread use of Software-Defined Radio devices today, with some being reasonably inexpensive, understanding their influence on Unmanned Aerial Vehicles safety is crucial. The generation of artificial interference capable of posing a potential threat in expanding Unmanned Aerial Vehicles airspace is deemed unacceptable.
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(This article belongs to the Section Drone Communications)
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Impact of Drone Battery Recharging Policy on Overall Carbon Emissions: The Traveling Salesman Problem with Drone
by
Emine Es Yurek
Drones 2024, 8(3), 108; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030108 - 20 Mar 2024
Abstract
This study investigates the traveling salesman problem with drone (TSP-D) from a sustainability perspective. In this problem, a truck and a drone simultaneously serve customers. Due to the limited battery and load capacity, the drone temporarily launches from and returns to the truck
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This study investigates the traveling salesman problem with drone (TSP-D) from a sustainability perspective. In this problem, a truck and a drone simultaneously serve customers. Due to the limited battery and load capacity, the drone temporarily launches from and returns to the truck after each customer visit. Previous studies indicate the potential of deploying drones to reduce delivery time and carbon emissions. However, they assume that the drone battery is swapped after each flight. In this study, we analyze the carbon emissions of the TSP-D under the recharging policy and provide a comparative analysis with the swapping policy. In the recharging policy, the drone is recharged simultaneously on top of the truck while the truck travels. A simulated annealing algorithm is proposed to solve this problem. The computational results demonstrate that the recharging policy can provide faster delivery and lower emissions than the swapping policy if the recharging is fast enough.
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(This article belongs to the Special Issue Advances of Drones in Logistics)
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