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
Design, Modeling, and Control of a Composite Tilt-Rotor Unmanned Aerial Vehicle
Drones 2024, 8(3), 102; https://doi.org/10.3390/drones8030102 - 16 Mar 2024
Abstract
Tilt-rotor unmanned aerial vehicles combine the advantages of multirotor and fixed-wing aircraft, offering features like rapid takeoff and landing, extended endurance, and wide flight conditions. This article provides a summary of the design, modeling, and control of a composite tilt-rotor. During modeling process,
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Tilt-rotor unmanned aerial vehicles combine the advantages of multirotor and fixed-wing aircraft, offering features like rapid takeoff and landing, extended endurance, and wide flight conditions. This article provides a summary of the design, modeling, and control of a composite tilt-rotor. During modeling process, aerodynamic modeling was performed on the tilting and non-tilting parts based on the subcomponent modeling method, and CFD simulation analysis was conducted on the entire unmanned aerial vehicle to obtain its accurate aerodynamic characteristics. In the process of modeling the motor propeller, the reduction of motor thrust and torque due to forward flow and tilt angle velocity is thoroughly examined, which is usually ignored in most tilt UAV propeller models. In the controller design, this paper proposes a fusion ADRC control strategy suitable for vertical takeoff and landing of this type of tiltrotor. The control system framework is built using Simulink, and the control algorithm’s efficiency has been verified through simulation testing. Through the proposed control scheme, it is possible for the composite tiltrotor unmanned aerial vehicle to smoothly transition between multirotor and fixed-wing flight modes.
Full article
(This article belongs to the Special Issue A UAV Platform for Flight Dynamics and Control System)
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Design of Pseudo-Command Restricted Controller for Tailless Unmanned Aerial Vehicles Based on Attainable Moment Set
by
Linxiao Han, Jianbo Hu, Yingyang Wang, Jiping Cong and Peng Zhang
Drones 2024, 8(3), 101; https://doi.org/10.3390/drones8030101 - 15 Mar 2024
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This work investigates the pseudo-command restricted problem for tailless unmanned aerial vehicles with snake-shaped maneuver flight missions. The main challenge of designing such a pseudo-command restricted controller lies in the fact that the necessity of control allocation means it will be difficult to
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This work investigates the pseudo-command restricted problem for tailless unmanned aerial vehicles with snake-shaped maneuver flight missions. The main challenge of designing such a pseudo-command restricted controller lies in the fact that the necessity of control allocation means it will be difficult to provide a precise envelope of pseudo-command to the flight controller; designing a compensation system to deal with insufficient capabilities beyond this envelope is another challenge. The envelope of pseudo-command can be expressed by attainable moment sets, which leave some open problems, such as how to obtain the attainable moment sets online and how to reduce the computational complexity of the algorithm, as well as how to ensure independent control allocation and the convexity of attainable moments sets. In this article, an innovative algorithm is proposed for the calculation of attainable moment sets, which can be implemented by fitting wind tunnel data into a function to solve the problems presented above. Furthermore, the algorithm is independent of control allocation and can be obtained online. Moreover, based on the above attainable moment sets algorithm, a flight performance assurance system is designed, which not only guarantees that the command is constrained within the envelope so that its behavior is more predictable, but also supports adaptive compensation for the pseudo-command restricted controller. Finally, the effectiveness of the AMS algorithm and the advantages of the pseudo-command restricted control system are validated through two sets of independent simulations.
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Open AccessArticle
Design and Demonstration of a Tandem Dual-Rotor Aerial–Aquatic Vehicle
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Sihuan Wu, Maosen Shao, Sifan Wu, Zhilin He, Hui Wang, Jinxiu Zhang and Yue You
Drones 2024, 8(3), 100; https://doi.org/10.3390/drones8030100 - 15 Mar 2024
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Aerial–aquatic vehicles (AAVs) hold great promise for marine applications, offering adaptability to diverse environments by seamlessly transitioning between underwater and aerial operations. Nevertheless, the design of AAVs poses inherent challenges, owing to the distinct characteristics of different fluid media. This article introduces a
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Aerial–aquatic vehicles (AAVs) hold great promise for marine applications, offering adaptability to diverse environments by seamlessly transitioning between underwater and aerial operations. Nevertheless, the design of AAVs poses inherent challenges, owing to the distinct characteristics of different fluid media. This article introduces a novel solution in the form of a tandem dual-rotor aerial–aquatic vehicle, strategically engineered to overcome these challenges. The proposed vehicle boasts a slender and streamlined body, enhancing its underwater mobility while utilizing a tandem rotor for aerial maneuvers. Outdoor scene tests were conducted to assess the tandem dual-rotor AAV’s diverse capabilities, including flying, hovering, and executing repeated cross-media locomotion. Notably, its versatility was further demonstrated through swift surface swimming on water. In addition to aerial evaluations, an underwater experiment was undertaken to evaluate the AAV’s ability to traverse narrow underwater passages. This capability was successfully validated through the creation of a narrow underwater gap. The comprehensive exploration of the tandem dual-rotor AAV’s potential is presented in this article, encompassing its foundational principles, overall design, simulation analysis, and avionics system design. The preliminary research and design outlined herein offer a proof of concept for the tandem dual-rotor AAV, establishing a robust foundation for AAVs seeking optimal performance in both water and air environments. This contribution serves as a valuable reference solution for the advancement of AAV technology.
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Unmanned Aircraft Systems in Road Assessment: A Novel Approach to the Pavement Condition Index and VIZIR Methodologies
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Diana Marcela Ortega Rengifo, Jose Capa Salinas, Javier Alexander Perez Caicedo and Manuel Alejandro Rojas Manzano
Drones 2024, 8(3), 99; https://doi.org/10.3390/drones8030099 - 14 Mar 2024
Abstract
This paper presents an innovative approach to road assessment, focusing on enhancing the Pavement Condition Index (PCI) and Visión Inspection de Zones et Itinéraires Á Risque (VIZIR) methodologies by integrating Unmanned Aircraft System (UAS) technology. The research was conducted in an urban setting,
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This paper presents an innovative approach to road assessment, focusing on enhancing the Pavement Condition Index (PCI) and Visión Inspection de Zones et Itinéraires Á Risque (VIZIR) methodologies by integrating Unmanned Aircraft System (UAS) technology. The research was conducted in an urban setting, utilizing a UAS to capture high-resolution imagery, which was subsequently processed to generate detailed orthomosaics of road surfaces. This study critically analyzed the discrepancies between traditional field measurements and UAS-derived data in pavement condition assessment. The study findings demonstrate that photogrammetry-derived data from UAS offer at least similar or, in some cases, improved information on the collection of a comprehensive state of roadways, particularly in local and collector roads. Furthermore, this study proposed key modifications to the existing methodologies, including dividing the road network into segments for more precise and relevant data collection. These enhancements aim to address the limitations of current practices in capturing the diverse and dynamic conditions of urban infrastructure. Integrating UAS technology improves the measurement of pavement condition assessments and offers a more efficient, cost-effective, and scalable approach to urban infrastructure management. The implications of this study are significant for urban planners and policymakers, providing a robust framework for future infrastructure assessment and maintenance strategies.
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(This article belongs to the Special Issue Intelligent Processing and Application of UAV Remote Sensing Image Data)
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Toward Virtual Testing of Unmanned Aerial Spraying Systems Operating in Vineyards
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Manuel Carreño Ruiz, Nicoletta Bloise, Giorgio Guglieri and Domenic D’Ambrosio
Drones 2024, 8(3), 98; https://doi.org/10.3390/drones8030098 - 13 Mar 2024
Abstract
In recent times, the objective of reducing the environmental impact of the agricultural industry has led to the mechanization of the sector. One of the consequences of this is the everyday increasing use of Unmanned Aerial Systems (UAS) for different tasks in agriculture,
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In recent times, the objective of reducing the environmental impact of the agricultural industry has led to the mechanization of the sector. One of the consequences of this is the everyday increasing use of Unmanned Aerial Systems (UAS) for different tasks in agriculture, such as spraying operations, mapping, or diagnostics, among others. Aerial spraying presents an inherent problem associated with the drift of small droplets caused by their entrainment in vortical structures such as tip vortices produced at the tip of rotors and wings. This problem is aggravated by other dynamic physical phenomena associated with the actual spray operation, such as liquid sloshing in the tank, GPS inaccuracies, wind gusts, and autopilot corrections, among others. This work focuses on analyzing the impact of nozzle position and liquid sloshing on droplet deposition through numerical modeling. To achieve this, the paper presents a novel six degrees of freedom numerical model of a DJI Matrice 600 equipped with a spray system. The spray is modeled using Lagrangian particles and the liquid sloshing is modeled with an interface-capturing method known as Volume of Fluid (VOF) approach. The model is tested in a spraying operation at a constant velocity of 2 m/s in a virtual vineyard. The maneuver is achieved using a PID controller that drives the angular rates of the rotors. This spraying mission simulator was used to obtain insights into optimal nozzle selection and positioning by quantifying the amount of droplet deposition.
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(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section)
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Species-Level Classification of Peatland Vegetation Using Ultra-High-Resolution UAV Imagery
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Gillian Simpson, Caroline J. Nichol, Tom Wade, Carole Helfter, Alistair Hamilton and Simon Gibson-Poole
Drones 2024, 8(3), 97; https://doi.org/10.3390/drones8030097 - 13 Mar 2024
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Peatland restoration projects are being employed worldwide as a form of climate change mitigation due to their potential for long-term carbon sequestration. Monitoring these environments (e.g., cover of keystone species) is therefore essential to evaluate success. However, existing studies have rarely examined peatland
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Peatland restoration projects are being employed worldwide as a form of climate change mitigation due to their potential for long-term carbon sequestration. Monitoring these environments (e.g., cover of keystone species) is therefore essential to evaluate success. However, existing studies have rarely examined peatland vegetation at fine scales due to its strong spatial heterogeneity and seasonal canopy development. The present study collected centimetre-scale multispectral Uncrewed Aerial Vehicle (UAV) imagery with a Parrot Sequoia camera (2.8 cm resolution; Parrot Drones SAS, Paris, France) in a temperate peatland over a complete growing season. Supervised classification algorithms were used to map the vegetation at the single-species level, and the Maximum Likelihood classifier was found to perform best at the site level (69% overall accuracy). The classification accuracy increased with the spatial resolution of the input data, and a large reduction in accuracy was observed when employing imagery of >11 cm resolution. Finally, the most accurate classifications were produced using imagery collected during the peak (July–August) or early growing season (start of May). These findings suggest that despite the strong heterogeneity of peatlands, these environments can be mapped at the species level using UAVs. Such an approach would benefit studies estimating peatland carbon emissions or using the cover of keystone species to evaluate restoration projects.
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Open AccessArticle
Generalized Labeled Multi-Bernoulli Filter-Based Passive Localization and Tracking of Radiation Sources Carried by Unmanned Aerial Vehicles
by
Jun Zhao, Renzhou Gui and Xudong Dong
Drones 2024, 8(3), 96; https://doi.org/10.3390/drones8030096 - 12 Mar 2024
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This paper discusses a key technique for passive localization and tracking of radiation sources, which obtains the motion trajectory of radiation sources carried by unmanned aerial vehicles (UAVs) by continuously or periodically localizing it without the active participation of the radiation sources. However,
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This paper discusses a key technique for passive localization and tracking of radiation sources, which obtains the motion trajectory of radiation sources carried by unmanned aerial vehicles (UAVs) by continuously or periodically localizing it without the active participation of the radiation sources. However, the existing methods have some limitations in complex signal environments and non-stationary wireless propagation that impact the accuracy of localization and tracking. To address these challenges, this paper extends the -generalized labeled multi-Bernoulli (GLMB) filter to the scenario of passive localization and tracking based on the random finite-set (RFS) framework and provides the extended Kalman filter (EKF) and unscented Kalman filter (UKF) implementations of the -GLMB filter, which fully take into account the nonlinear motion of the radiation source. By modeling the “obstacle scenario” and the influence of external factors (e.g., weather, terrain), our proposed GLMB filter can accurately track the target and capture its motion trajectory. Simulation results verify the effectiveness of the GLMB filter in target identification and state tracking.
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Open AccessArticle
Deep Deterministic Policy Gradient (DDPG) Agent-Based Sliding Mode Control for Quadrotor Attitudes
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Wenjun Hu, Yueneng Yang and Zhiyang Liu
Drones 2024, 8(3), 95; https://doi.org/10.3390/drones8030095 - 12 Mar 2024
Abstract
A novel reinforcement deep learning deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the quadrotor under study is
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A novel reinforcement deep learning deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the quadrotor under study is derived, and the attitude control problem is described using formulas. Second, a sliding mode controller, including its sliding mode surface and reaching law, is chosen for the nonlinear dynamic system. The stability of the designed SMC system is validated through the Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adaptively adjust the switching control gain. During the training process, the input signals for the agent are the actual and desired attitude angles, while the output action is the time-varying control gain. Finally, the trained agent mentioned above is utilized in the SMC as a parameter regulator to facilitate the adaptive adjustment of the switching control gain associated with the reaching law. The simulation results validate the robustness and effectiveness of the proposed DDPG-SMC method.
Full article
(This article belongs to the Special Issue Advances in Quadrotor Unmanned Aerial Vehicles)
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Open AccessArticle
Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
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Maaz Ali Awan, Yaser Dalveren, Ali Kara and Mohammad Derawi
Drones 2024, 8(3), 94; https://doi.org/10.3390/drones8030094 - 11 Mar 2024
Abstract
Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide
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Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.
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(This article belongs to the Special Issue Recent Advances of Targeted Observation by Radar/Optical Sensors and UAS)
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Hybrid Mode: Routinization of the Transition Mode as the Third Common Mode for Compound VTOL Drones
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Jiahao Hu, Jingbo Wei, Kun Liu, Xiaobin Yu, Mingzhi Cao and Zijie Qin
Drones 2024, 8(3), 93; https://doi.org/10.3390/drones8030093 - 08 Mar 2024
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Fixed-wing Vertical Takeoff and Landing (VTOL) drones have been widely researched and applied because they combine the advantages of both rotorcraft and fixed-wing drones. However, the research on the transition mode of this type of drone has mainly focused on completing the process
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Fixed-wing Vertical Takeoff and Landing (VTOL) drones have been widely researched and applied because they combine the advantages of both rotorcraft and fixed-wing drones. However, the research on the transition mode of this type of drone has mainly focused on completing the process quickly and stably, and the application potential of this mode has not been given much attention. The objective of this paper is to routinize the transition mode of compound VTOL drones, i.e., this mode works continuously for a longer period of time as a third commonly used mode besides multi-rotor and fixed-wing modes, which is referred to as the hybrid mode. For this purpose, we perform detailed dynamics modeling of the drone in this mode and use saturated PID controllers to control the altitude, velocity, and attitude of the drone. In addition, for more stable altitude control in hybrid mode, we identify the relevant parameters for the lift of the fixed-wings and the thrust of the actuators. Simulation and experimental results show that the designed control method can effectively control the compound VTOL drone in hybrid mode. Moreover, it is proven that flight in hybrid mode can reduce the flight energy consumption to some extent.
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Open AccessArticle
Quantity Monitor Based on Differential Weighing Sensors for Storage Tank of Agricultural UAV
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Junhao Huang, Weizhuo He, Deshuai Yang, Jianqin Lin, Yuanzhen Ou, Rui Jiang and Zhiyan Zhou
Drones 2024, 8(3), 92; https://doi.org/10.3390/drones8030092 - 07 Mar 2024
Abstract
Nowadays, unmanned aerial vehicles (UAVs) play a pivotal role in agricultural production. In scenarios involving the release of particulate materials, the precision of quantity monitors for the storage tank of UAVs directly impacts its operational accuracy. Therefore, this paper introduces a novel noise-mitigation
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Nowadays, unmanned aerial vehicles (UAVs) play a pivotal role in agricultural production. In scenarios involving the release of particulate materials, the precision of quantity monitors for the storage tank of UAVs directly impacts its operational accuracy. Therefore, this paper introduces a novel noise-mitigation design for agricultural UAVs’ quantity monitors, utilizing differential weighing sensors. The design effectively addresses three primary noise sources: sensor-intrinsic noise, vibration noise, and weight-loading uncertainty. Additionally, two comprehensive data processing methods are proposed for noise reduction: the first combines the Butterworth low-pass filter, the Kalman filter, and the moving average filter (BKM), while the second integrates the Least Mean Squares (LMS) adaptive filter, the Kalman filter, and the moving average filter (LKM). Rigorous data processing has been conducted, and the monitor’s performance has been assessed in three UAV typical states: static, hovering, and flighting. Specifically, compared to the BKM, the LKM’s maximum relative error ranges between 1.24% and 2.74%, with an average relative error of 0.31%~0.58% when the UAV was in a hovering state. In flight mode, the LKM’s maximum relative error varies from 1.68% to 10.06%, while the average relative error ranges between 0.74% and 2.54%. Furthermore, LKM can effectively suppress noise interference near 75 Hz and 150 Hz. The results reveal that the LKM technology demonstrated superior adaptability to noise and effectively mitigates its impact in the quantity monitoring for storage tank of agricultural UAVs.
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(This article belongs to the Special Issue Drones in Sustainable Agriculture)
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Open AccessArticle
Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery
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Daihao Yin, Yijun Cai, Yajing Li, Wenshan Yuan and Zhong Zhao
Drones 2024, 8(3), 91; https://doi.org/10.3390/drones8030091 - 07 Mar 2024
Abstract
Assessing the health status of old trees is crucial for the effective protection and health management of old trees. In this study, we utilized an unmanned aerial vehicle (UAV) equipped with multispectral cameras to capture images for the rapid assessment of the health
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Assessing the health status of old trees is crucial for the effective protection and health management of old trees. In this study, we utilized an unmanned aerial vehicle (UAV) equipped with multispectral cameras to capture images for the rapid assessment of the health status of old trees. All trees were classified according to health status into three classes: healthy, declining, and severe declining trees, based on the above-ground parts of the trees. Two traditional machine learning algorithms, Support Vector Machines (SVM) and Random Forest (RF), were employed to assess their health status. Both algorithms incorporated selected variables, as well as additional variables (aspect and canopy area). The results indicated that the inclusion of these additional variables improved the overall accuracy of the models by 8.3% to 13.9%, with kappa values ranging from 0.166 and 0.233. Among the models tested, the A-RF model (RF with aspect and canopy area variables) demonstrated the highest overall accuracy (75%) and kappa (0.571), making it the optimal choice for assessing the health condition of old trees. Overall, this research presents a novel and cost-effective approach to assessing the health status of old trees.
Full article
(This article belongs to the Special Issue Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring)
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Multi-Level Hazard Detection Using a UAV-Mounted Multi-Sensor for Levee Inspection
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Shan Su, Li Yan, Hong Xie, Changjun Chen, Xiong Zhang, Lyuzhou Gao and Rongling Zhang
Drones 2024, 8(3), 90; https://doi.org/10.3390/drones8030090 - 06 Mar 2024
Abstract
This paper introduces a developed multi-sensor integrated system comprising a thermal infrared camera, an RGB camera, and a LiDAR sensor, mounted on a lightweight unmanned aerial vehicle (UAV). This system is applied to the inspection tasks of levee engineering, enabling the real-time, rapid,
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This paper introduces a developed multi-sensor integrated system comprising a thermal infrared camera, an RGB camera, and a LiDAR sensor, mounted on a lightweight unmanned aerial vehicle (UAV). This system is applied to the inspection tasks of levee engineering, enabling the real-time, rapid, all-day, all-round, and non-contact acquisition of multi-source data for levee structures and their surrounding environments. Our aim is to address the inefficiencies, high costs, limited data diversity, and potential safety hazards associated with traditional methods, particularly concerning the structural safety of dam bodies. In the preprocessing stage of multi-source data, techniques such as thermal infrared data enhancement and multi-source data alignment are employed to enhance data quality and consistency. Subsequently, a multi-level approach to detecting and screening suspected risk areas is implemented, facilitating the rapid localization of potential hazard zones and assisting in assessing the urgency of addressing these concerns. The reliability of the developed multi-sensor equipment and the multi-level suspected hazard detection algorithm is validated through on-site levee engineering inspections conducted during flood disasters. The application reliably detects and locates suspected hazards, significantly reducing the time and resource costs associated with levee inspections. Moreover, it mitigates safety risks for personnel engaged in levee inspections. Therefore, this method provides reliable data support and technical services for levee inspection, hazard identification, flood control, and disaster reduction.
Full article
(This article belongs to the Special Issue Intelligent Processing and Application of UAV Remote Sensing Image Data)
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Open AccessArticle
Research of Slamming Load Characteristics during Trans-Media Aircraft Entry into Water
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Xinyu Liu, Liguo Tan, Xinbin Zhang and Liang Li
Drones 2024, 8(3), 89; https://doi.org/10.3390/drones8030089 - 06 Mar 2024
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The trans-media aircraft water entry process generates strong slamming loads that will seriously affect the stability and safety of the aircraft. To address this problem, we design a fixed-wing aircraft configuration and employ numerical simulations with the volume of fluid (VOF) multiphase flow
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The trans-media aircraft water entry process generates strong slamming loads that will seriously affect the stability and safety of the aircraft. To address this problem, we design a fixed-wing aircraft configuration and employ numerical simulations with the volume of fluid (VOF) multiphase flow model, standard k-epsilon turbulence model, and dynamic mesh technique. We explore the characteristics of aircraft subjected to bang loads under different conditions. The results show the following: the pressure load on the aircraft surface increases with higher water entry velocity; larger entry angles lead to more drastic changes in the aircraft’s drag coefficient, demonstrating strong nonlinear characteristics; the greater the angle of attack into the water, the greater the pressure load on the root underneath the wing, with little effect on the pressure load on the head; and the water entry drag coefficient and average pressure load follow an increasing order of conical head, hemispherical head, and flat head. These findings provide theoretical references for studying the load characteristics during trans-media water entry of various flying bodies and optimizing fuselage structural strength.
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Open AccessArticle
Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology
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Sándor Zsebő, László Bede, Gábor Kukorelli, István Mihály Kulmány, Gábor Milics, Dávid Stencinger, Gergely Teschner, Zoltán Varga, Viktória Vona and Attila József Kovács
Drones 2024, 8(3), 88; https://doi.org/10.3390/drones8030088 - 05 Mar 2024
Abstract
This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving
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This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.
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(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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Open AccessEditorial
Special Issue on Intelligent Image Processing and Sensing for Drones
by
Seokwon Yeom
Drones 2024, 8(3), 87; https://doi.org/10.3390/drones8030087 - 04 Mar 2024
Abstract
Recently, the use of drones or unmanned aerial vehicles (UAVs) for various purposes has been increasing [...]
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(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)
Open AccessArticle
Collision Avoidance Capabilities in High-Density Airspace Using the Universal Access Transceiver ADS-B Messages
by
Coulton Karch, Jonathan Barrett, Jaron Ellingson, Cameron K. Peterson and V. Michael Contarino
Drones 2024, 8(3), 86; https://doi.org/10.3390/drones8030086 - 01 Mar 2024
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The safe integration of a large number of unmanned aircraft systems (UASs) into the National Airspace System (NAS) is essential for advanced air mobility. This requires reliable air-to-air transmission systems and robust collision avoidance algorithms. Automatic Dependent Surveillance-Broadcast (ADS-B) is a potential solution
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The safe integration of a large number of unmanned aircraft systems (UASs) into the National Airspace System (NAS) is essential for advanced air mobility. This requires reliable air-to-air transmission systems and robust collision avoidance algorithms. Automatic Dependent Surveillance-Broadcast (ADS-B) is a potential solution for a dependable air-to-air messaging system, but its reliability when stressed with hundreds to thousands of vehicles operating simultaneously is in question. This paper presents an ADS-B model and analyzes the capabilities of the Universal Access Transceiver (UAT), which operates at a frequency of 978 MHz. We use a probabilistic collision avoidance algorithm to examine the impact of varying parameters, including the number of vehicles and the transmission power of the UAT, on the overall safety of the vehicles. Additionally, we investigate the root causes of co-channel interference, proposing enhancements for safe operations in environments with a high density of UAS. Simulation results show message success and collision rates. With our proposed enhancements, UAT ADS-B can provide a decentralized air traffic system that operates safely in high-density situations.
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Open AccessArticle
Reducing Oscillations for Obstacle Avoidance in a Dense Environment Using Deep Reinforcement Learning and Time-Derivative of an Artificial Potential Field
by
Zhilong Xi, Haoran Han, Jian Cheng and Maolong Lv
Drones 2024, 8(3), 85; https://doi.org/10.3390/drones8030085 - 01 Mar 2024
Abstract
Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL)
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Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) for training low-level motion controllers. Unlike traditional potential field methods, our approach modifies the state information received by the motion controllers using the outputs of the APF path planner. Specifically, the assumed target position is pushed away from obstacles, resulting in adjustments to the perceived position errors. Additionally, we address path oscillations by incorporating the target’s velocity information, which is calculated based on the time-derivative of the repulsive force. Experimental results have validated the effectiveness of our proposed framework in avoiding collisions with obstacles and reducing oscillations.
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(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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Open AccessArticle
PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8
by
Noor Ul Ain Tahir, Zhe Long, Zuping Zhang, Muhammad Asim and Mohammed ELAffendi
Drones 2024, 8(3), 84; https://doi.org/10.3390/drones8030084 - 28 Feb 2024
Abstract
In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small
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In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.
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(This article belongs to the Special Issue Advances in Detection and Tracking Applications for Drones and UAM Systems)
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Open AccessArticle
Multiple-Target Matching Algorithm for SAR and Visible Light Image Data Captured by Multiple Unmanned Aerial Vehicles
by
Hang Zhang, Jiangbin Zheng and Chuang Song
Drones 2024, 8(3), 83; https://doi.org/10.3390/drones8030083 - 27 Feb 2024
Abstract
Unmanned aerial vehicle (UAV) technology has witnessed widespread utilization in target surveillance activities. However, cooperative multiple UAVs for the identification of multiple targets poses a significant challenge due to the susceptibility of individual UAVs to false positive (FP) and false negative (FN) target
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Unmanned aerial vehicle (UAV) technology has witnessed widespread utilization in target surveillance activities. However, cooperative multiple UAVs for the identification of multiple targets poses a significant challenge due to the susceptibility of individual UAVs to false positive (FP) and false negative (FN) target detections. Specifically, the primary challenge addressed in this study stems from the weak discriminability of features in Synthetic Aperture Radar (SAR) imaging targets, leading to a high false alarm rate in SAR target detection. Additionally, the uncontrollable false alarm rate during electro-optical proximity detection results in an elevated false alarm rate as well. Consequently, a cumulative error propagation problem arises when SAR and electro-optical observations of the same target from different perspectives occur at different times. This paper delves into the target association problem within the realm of collaborative detection involving multiple unmanned aerial vehicles. We first propose an improved triplet loss function to effectively assess the similarity of targets detected by multiple UAVs, mitigating false positives and negatives. Then, a consistent discrimination algorithm is described for targets in multi-perspective scenarios using distributed computing. We established a multi-UAV multi-target detection database to alleviate training and validation issues for algorithms in this complex scenario. Our proposed method demonstrates a superior correlation performance compared to state-of-the-art networks.
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(This article belongs to the Special Issue Intelligent Recognition and Detection for Unmanned Systems)
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