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Estimating Stratified Biomass in Cotton Fields Using UAV Multispectral Remote Sensing and Machine Learning
Assessment of the Maize Crop Water Stress Index (CWSI) Using Drone-Acquired Data Across Different Phenological Stages

Journal Description

Drones

Drones is an international, peer-reviewed, open access journal published monthly online by MDPI. The journal focuses on the design and applications of drones, including unmanned aerial vehicles (UAVs), Unmanned Aircraft Systems (UASs), and Remotely Piloted Aircraft Systems (RPASs), etc. Likewise, contributions based on unmanned marine/water/underwater drones, unmanned ground vehicles, fully autonomous driving, and space drones are also welcome.

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, Ei Compendex and other databases.
Journal Rank: JCR - Q1 (Remote Sensing) / CiteScore - Q1 (Aerospace Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.2 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2024).
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: 4.4 (2023); 5-Year Impact Factor: 4.8 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2504-446X

Latest Articles

25 pages, 14953 KiB

Open AccessArticle

Optimising Construction Site Auditing: A Novel Methodology Integrating Ground Drones and Building Information Modelling (BIM) Analysis

by Diego Guerrero-Sevilla, Rocío Rodríguez-Gómez, Alberto Morcillo-Sanz and Diego Gonzalez-Aguilera

Drones 2025, 9(4), 277; https://doi.org/10.3390/drones9040277 (registering DOI) - 4 Apr 2025

Monitoring and management of construction sites are critical to ensuring project success, efficiency, and safety. Traditional methods often struggle to provide real-time, accurate, and comprehensive data, leading to delays, cost overruns, and errors. This paper presents a novel methodology utilising a ground drone [...] Read more.

Monitoring and management of construction sites are critical to ensuring project success, efficiency, and safety. Traditional methods often struggle to provide real-time, accurate, and comprehensive data, leading to delays, cost overruns, and errors. This paper presents a novel methodology utilising a ground drone for auditing construction sites to detect changes and deviations from planned Building Information Modelling (BIM). The methodology focuses on developing a novel tool that facilitates Scan-vs-BIM auditing through time. Experimental results are presented, demonstrating the effectiveness and accuracy of the proposed methodology for assessing structural discrepancies. This research contributes to advancing construction auditing practices by integrating state-of-the-art technologies and innovative techniques, ultimately enhancing project monitoring and management processes in the construction industry. Full article

(This article belongs to the Special Issue Digital Twins and Extended Reality: Opportunities and Challenges of Integrated Applications (2nd Edition))

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24 pages, 1301 KiB

Open AccessArticle

Finite-Time Formation Control for Clustered UAVs with Obstacle Avoidance Inspired by Pigeon Hierarchical Behavior

by Zhaoyu Zhang, Yang Yuan and Haibin Duan

Drones 2025, 9(4), 276; https://doi.org/10.3390/drones9040276 (registering DOI) - 4 Apr 2025

To address the formation control issue of multiple unmanned aerial vehicles (UAVs), a finite-time control scheme based on terminal sliding mode (TSM) is investigated in this paper. A quadcopter UAV with the vertical takeoff property is considered, with cascaded kinematics composed of rotational [...] Read more.

To address the formation control issue of multiple unmanned aerial vehicles (UAVs), a finite-time control scheme based on terminal sliding mode (TSM) is investigated in this paper. A quadcopter UAV with the vertical takeoff property is considered, with cascaded kinematics composed of rotational and translational loops. To strengthen the application in the low-cost UAV system, the applied torque is synthesized with an auxiliary rotational system, which can avoid utilizing direct attitude measurement. Furthermore, a terminal sliding mode surface is established and employed in the finite-time formation control protocol (FTFCP) as the driven thrust of multiple UAVs over an undirected topology in the translational system. To maintain the safe flight of the UAV clusters in an environment to avoid collision with obstacles or with other UAV neighbors, a pigeon-hierarchy-inspired obstacle avoidance protocol (PHOAP) is proposed. By imitating the interactive hierarchy that exists among the homing pigeon flocks, the collision avoidance scheme is separately enhanced to generate the repulsive potential field for the leader maneuver target and the follower UAV cluster. Subsequently, the collision avoidance laws based on pigeon homing behavior are combined with the finite-time sliding mode formation protocol, and the applied torque is attached as a cascaded structure in the attitude loop to synthesize an obstacle avoidance cooperative control framework. Finally, simulation scenarios of multiple UAVs to reach a desired formation among obstacles is investigated, and the effectiveness of the proposed approach is validated. Full article

(This article belongs to the Special Issue Biological UAV Swarm Control)

30 pages, 71080 KiB

Open AccessArticle

GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control

by Jingyi Huang, Yujie Cui, Guipeng Xi, Shuangxia Bai, Bo Li, Geng Wang and Evgeny Neretin

Drones 2025, 9(4), 275; https://doi.org/10.3390/drones9040275 - 3 Apr 2025

Research on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stage learning efficiency. To [...] Read more.

Research on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stage learning efficiency. To address these issues, this paper integrates DRL with the Transformer architecture to propose the GTrXL-SAC (gated Transformer-XL soft actor critic) algorithm. The algorithm performs positional embedding on multimodal data combining visual and sensor information. Leveraging the self-attention mechanism of GTrXL, it effectively focuses on different segments of multimodal data for encoding while capturing sequential relationships, significantly improving obstacle recognition accuracy and enhancing both learning efficiency and sample efficiency. Additionally, the algorithm capitalizes on GTrXL’s memory characteristics to generate current drone control decisions through the combined analysis of historical experiences and present states, effectively mitigating long-term dependency issues. Experimental results in the AirSim drone simulation environment demonstrate that compared to PPO and SAC algorithms, GTrXL-SAC achieves more precise policy exploration and optimization, enabling superior control of drone velocity and attitude for stabilized flight while accelerating convergence speed by nearly 20%. Full article

23 pages, 3430 KiB

Open AccessArticle

Joint Optimization of Task Completion Time and Energy Consumption in UAV-Enabled Mobile Edge Computing

by Hanwen Zhang, Tao Chen, Bangbang Ren, Ruozhe Li and Hao Yuan

Drones 2025, 9(4), 274; https://doi.org/10.3390/drones9040274 - 3 Apr 2025

Unmanned Aerial Vehicles (UAVs) hold great promise for Mobile Edge Computing (MEC) owing to their flexible mobility, rapid deployment, and low-cost characteristics. However, UAV-enabled MEC still faces challenges in terms of the real-time arrival of computational tasks, energy reservation, and the actual response [...] Read more.

Unmanned Aerial Vehicles (UAVs) hold great promise for Mobile Edge Computing (MEC) owing to their flexible mobility, rapid deployment, and low-cost characteristics. However, UAV-enabled MEC still faces challenges in terms of the real-time arrival of computational tasks, energy reservation, and the actual response efficiency of the system. In this study, we focus on a UAV-enabled MEC scenario, where multiple UAVs function as airborne edge servers, offering computation services to multiple ground-based user devices (UDs). We aim to minimize the cost of the MEC system by optimizing the computation offloading policy. Specifically, we take task latency into account to ensure the timeliness of real-time tasks. The Lyapunov optimization method is employed to maintain a uniform and stable queue for energy consumption. Additionally, we draw on the concept of maximum completion time in shop-floor scheduling to optimize the actual response latency. To this end, we propose a joint optimization algorithm. First, the joint optimization problem is transformed into a per-time-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a reinforcement learning method, LyraRD, is proposed to solve the PROP. Experimental results verify that the proposed approach outperforms the benchmarks in terms of system performance. Full article

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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19 pages, 7302 KiB

Open AccessArticle

Safe and Optimal Motion Planning for Autonomous Underwater Vehicles: A Robust Model Predictive Control Framework Integrating Fast Marching Time Objectives and Adaptive Control Barrier Functions

by Zhonghe Tian and Mingzhi Chen

Drones 2025, 9(4), 273; https://doi.org/10.3390/drones9040273 - 3 Apr 2025

Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods [...] Read more.

Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods oversimplify obstacle geometries, and both fail to ensure safety and satisfy turning radius constraints for under-actuated AUVs in intricate environments. This paper proposes a robust Model Predictive Control (MPC) framework integrating an enhanced fast marching control barrier function, specifically designed for AUVs equipped with fully directional sonar systems. The framework introduces a novel improvement for moving obstacles by extending the control barrier function field propagation along the obstacle’s movement direction. This enhancement generates precise motion plans that ensure safety, satisfy kinematic constraints, and effectively handle static and dynamic obstacles. Simulation results demonstrate superior obstacle avoidance and motion planning performance in complex scenarios, with key outcomes including a minimum safety margin of 1.86 m in cluttered environments (vs. 0 m for A* and FMM) and 1.76 m in dynamic obstacle scenarios (vs. 0.13 m for MPC-APFCBF), highlighting the framework’s ability to enhance navigation safety and efficiency for real-world AUV deployments in unpredictable marine environments. Full article

(This article belongs to the Special Issue Advances in Intelligent Coordination Control for Autonomous UUVs)

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15 pages, 2448 KiB

Open AccessArticle

Autonomous Task Planning of Intelligent Unmanned Aerial Vehicle Swarm Based on Deep Deterministic Policy Gradient

by Qiang Jiang, Yongzhao Yan, Yinxing Dai, Zequan Yang, Huazhen Cao, Bo Wang and Xiaoping Ma

Drones 2025, 9(4), 272; https://doi.org/10.3390/drones9040272 - 3 Apr 2025

Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses [...] Read more.

Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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24 pages, 6915 KiB

Open AccessArticle

Control of Unmanned Aerial Vehicle Swarms to Cruise and Cluster While Considering Rhythmless Coupled Oscillation

by Yonggang Li, Peide Fu, Ang Gao and Longjiang Li

Drones 2025, 9(4), 271; https://doi.org/10.3390/drones9040271 - 2 Apr 2025

When multiple unmanned aerial vehicles (UAVs) form a cluster, their flight process is divided into two phases. The first phase is the cruising stage, during which UAVs move from random positions toward the target, gradually forming a spherical topology. In the initial cruising [...] Read more.

When multiple unmanned aerial vehicles (UAVs) form a cluster, their flight process is divided into two phases. The first phase is the cruising stage, during which UAVs move from random positions toward the target, gradually forming a spherical topology. In the initial cruising phase, to address the oscillation phenomenon in traditional sliding mode control, we propose a new reaching law to overcome the typical residual oscillations present in conventional reaching laws, called the Control Law for Residual Chattering Oscillation Elimination (CL-RCO). Based on this proposed new law, we have designed an artificial potential field-based sliding mode formation controller for UAVs to manage the formation control of UAVs. The second stage is the clustering phase, which focuses on overcoming oscillations to establish a stable topology. In this phase, we design a controller that combines artificial potential fields with variable repulsion coefficients and backstepping control. This method addresses the persistent residual oscillations in formations maintained solely by artificial potential fields during the clustering phase. Lyapunov stability analysis is employed to confirm the feasibility of the designed controller. Eventually, numerical simulations and comparative analyses are performed, successfully demonstrating the proposed method’s effectiveness. Full article

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19 pages, 5676 KiB

Open AccessArticle

Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery

by Hongyan Yang, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Jie Li, Qihong Da, Xuchun Li and Kejing Cheng

Drones 2025, 9(4), 270; https://doi.org/10.3390/drones9040270 - 1 Apr 2025

Accurately monitoring total nitrogen (TN) content in field soils is crucial for precise fertilization management. TN content is one of the core indicators in soil fertility evaluation systems. Rapid and accurate determination of TN in the tillage layer is essential for agricultural production. [...] Read more.

Accurately monitoring total nitrogen (TN) content in field soils is crucial for precise fertilization management. TN content is one of the core indicators in soil fertility evaluation systems. Rapid and accurate determination of TN in the tillage layer is essential for agricultural production. Although UAV-based multispectral remote sensing technology has shown potential in agricultural monitoring, research on its quantitative assessment of soil TN content remains limited. This study utilized UAV (unmanned aerial vehicle) multispectral imagery and field-measured TN data from four key growth stages of silage corn in 2022 at Huari Ranch, Minle County, Hexi region. The support vector machine–recursive feature elimination (SVM-RFE) algorithm was applied to select vegetation indices as model inputs. A total of 18 models based on machine learning algorithms, including BP neural networks (BPNNs), random forest (RF), and partial least squares regression (PLSR), were constructed to compare the most suitable inversion model for TN in the rhizosphere soil (0–30 cm) of silage corn at different growth stages. The optimal period for TN inversion was determined. The SVM-RFE algorithm outperformed the models built without feature selection in terms of accuracy. Among the nitrogen inversion models based on different machine learning algorithms, the PLSR model showed the best performance, followed by the RF model, while the BPNN model performed the worst. The PLSR model established for the mature growth stage at soil depths demonstrated the highest inversion accuracy, with R and RMSE values of 0.663 and 0.281, respectively. The next best period was the tasseling stage, while the worst inversion accuracy was observed during the seedling stage, indicating that the mature stage is the optimal period for TN inversion in the study area. Full article

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19 pages, 10510 KiB

Open AccessArticle

Performance Analysis and Flow Mechanism of Close-Range Overlapping Rotor in Hover

by Ziyi Xu, Yi Ding, Zhe Hui, Chu Tang, Zhaobing Jiang and Liang Wang

Drones 2025, 9(4), 269; https://doi.org/10.3390/drones9040269 - 1 Apr 2025

High payload capacity multi-rotor aerial vehicles are typically configured with multiple propellers to achieve the required aerodynamic lift. However, this design approach often results in an increased overall dimensional envelope, which introduces significant operational limitations in confined spatial environments such as urban airspace. [...] Read more.

High payload capacity multi-rotor aerial vehicles are typically configured with multiple propellers to achieve the required aerodynamic lift. However, this design approach often results in an increased overall dimensional envelope, which introduces significant operational limitations in confined spatial environments such as urban airspace. By utilizing a limited overlap rotor configuration, the spatial utilization rate of an aircraft can be greatly improved, ensuring a sufficient thrust of rotor while simultaneously reducing the size of the aircraft. However, the slipstreams of two rotors overlap, which may create a significant aerodynamic interface. This paper utilizes numerical simulation based on the unsteady RANS (Reynolds-averaged Navier–Stokes) method to analyze the influence of parameters such as distance, blade distance, and rotation direction on the interference flow field of overlapping rotors. Research indicates that aerodynamic interference only affects the overlapping area between two rotors at the inner blade, leading to the offset of loading distribution on the blade, which can be explained by the slipstream effect, suction effect, and induced effects generated by two rotors. As the axis distance between two rotors decreases, the strengthening of the slipstream and suction effects leads to a rapid decrease in the aerodynamic efficiency of the two rotors. When the blade between the two rotors increases, the weakening of the suction effect and induced effects causes the load on the lower rotor to translate to the upper rotor. Moreover, the variation in the spatial distribution of the blade tip–vortex leads to blade–vortex interaction, which causes a change in the spanwise distribution of the load on the lower blade. Full article

(This article belongs to the Section Drone Design and Development)

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26 pages, 12220 KiB

Open AccessArticle

Preassigned Fixed-Time Synergistic Constrained Control for Fixed-Wing Multi-UAVs with Actuator Faults

by Jianhua Lu, Zehao Yuan and Ning Wang

Drones 2025, 9(4), 268; https://doi.org/10.3390/drones9040268 - 1 Apr 2025

This study focuses on the distributed fixed-time fault-tolerant control problem for a network of six-degree-of-freedom (DOF) fixed-wing unmanned aerial vehicles (UAVs), which are subject to full-state constraints and actuator faults. The novelty of the proposed design lies in the incorporation of an enhanced [...] Read more.

This study focuses on the distributed fixed-time fault-tolerant control problem for a network of six-degree-of-freedom (DOF) fixed-wing unmanned aerial vehicles (UAVs), which are subject to full-state constraints and actuator faults. The novelty of the proposed design lies in the incorporation of an enhanced asymmetric time-varying tan-type barrier Lyapunov function (BLF), which is applicable in both constrained and unconstrained scenarios. This function ensures that the UAV states remain within compact sets at all times while achieving fixed-time convergence. Additionally, a fixed-time performance function (FTPF) is developed to eliminate the dependency on exponential functions commonly used in traditional fixed-time control methods. The adverse effects of actuator faults, including lock-in-place and loss of effectiveness, are mitigated through a bounded uniform tracking control design. A rigorous Lyapunov function analysis demonstrates that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with both velocity and attitude tracking errors converging to residual sets near the origin. Experimental validation tests are conducted to confirm the effectiveness of the theoretical findings. Full article

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24 pages, 6970 KiB

Open AccessArticle

Two-Stage Hierarchical 4D Low-Risk Trajectory Planning for Urban Air Logistics

by Yuan Zheng, Yichao Li, Jie Cheng, Chenglong Li and Shichen Hu

Drones 2025, 9(4), 267; https://doi.org/10.3390/drones9040267 - 31 Mar 2025

The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not adequately consider the ground risks [...] Read more.

The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not adequately consider the ground risks and secondary conflicts arising from high-density operations in urban VLL airspace. To address these challenges, this paper proposes a two-stage hierarchical 4D trajectory planning method to minimize multiple risks. Specifically, the method consists of a risk-aware global planning module (RAGPM) for preflight trajectory planning and a non-secondary conflict local planning module (NCLPM) for in-flight conflict avoidance. Consequently, low-risk trajectory without secondary conflict can be found in complex environments with high-density operations, as illustrated by extensive experiments. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)

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17 pages, 13837 KiB

Open AccessArticle

Mapping, Modeling and Designing a Marble Quarry Using Integrated Electric Resistivity Tomography and Unmanned Aerial Vehicles: A Study of Adaptive Decision-Making

by Zahid Hussain, Hanan ud Din Haider, Jiajie Li, Zhengxing Yu, Jianxin Fu, Siqi Zhang, Sitao Zhu, Wen Ni and Michael Hitch

Drones 2025, 9(4), 266; https://doi.org/10.3390/drones9040266 - 31 Mar 2025

The characterization of dimensional stone deposits is essential for quarry assessment and design. However, uncertainties in mapping and designing pose significant challenges. To address this issue, an innovative approach is initiated to develop a virtual reality model by integrating unmanned aerial vehicle (UAV) [...] Read more.

The characterization of dimensional stone deposits is essential for quarry assessment and design. However, uncertainties in mapping and designing pose significant challenges. To address this issue, an innovative approach is initiated to develop a virtual reality model by integrating unmanned aerial vehicle (UAV) photogrammetry for surface modeling and Electric Resistivity Tomography (ERT) for subsurface deposit imaging. This strategy offers a cost-effective, time-efficient, and safer alternative to traditional surveying methods for challenging mountainous terrain. UAV methodology involved data collection using a DJI Mavic 2 Pro (20 MP camera) with 4 K resolution images captured at 221 m altitude and 80 min flight duration. Images were taken with 75% frontal and 70% side overlaps. The Structure from Motion (SfM) processing chain generated high-resolution outputs, including point clouds, Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and orthophotos. To ensure accuracy, five ground control points (GCPs) were established by a Real-Time Kinematic Global Navigation Satellite System (RTK GNSS). An ERT method known as vertical electric sounding (VES) revealed subsurface anomalies like solid rock mass, fractured zones and areas of iron leaching within marble deposits. Three Schlumberger (VES-1, 2, 3) and two parallel Wenner (VES-4, 5) arrays to a depth of 60 m were employed. The resistivity signature acquired by PASI RM1 was analyzed using 1D inversion technique software (ZondP1D). The integrated outputs of photogrammetry and subsurface imaging were used to design an optimized quarry with bench heights of 30 feet and widths of 50 feet, utilizing open-source 3D software (Blender, BIM, and InfraWorks). This integrated approach provides a comprehensive understanding of deposit surface and subsurface characteristics, facilitating optimized and sustainable quarry design and extraction. This research demonstrates the value of an innovative approach in synergistic integration of UAV photogrammetry and ERT, which are often used separately, for enhanced characterization, decision-making and promoting sustainable practices in dimensional stone deposits. Full article

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27 pages, 2415 KiB

Open AccessReview

State-of-the-Art Review on the Application of Unmanned Aerial Vehicles (UAVs) in Power Line Inspections: Current Innovations, Trends, and Future Prospects

by Bongumsa Mendu and Nhlanhla Mbuli

Drones 2025, 9(4), 265; https://doi.org/10.3390/drones9040265 - 31 Mar 2025

Unmanned aerial vehicles (UAVs) make power line inspections more safe, efficient, and cost-effective, replacing risky manual checks and expensive helicopter surveys while overcoming challenges like stability and regulations. The aim of this study is to conduct a systematic review of the application of [...] Read more.

Unmanned aerial vehicles (UAVs) make power line inspections more safe, efficient, and cost-effective, replacing risky manual checks and expensive helicopter surveys while overcoming challenges like stability and regulations. The aim of this study is to conduct a systematic review of the application of UAVs for power line inspections. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is implemented to ensure a structured and comprehensive review process. The Scopus database is used to identify relevant publications, and after screening and applying eligibility criteria, 75 documents were selected for further analysis. The study results show a shift toward predictive maintenance, multi-UAV operations, and real-time data analysis. However, challenges remain, including UAV–grid connectivity, resilience to extreme weather, and large-scale automation. This work provides key insights into technological and algorithmic advancements and research trends on UAV-based power line inspections while pointing out gaps in the existing literature. Finally, future research directions to advance UAV-based power line inspections are suggested. Full article

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21 pages, 4799 KiB

Open AccessArticle

Data-Efficient Reinforcement Learning Framework for Autonomous Flight Based on Real-World Flight Data

by Uicheon Lee, Seonah Lee and Kyonghoon Kim

Drones 2025, 9(4), 264; https://doi.org/10.3390/drones9040264 - 31 Mar 2025

Recently, autonomous flight has emerged as a key technology in the aerospace and defense sectors; however, traditional code-based autonomous flight systems face limitations in complex environments. Although reinforcement learning offers an alternative, its practical application in real-world settings is hindered by the substantial [...] Read more.

Recently, autonomous flight has emerged as a key technology in the aerospace and defense sectors; however, traditional code-based autonomous flight systems face limitations in complex environments. Although reinforcement learning offers an alternative, its practical application in real-world settings is hindered by the substantial data requirements. In this study, we develop a framework that integrates a Generative Adversarial Network (GAN) and Hindsight Experience Replay (HER) into model-based reinforcement learning to enhance data efficiency and accuracy. We compared the proposed framework against existing algorithms in actual quadcopter control. In the comparative experiment, we demonstrated an improvement of up to 70.59% in learning speed, clearly highlighting the impact of the environmental model. To the best of our knowledge, this study is the first where a GAN and HER are combined with model-based reinforcement learning, and it is expected to contribute significantly to the practical application of reinforcement learning in autonomous flight. Full article

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32 pages, 1474 KiB

Open AccessArticle

A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions

by Mamunur Rahman, Nurul I. Sarkar and Raymond Lutui

Drones 2025, 9(4), 263; https://doi.org/10.3390/drones9040263 - 31 Mar 2025

Multi-UAV path planning algorithms are crucial for the successful design and operation of unmanned aerial vehicle (UAV) networks. While many network researchers have proposed UAV path planning algorithms to improve system performance, an in-depth review of multi-UAV path planning has not been fully [...] Read more.

Multi-UAV path planning algorithms are crucial for the successful design and operation of unmanned aerial vehicle (UAV) networks. While many network researchers have proposed UAV path planning algorithms to improve system performance, an in-depth review of multi-UAV path planning has not been fully explored yet. The purpose of this study is to survey, classify, and compare the existing multi-UAV path planning algorithms proposed in the literature over the last eight years in various scenarios. After detailing classification, we compare various multi-UAV path planning algorithms based on time consumption, computational cost, complexity, convergence speed, and adaptability. We also examine multi-UAV path planning approaches, including metaheuristic, classical, heuristic, machine learning, and hybrid methods. Finally, we identify several open research problems for further investigation. More research is required to design smart path planning algorithms that can re-plan pathways on the fly in real complex scenarios. Therefore, this study aims to provide insight into the multi-UAV path planning algorithms for network researchers and engineers to contribute further to the design of next-generation UAV systems. Full article

(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones)

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24 pages, 7999 KiB

Open AccessArticle

Adaptive Impact-Time-Control Cooperative Guidance Law for UAVs Under Time-Varying Velocity Based on Reinforcement Learning

by Zhenyu Liu, Gang Lei, Yong Xian, Leliang Ren, Shaopeng Li and Daqiao Zhang

Drones 2025, 9(4), 262; https://doi.org/10.3390/drones9040262 - 29 Mar 2025

In this study, an adaptive impact-time-control cooperative guidance law based on deep reinforcement learning considering field-of-view (FOV) constraints is proposed for high-speed UAVs with time-varying velocity. Firstly, a reinforcement learning framework for the high-speed UAVs’ guidance problem is established. The optimization objective is [...] Read more.

In this study, an adaptive impact-time-control cooperative guidance law based on deep reinforcement learning considering field-of-view (FOV) constraints is proposed for high-speed UAVs with time-varying velocity. Firstly, a reinforcement learning framework for the high-speed UAVs’ guidance problem is established. The optimization objective is to maximize the impact velocity; and the constraints for impact time, dive attacking, and FOV are considered simultaneously. The time-to-go estimation method is improved so that it can be applied to high-speed UAVs with time-varying velocity. Then, in order to improve the applicability and robustness of the agent, environmental uncertainties, including aerodynamic parameter errors, observation noise, and target random maneuvers, are incorporated into the training process. Furthermore, inspired by the

{RL}^{2}

algorithm, the recurrent layer is introduced into both the policy and value network. In this way, the agent can automatically adapt to different mission scenarios by updating the hidden states of the recurrent layer. In addition, a compound reward function is designed to train the agent to satisfy the requirements of impact-time control and dive attack simultaneously. Finally, the effectiveness and robustness of the proposed guidance law are validated through numerical simulations conducted across a wide range of scenarios. Full article

(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)

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26 pages, 3043 KiB

Open AccessArticle

High-Precision Landing on a Moving Platform Based on Drone Vision Using YOLO Algorithm

by Hao Wu, Wei Wang, Tong Wang and Satoshi Suzuki

Drones 2025, 9(4), 261; https://doi.org/10.3390/drones9040261 - 29 Mar 2025

High-precision landing is a key technical problem that Unmanned Aerial Vehicles (UAVs) will encounter in all application fields, especially for the landing of moving targets. This paper focuses on developing a landing system designed to achieve real-time precise navigation by integrating the Global [...] Read more.

High-precision landing is a key technical problem that Unmanned Aerial Vehicles (UAVs) will encounter in all application fields, especially for the landing of moving targets. This paper focuses on developing a landing system designed to achieve real-time precise navigation by integrating the Global Navigation Satellite System (GNSS) with the quadcopter’s vision data. To overcome the challenge of the flight altitude being too high to detect the landing target, this paper first detects large-volume targets, followed by the precise identification of smaller targets, achieving enhanced recognition accuracy and speed through an improved YOLOv8 OBB algorithm. To maintain the UAV’s safety and stability throughout the landing process, this paper applies a position control approach using a reference model-based sliding mode controller (RMSMC). The quadcopter’s position is then controlled by the RMSMC throughout the entire landing procedure. The reference value of each state is determined by the reference model, which improves the stability and safety of the entire position control system. During the final experiment, the results demonstrate that the enhanced YOLOv8 OBB identification model increases the mAP0.5:0.95 index for landing target point detection by 2.22 percentage points compared to the original YOLOv8 OBB model, running at 53 FPS on Nvidia AGX. Through multiple actual flights, the proposed landing system consistently achieves an average position error of just 0.07 m. Full article

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24 pages, 2187 KiB

Open AccessArticle

PUF-Based Secure and Efficient Anonymous Authentication Protocol for UAV Towards Cross-Domain Environments

by Qi Xie and Haohua Wang

Drones 2025, 9(4), 260; https://doi.org/10.3390/drones9040260 - 28 Mar 2025

Cross-domain authentication of drones has played an important role in emergency rescue, collaborative missions, and so on. However, the existing cross-domain authentication protocols for drones may cause privacy leakages and stolen-verifier attacks due to the storage of drone information by ground stations, and [...] Read more.

Cross-domain authentication of drones has played an important role in emergency rescue, collaborative missions, and so on. However, the existing cross-domain authentication protocols for drones may cause privacy leakages and stolen-verifier attacks due to the storage of drone information by ground stations, and drones and ground stations are susceptible to capture attacks, which may suffer from impersonation attacks. To address these problems, we propose a lightweight cross-domain authentication protocol based on physical unclonable function (PUF). In the proposed protocol, the control center is not involved in the authentication process, preventing bottleneck problems when multiple drones authenticate simultaneously. Ground stations do not store drone information, effectively safeguarding against privacy leakage and stolen-verifier attacks. PUF is utilized to protect drones from capture attacks. We conduct both informal security analysis and formal security proof to demonstrate the protocol’s security. In terms of performance, compared with relevant schemes, our protocol shows remarkable efficiency improvements. Computationally, it is 5–92% more efficient. Regarding communication overhead, it is 9–68% lower than relevant schemes. For storage, it is 22–48% lower than relevant schemes. We simulated the proposed protocol using a Raspberry Pi 4B, which emulates the computational capabilities of actual UAV and ground stations. During the simulation, a large number of authentication requests were generated. We monitored key performance indicators such as authentication success rate, response time, and resource utilization. To test its security, we simulated common attacks like replay, forgery, and impersonation. The protocol’s timestamps effectively identified and rejected replayed messages. Meanwhile, the PUF mechanism and unique signature scheme foiled our attempts to forge authentication messages. These simulation results, combined with theoretical security proofs, confirm the protocol’s practical viability and security in real-world-like scenarios. Full article

(This article belongs to the Section Drone Communications)

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27 pages, 6176 KiB

Open AccessArticle

Distributed Event-Triggered-Based Adaptive Formation Tracking Control for Multi-UAV Systems Under Fixed and Switched Topologies

by Chengqing Liang, Lei Liu, Lei Li and Dongmei Yan

Drones 2025, 9(4), 259; https://doi.org/10.3390/drones9040259 - 28 Mar 2025

This paper investigates the time-varying formation-tracking (TVFT) problem for multi-UAV systems (MUSs), where the followers need to achieve a predefined time-varying formation configuration while tracking the leader’s state. In order to reduce the consumption of communication resources, an adaptive event-triggered mechanism (AETM) is [...] Read more.

This paper investigates the time-varying formation-tracking (TVFT) problem for multi-UAV systems (MUSs), where the followers need to achieve a predefined time-varying formation configuration while tracking the leader’s state. In order to reduce the consumption of communication resources, an adaptive event-triggered mechanism (AETM) is designed. By combining the advantages of the adaptive technique and the event-triggered mechanism (ETM), UAVs can realize intermittent communication without relying on global information. Secondly, to improve the flexibility of formation-tracking trajectories, the TVFT consensus protocol with non-zero leader inputs is constructed. Meanwhile, the scope of the formation-tracking feasibility condition is extended. Then, the stability of the system is verified by Lyapunov stability theory, and sufficient conditions for MUSs to realize the desired TVFT configuration are obtained. In addition, the designed consensus protocol can be applied to both fixed topologies and switching topologies. Finally, the validity of the designed algorithm is confirmed by numerical examples and software-in-the-loop (SIL) simulation experiments. Full article

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21 pages, 6672 KiB

Open AccessArticle

Influence of Ground Control Point Placement and Surrounding Environment on Unmanned Aerial Vehicle-Based Structure-from-Motion Forest Resource Estimation

by Shohei Kameyama

Drones 2025, 9(4), 258; https://doi.org/10.3390/drones9040258 - 28 Mar 2025

Ground control points (GCPs) are used in forest surveys employing unmanned aerial vehicle (UAV)-based structure from motion (SfM). In that context, the influence of the surrounding environment on GCP placement requires further analysis. This study investigated the effects of GCP placement and the [...] Read more.

Ground control points (GCPs) are used in forest surveys employing unmanned aerial vehicle (UAV)-based structure from motion (SfM). In that context, the influence of the surrounding environment on GCP placement requires further analysis. This study investigated the effects of GCP placement and the surrounding environment on the estimation of forest information by UAV-SfM. Forest resource estimation was performed using UAV (Inspire2) aerial images and SfM analysis (via Pix4Dmapper) under varying environmental conditions around GCPs within the same forest stand. The results indicated that GCP placement had no significant effect on SfM processing, tree top extraction (the number of extracted target trees was 151 or 150), or tree crown area estimation (RMSEs ranged from approximately 5 to 6.5 m²). However, when GCPs were placed in open areas, the tree height estimation accuracy improved, without significant differences between estimated and measured values (patterns A, B, D and E, had RMSEs of 1.60 to 3.09 m; patterns C and D had RMSEs of 5.69 to 7.92 m). These findings suggest that in UAV-SfM-based forest resource surveys, particularly for tree height estimation, both the number and placement of GCPs, as well as the surrounding environment, are crucial in enhancing estimation accuracy. Full article

(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)

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