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Volume 9
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Drones, Volume 9, Issue 8 (August 2025) – 80 articles

Cover Story (view full-size image): This study presents a method for encoding UAV-based pseudolite motion into standard GNSS navigation messages, enabling full compatibility with existing receivers without hardware or software modifications. By mapping UAV trajectories to GNSS ephemeris parameters through coordinate transformation, error analysis, and parameter optimization, the approach provides precise positioning in GNSS-denied or degraded environments. Simulations and UAV flight tests confirm metre-level accuracy, offering a lightweight, interoperable solution for rapid deployment in emergency and disaster relief scenarios. View this paper
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26 pages, 6324 KB  
Article
A Multi-UAV Distributed Collaborative Search Algorithm Based on Maximum Entropy Mechanism
by Siyuan Cui, Hao Li, Xiangyu Fan, Lei Ni and Jiahang Hou
Drones 2025, 9(8), 592; https://doi.org/10.3390/drones9080592 - 21 Aug 2025
Viewed by 863
Abstract
This paper addresses the core issues of slow coverage rate growth and high repeated detection rates in multi-UAV cooperative search operations within unknown areas. A distributed cooperative search algorithm based on the maximum entropy mechanism is proposed to resolve these challenges. It innovatively [...] Read more.
This paper addresses the core issues of slow coverage rate growth and high repeated detection rates in multi-UAV cooperative search operations within unknown areas. A distributed cooperative search algorithm based on the maximum entropy mechanism is proposed to resolve these challenges. It innovatively integrates the entropy gradient decision framework with DMPC-OODA (Distributed Model Predictive Control-Observe, Orient, Decide, Act) rolling optimization: environmental uncertainty is quantified through an exponential decay entropy model to drive UAVs to migrate toward high-entropy regions; element-wise product operations are employed to efficiently update environmental maps; and a dynamic weight function is designed to adaptively adjust the weights of coverage gain and entropy gain, thereby balancing “rapid coverage” and “accurate exploration”. Through multiple independent repeated experiments, the algorithm demonstrates significant improvements in coverage efficiency—by 6.95%, 12.22%, and 59.49%, respectively—compared with the Search Intent Interaction (SII) mode, non-entropy mode, and random mode, which effectively enhances resource utilization. Full article
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19 pages, 9202 KB  
Article
Fuzzy Adaptive Fixed-Time Bipartite Consensus Self-Triggered Control for Multi-QUAVs with Deferred Full-State Constraints
by Chenglin Wu, Shuai Song, Xiaona Song and Heng Shi
Drones 2025, 9(8), 591; https://doi.org/10.3390/drones9080591 - 20 Aug 2025
Viewed by 521
Abstract
This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to [...] Read more.
This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)
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30 pages, 8330 KB  
Article
iBANDA: A Blockchain-Assisted Defense System for Authentication in Drone-Based Logistics
by Simeon Okechukwu Ajakwe, Ikechi Saviour Igboanusi, Jae-Min Lee and Dong-Seong Kim
Drones 2025, 9(8), 590; https://doi.org/10.3390/drones9080590 - 20 Aug 2025
Viewed by 1249
Abstract
Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real [...] Read more.
Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real time, while blockchain-assisted solutions are often hindered by high latency and limited scalability. Methods: To address these challenges, we propose iBANDA, a blockchain- and AI-assisted DDS framework. The system integrates a lightweight You Only Look Once 5 small (YOLOv5s) object detection model with a Snowball-based Proof-of-Stake consensus mechanism to enable dual-layer authentication of drones and their attached payloads. Authentication processes are coordinated through an edge-deployable decentralized application (DApp). Results: The experimental evaluation demonstrates that iBANDA achieves a mean average precision of 99.5%, recall of 100%, and an F1-score of 99.8% at an inference time of 0.021 s, validating its suitability for edge devices. Blockchain integration achieved an average network latency of 97.7 ms and an end-to-end transaction latency of 1.6 s, outperforming Goerli, Sepolia, and Polygon Mumbai testnets in scalability and throughput. Adversarial testing further confirmed resilience to Sybil attacks and GPS spoofing, maintaining a false acceptance rate below 2.5% and continuity above 96%. Conclusions: iBANDA demonstrates that combining AI-based visual detection with blockchain consensus provides a secure, low-latency, and scalable authentication mechanism for UAV-based logistics. Future work will explore large-scale deployment in heterogeneous UAV networks and formal verification of smart contracts to strengthen resilience in safety-critical environments. Full article
(This article belongs to the Topic Advances in Integrative AI, Machine Learning, and Big Data for Transformative Applications)
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19 pages, 565 KB  
Article
Dynamic Recovery and a Resilience Metric for UAV Swarms Under Attack
by Tianzhen Hu, Yan Zong, Ningyun Lu and Bin Jiang
Drones 2025, 9(8), 589; https://doi.org/10.3390/drones9080589 - 20 Aug 2025
Viewed by 726
Abstract
Unmanned Aerial Swarms are attracting widespread interest in fields such as disaster response, environmental monitoring, and agriculture. However, there is still a lack of effective recovery strategies and comprehensive performance metrics for UAV swarms facing communication attacks, especially in capturing dynamic recovery. The [...] Read more.
Unmanned Aerial Swarms are attracting widespread interest in fields such as disaster response, environmental monitoring, and agriculture. However, there is still a lack of effective recovery strategies and comprehensive performance metrics for UAV swarms facing communication attacks, especially in capturing dynamic recovery. The aim of this study is to recover the split and disconnected UAV swarm under attacks. A dynamic recovery method is proposed under attacks by establishing the relationship between algebraic connectivity and consensus speed. The proposed recovery method enables each UAV to selectively establish communication links with responsive UAVs based on the proposed recovery method to reduce communication cost, rather than linking with all neighbours within communication range. Based on this, a set of performance indexes is introduced, considering factors such as consensus ability, communication efficiency, mission execution, and resource consumption. Furthermore, a resilience metric is proposed to quantitatively assess the efficiency of recovery and consensus transition, providing a comprehensive measure of the ability to reach consensus after attacks. Simulations utilizing the second-order consensus protocol and dynamics validate that the consensus speed of the proposed recovery method is 18.88% faster than random recovery. The proposed resilience metric captures the change in the time from recovery to new consensus state, and the resilience of the proposed recovery method is 66.99% higher than random recovery. Full article
(This article belongs to the Collection Drones for Security and Defense Applications)
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33 pages, 5010 KB  
Article
Three-Dimensional Deployment Optimization of UAVs Using Symbolic Control for Coverage Enhancement via UAV-Mounted 6G Mobile Base Stations
by Mete Özbaltan, Serkan Çaşka, Cihat Şeker, Merve Yıldırım, Hazal Su Bıçakcı Yeşilkaya, Faruk Emre Aysal, Emrah Kuzu and Murat Demir
Drones 2025, 9(8), 588; https://doi.org/10.3390/drones9080588 - 20 Aug 2025
Viewed by 1181
Abstract
We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base stations. The number and placement optimization of UAV-mounted 6G mobile base stations, deployed [...] Read more.
We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base stations. The number and placement optimization of UAV-mounted 6G mobile base stations, deployed to support terrestrial base stations during periods of increased population density in a given area, are addressed using a symbolic limited optimal discrete controller synthesis technique. Within the scope of this study, the UAVs’ altitude and attitude behaviors are optimized to ensure the most efficient trajectory toward the designated base station coordinates. Additionally, at their new locations, these behaviors are adjusted to facilitate accurate coverage estimation from the base stations they serve. In the deployment optimization of UAVs, the placement of base stations is determined using received signal strength data obtained through the ray-tracing-based channel modeling technique. The channel model considered critical parameters such as path loss, received power, weather loss, and foliage loss. Final average path loss values of 102.3 dB, 111.7 dB, and 127.4 dB were obtained at the carrier frequencies of 7 GHz, 26 GHz, and 140 GHz, respectively. These findings were confirmed with MATLAB-based ray tracing simulations. Our proposed approach is validated through experimental evaluations, demonstrating superior performance compared to existing methods reported in the literature. Full article
(This article belongs to the Special Issue Space–Air–Ground Integrated Networks for 6G)
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24 pages, 11770 KB  
Article
Secure Communication and Resource Allocation in Double-RIS Cooperative-Aided UAV-MEC Networks
by Xi Hu, Hongchao Zhao, Dongyang He and Wujie Zhang
Drones 2025, 9(8), 587; https://doi.org/10.3390/drones9080587 - 19 Aug 2025
Viewed by 596
Abstract
In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC [...] Read more.
In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article
(This article belongs to the Section Drone Communications)
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20 pages, 16915 KB  
Article
Cluster Characteristics Analysis of UAV Air-to-Air Channels Based on Ray Tracing and Wasserstein Generative Adversarial Network with Gradient Penalty
by Liwei Han, Xiaomin Chen, Boyu Hua, Qingzhe Deng, Kai Mao, Weizhi Zhong and Qiuming Zhu
Drones 2025, 9(8), 586; https://doi.org/10.3390/drones9080586 - 18 Aug 2025
Viewed by 662
Abstract
Air-to-air (A2A) communication plays a vital role in low-altitude unmanned aerial vehicle (UAV) networks and demands accurate channel modeling to support system analysis and design. A key challenge in A2A channel modeling lies in extracting reliable cluster characteristics, which are often limited due [...] Read more.
Air-to-air (A2A) communication plays a vital role in low-altitude unmanned aerial vehicle (UAV) networks and demands accurate channel modeling to support system analysis and design. A key challenge in A2A channel modeling lies in extracting reliable cluster characteristics, which are often limited due to the scarcity of measurement data. To overcome this limitation, a cluster characteristic analysis method is proposed for UAV A2A channels in built-up environments. First, we reconstruct virtual urban environments, followed by the acquisition of A2A channel data using ray tracing (RT) techniques. Then, a kernel power density (KPD) clustering algorithm is applied to group the multipath components (MPCs). To enhance the modeling accuracy of intra-cluster angular offsets in both elevation and azimuth domains, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is further introduced for generative modeling. A comprehensive analysis is conducted on key cluster characteristics, including the intra-cluster number of MPCs, intra-cluster delay and angular spreads, number of clusters, and angular distributions. The numerical results demonstrate that the proposed WGAN-GP-based approach achieves superior angular fitting accuracy compared to conventional empirical distribution methods. Full article
(This article belongs to the Special Issue Technologies and Applications of UAV Channel Models in Communications and Spectrum Awareness: 2nd Edition)
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24 pages, 9014 KB  
Article
A Computational Method for the Nonlinear Attainable Moment Set of Tailless UAVs in Flight-Control-Oriented Scenarios
by Linxiao Han, Peng Zhang, Yingyang Wang, Yuan Bian and Jianbo Hu
Drones 2025, 9(8), 585; https://doi.org/10.3390/drones9080585 - 18 Aug 2025
Viewed by 490
Abstract
Tailless unmanned aerial vehicles (UAVs) achieve high-agility maneuvers with flight control systems. The attainable moment set (AMS) provides critical theoretical foundations and constraints for their optimization. A computational method is proposed herein to address controllability limitations caused by nonlinear aerodynamic effectiveness. This method [...] Read more.
Tailless unmanned aerial vehicles (UAVs) achieve high-agility maneuvers with flight control systems. The attainable moment set (AMS) provides critical theoretical foundations and constraints for their optimization. A computational method is proposed herein to address controllability limitations caused by nonlinear aerodynamic effectiveness. This method incorporates dual constraints on control surface angles and angular rates for the nonlinear AMS, aiming to meet the demands of attitude tracking dynamics in flight control systems. First, a quantitative model is established to correlate dual deflection constraints with aerodynamic moment amplitude and bandwidth limitations. Next, we construct a computational framework for the incremental attainable moment set (IAMS) based on differential inclusion theory. For monotonic nonlinear aerodynamic effectiveness, the vertices of the IAMS are updated using local interpolation, yielding the incremental nonlinear attainable moment set (INAMS). When non-monotonic nonlinearity occurs, stationary points are calculated to adjust the control effectiveness matrix and admissible control set, thereby reducing computational errors induced by non-monotonic characteristics. Furthermore, the effective actions set, derived from a time-varying incremental nonlinear attainable moment set, quantifies the residual moment envelope of tailless UAVs during maneuvers. Comparative simulations indicate that the proposed method achieves correct computation under nonlinear aerodynamic conditions while reliably determining safe flight boundaries during control failure. Full article
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24 pages, 5199 KB  
Article
Analysis and Proposal of Strategies for the Management of Drone Swarms Through Wi-Fi Technologies
by Guido Betcher-Sbrolla, Elena Lopez-Aguilera and Eduard Garcia-Villegas
Drones 2025, 9(8), 584; https://doi.org/10.3390/drones9080584 - 18 Aug 2025
Viewed by 1027
Abstract
The main purpose of this paper is to explore the benefits of combining two radio interfaces onboard an unmanned aerial vehicle (UAV) to communicate with a ground control station (GCS) and other UAVs inside a swarm. The goals are to use the IEEE [...] Read more.
The main purpose of this paper is to explore the benefits of combining two radio interfaces onboard an unmanned aerial vehicle (UAV) to communicate with a ground control station (GCS) and other UAVs inside a swarm. The goals are to use the IEEE 802.11ah standard (Wi-Fi HaLow) combined with the IEEE 802.11ax specification (Wi-Fi 6/6E) to enable real-time video transmission from UAVs to the GCS. While airport runway inspection serves as the proof-of-concept use case, the proposed multi-hop architectures apply to other medium-range UAV operations (i.e., a few kilometers) requiring real-time video transmission, such as natural disaster relief and agricultural monitoring. Several scenarios in which a UAV swarm performs infrastructure inspection are emulated. During the missions, UAVs have to send real-time video to the GCS through a multi-hop network when some damage in the infrastructure is found. The different scenarios are studied by means of emulation. Emulated scenarios are defined using different network architectures and radio technologies. Once the emulations finish, different performance metrics related to time, energy and the multi-hop video transmission network are analyzed. The capacity of a multi-hop network is a limiting factor for the transmission of high-quality video. As a first contribution, an expression to find this capacity from distances between UAVs in the emulated scenario is found using the NS-3 simulator. Then, this expression is applied in the algorithms in charge of composing the multi-hop network to offer on-demand quality video. However, the main contribution of this work lies in the development of efficient mechanisms for exchanging control information between UAVs and the GCS, and for forming a multi-hop network to transmit video. Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
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58 pages, 7149 KB  
Review
Secure Communication in Drone Networks: A Comprehensive Survey of Lightweight Encryption and Key Management Techniques
by Sayani Sarkar, Sima Shafaei, Trishtanya S. Jones and Michael W. Totaro
Drones 2025, 9(8), 583; https://doi.org/10.3390/drones9080583 - 18 Aug 2025
Cited by 1 | Viewed by 3135
Abstract
Deployment of Unmanned Aerial Vehicles (UAVs) continues to expand rapidly across a wide range of applications, including environmental monitoring, precision agriculture, and disaster response. Despite their increasing ubiquity, UAVs remain inherently vulnerable to security threats due to resource-constrained hardware, energy limitations, and reliance [...] Read more.
Deployment of Unmanned Aerial Vehicles (UAVs) continues to expand rapidly across a wide range of applications, including environmental monitoring, precision agriculture, and disaster response. Despite their increasing ubiquity, UAVs remain inherently vulnerable to security threats due to resource-constrained hardware, energy limitations, and reliance on open wireless communication channels. These factors render traditional cryptographic solutions impractical, thereby necessitating the development of lightweight, UAV-specific security mechanisms. This review article presents a comprehensive analysis of lightweight encryption techniques and key management strategies designed for energy-efficient and secure UAV communication. Special emphasis is placed on recent cryptographic advancements, including the adoption of the ASCON family of ciphers and the emergence of post-quantum algorithms that can secure UAV networks against future quantum threats. Key management techniques such as blockchain-based decentralized key exchange, Physical Unclonable Function (PUF)-based authentication, and hierarchical clustering schemes are evaluated for their performance and scalability. To ensure comprehensive protection, this review introduces a multilayer security framework addressing vulnerabilities from the physical to the application layer. Comparative analysis of lightweight cryptographic algorithms and multiple key distribution approaches is conducted based on energy consumption, latency, memory usage, and deployment feasibility in dynamic aerial environments. Unlike design- or implementation-focused studies, this work synthesizes existing literature across six interconnected security dimensions to provide an integrative foundation. Our review also identifies key research challenges, including secure and efficient rekeying during flight, resilience to cross-layer attacks, and the need for standardized frameworks supporting post-quantum cryptography in UAV swarms. By highlighting current advancements and research gaps, this study aims to guide future efforts in developing secure communication architectures tailored to the unique operational constraints of UAV networks. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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20 pages, 4156 KB  
Article
A Model-Driven Multi-UAV Spectrum Map Fast Fusion Method for Strongly Correlated Data Environments
by Shengwen Wu, Hui Ding, He Li, Zhipeng Lin, Jie Zeng, Qianhao Gao, Weizhi Zhong and Jun Zhou
Drones 2025, 9(8), 582; https://doi.org/10.3390/drones9080582 - 17 Aug 2025
Viewed by 473
Abstract
Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance. In this paper, we propose a new multi-unmanned [...] Read more.
Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance. In this paper, we propose a new multi-unmanned aerial vehicle (UAV) spectrum map fusion method based on differential ridge regression. We first construct spectrum maps of UAVs by using differential features of spectrum data. Next, we present a spectrum map fusion model by leveraging the spatial distribution characteristic of spectrum data. To reduce the sensitivity of the fusion model to the strongly correlated data, a new map fusion regularization term is designed, which introduces l2-norm to constrain the fusion regularization parameters and compress the ridge regression coefficient sizes. As a result, accurate spectrum maps can be constructed for the environments with highly correlated spectrum data. We then formulate a model-driven solution to the spectrum map fusion problem and derive its lower bound. By combining the propagation characteristics of the spectrum signal with the developed Lagrange duality, we can guarantee the convergence of map fusion processing while enhancing the convergence rate. Finally, we propose an accelerated maximally split alternating directions method of multipliers (AMS-ADMM) to reduce the computational complexity of spectrum map construction. Simulation results demonstrate that our proposed method can effectively eliminate external noise interference and outliers, and achieve an accuracy improvement of more than 27% compared to state-of-the-art fusion methods in spectrum map construction with low complexity. Full article
(This article belongs to the Special Issue Technologies and Applications of UAV Channel Models in Communications and Spectrum Awareness: 2nd Edition)
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19 pages, 1277 KB  
Article
Reinforcement Learning-Based PD Controller Gains Prediction for Quadrotor UAVs
by Serhat Sönmez, Luca Montecchio, Simone Martini, Matthew J. Rutherford, Alessandro Rizzo, Margareta Stefanovic and Kimon P. Valavanis
Drones 2025, 9(8), 581; https://doi.org/10.3390/drones9080581 - 16 Aug 2025
Cited by 1 | Viewed by 784
Abstract
This paper presents a reinforcement learning (RL)-based methodology for the online fine-tuning of PD controller gains, with the goal of bridging the gap between simulation-trained controllers and real-world quadrotor applications. As a first step toward real-world implementation, the proposed approach applies a Deep [...] Read more.
This paper presents a reinforcement learning (RL)-based methodology for the online fine-tuning of PD controller gains, with the goal of bridging the gap between simulation-trained controllers and real-world quadrotor applications. As a first step toward real-world implementation, the proposed approach applies a Deep Deterministic Policy Gradient (DDPG) algorithm—an off-policy actor–critic method—to adjust the gains of a quadrotor attitude PD controller during flight. The RL agent was initially trained offline in a simulated environment, using MATLAB/Simulink 2024a and the UAV Toolbox Support Package for PX4 Autopilots v1.14.0. The trained controller was then validated through both simulation and experimental flight tests. Comparative performance analyses were conducted between the hand-tuned and RL-tuned controllers. Our results demonstrate that the RL-based tuning method successfully adapts the controller gains in real time, leading to improved attitude tracking and reduced steady-state error. This study constitutes the first stage of a broader research effort investigating RL-based PID, LQR, MRAC, and Koopman-integrated RL-based PID controllers for real-time quadrotor control. Full article
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31 pages, 5802 KB  
Review
Exploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Systematic Review
by Qian Zhong, Neil Bose, Jimin Hwang and Ting Zou
Drones 2025, 9(8), 580; https://doi.org/10.3390/drones9080580 - 15 Aug 2025
Viewed by 1994
Abstract
AUVs offer the potential for in situ MP detection at constant, pre-set depths in marine environments. By carrying onboard MP detectors, AUVs can serve as alternatives to traditional methods of sample collection, processing, and analysis, while also addressing the inefficiencies and complexities associated [...] Read more.
AUVs offer the potential for in situ MP detection at constant, pre-set depths in marine environments. By carrying onboard MP detectors, AUVs can serve as alternatives to traditional methods of sample collection, processing, and analysis, while also addressing the inefficiencies and complexities associated with conventional detection procedures. This study conducts a comprehensive review of existing and potential MP detection methods that can be integrated with AUVs for in situ detection. In particular, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review analyzes selected studies on MP detection using AUVs. It finds that real-time, in situ MP detection via AUVs or multi-AUV systems remains underdeveloped. Key challenges include deep-sea communication, sensor integration, and underwater durability. The review highlights the current advances, research gaps, and future directions for AUV-based MP detection technologies. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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22 pages, 4524 KB  
Article
RAEM-SLAM: A Robust Adaptive End-to-End Monocular SLAM Framework for AUVs in Underwater Environments
by Yekai Wu, Yongjie Li, Wenda Luo and Xin Ding
Drones 2025, 9(8), 579; https://doi.org/10.3390/drones9080579 - 15 Aug 2025
Viewed by 1019
Abstract
Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remains a significant challenge. While vision-based Simultaneous Localization and Mapping (SLAM) [...] Read more.
Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remains a significant challenge. While vision-based Simultaneous Localization and Mapping (SLAM) provides a cost-effective alternative for AUV navigation, existing methods are primarily designed for terrestrial applications and struggle to address underwater-specific issues, such as poor illumination, dynamic interference, and sparse features. To tackle these challenges, we propose RAEM-SLAM, a robust adaptive end-to-end monocular SLAM framework for AUVs in underwater environments. Specifically, we propose a Physics-guided Underwater Adaptive Augmentation (PUAA) method that dynamically converts terrestrial scene datasets into physically realistic pseudo-underwater images for the augmentation training of RAEM-SLAM, improving the system’s generalization and adaptability in complex underwater scenes. We also introduce a Residual Semantic–Spatial Attention Module (RSSA), which utilizes a dual-branch attention mechanism to effectively fuse semantic and spatial information. This design enables adaptive enhancement of key feature regions and suppression of noise interference, resulting in more discriminative feature representations. Furthermore, we incorporate a Local–Global Perception Block (LGP), which integrates multi-scale local details with global contextual dependencies to significantly improve AUV pose estimation accuracy in dynamic underwater scenes. Experimental results on real-world underwater datasets demonstrate that RAEM-SLAM outperforms state-of-the-art SLAM approaches in enabling precise and robust navigation for AUVs. Full article
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28 pages, 9030 KB  
Article
UAV Path Planning via Semantic Segmentation of 3D Reality Mesh Models
by Xiaoxinxi Zhang, Zheng Ji, Lingfeng Chen and Yang Lyu
Drones 2025, 9(8), 578; https://doi.org/10.3390/drones9080578 - 14 Aug 2025
Cited by 1 | Viewed by 1177
Abstract
Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality [...] Read more.
Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article
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33 pages, 941 KB  
Review
Noise Prediction and Mitigation for UAS and eVTOL Aircraft: A Survey
by Waleed Raza and Richard S. Stansbury
Drones 2025, 9(8), 577; https://doi.org/10.3390/drones9080577 - 14 Aug 2025
Viewed by 2837
Abstract
The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey [...] Read more.
The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings. Full article
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17 pages, 4409 KB  
Article
Combined Robust Control for Quadrotor UAV Using Model Predictive Control and Super-Twisting Algorithm
by Shunsuke Komiyama, Kenji Uchiyama and Kai Masuda
Drones 2025, 9(8), 576; https://doi.org/10.3390/drones9080576 - 13 Aug 2025
Viewed by 1168
Abstract
This paper proposes a robust control method of trajectory tracking for quadrotors under disturbance conditions, combining Model Predictive Control (MPC) and the Super-Twisting Algorithm (STA). MPC is a control strategy that solves an optimization problem by predicting the finite time future response from [...] Read more.
This paper proposes a robust control method of trajectory tracking for quadrotors under disturbance conditions, combining Model Predictive Control (MPC) and the Super-Twisting Algorithm (STA). MPC is a control strategy that solves an optimization problem by predicting the finite time future response from the model under control at each time step. However, MPC cannot guarantee control performance under disturbances such as modeling errors and wind gusts because it predicts future states of the control objects using a nominal model. To solve this problem, we propose a composite control method that uses Adaptive Super-Twisting Sliding Mode Disturbance Observer (ASTSMDO), which constrains the system to follow the MPC’s nominal model. The effectiveness of the proposed method is confirmed through numerical simulation. Compared to conventional MPC, the proposed controller achieves superior robustness and trajectory tracking performance under modeling error and wind disturbance. Full article
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22 pages, 4478 KB  
Article
A Hierarchical Decoupling Task Planning Method for Multi-UAV Collaborative Multi-Region Coverage with Task Priority Awareness
by Yiyuan Li, Weiyi Chen, Bing Fu, Zhonghong Wu and Lingjun Hao
Drones 2025, 9(8), 575; https://doi.org/10.3390/drones9080575 - 13 Aug 2025
Viewed by 500
Abstract
This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and [...] Read more.
This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and path planning. First, a coverage voyage estimation model is constructed based on regional geometric features to provide basic data for subsequent task allocation. Second, an improved multi-objective, multi-population grey wolf optimizer (IM2GWO) is designed to solve the task allocation problem; this integrates adaptive genetic operations and the multi-population coevolutionary mechanism. Finally, a globally optimal coverage path is generated based on the improved dynamic programming (DP). Simulation results indicate that the proposed method effectively reduces total task duration while boosting overall coverage benefits through the aggregation of high-value regions. IM2GWO demonstrates statistically superior performance with respect to the Pareto front distribution index across all test scenarios. Meanwhile, the path planning module based on DP can effectively reduce the overall coverage path cost. 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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17 pages, 1182 KB  
Article
Task Allocation Algorithm for Heterogeneous UAV Swarm with Temporal Task Chains
by Haixiao Liu, Zhichao Shao, Quanzhi Zhou, Jianhua Tu and Shuo Zhu
Drones 2025, 9(8), 574; https://doi.org/10.3390/drones9080574 - 13 Aug 2025
Viewed by 1050
Abstract
In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task [...] Read more.
In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task chains assignment process, this places higher demands on the real-time dynamic decision-making and system fault tolerance of its task assignment algorithm. This study addresses the sequential dependencies among disaster reconnaissance, material delivery, and effect evaluation stages. A task allocation model for heterogeneous UAV swarm targeting temporal task chains is formulated, with objectives to minimize task completion time and energy consumption. A dynamic coalition formation algorithm based on temporary leader election and multi-round negotiation mechanisms is proposed to enhance continuous decision-making capabilities in complex disaster environments. A simulation scenario involving twenty heterogeneous UAVs and seven temporal rescue task chains is constructed. The results show that the proposed algorithm reduces average task completion time by 15.2–23.7% and average fuel consumption by 18.3–26.4% compared with cooperative network protocols and distributed auctions, with up to a 43% reduction in fuel consumption fluctuations. Full article
(This article belongs to the Special Issue The Conceptual Design Methodology for UAV: New Research and New Development)
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19 pages, 4228 KB  
Article
Data-Driven Optimal Bipartite Containment Tracking for Multi-UAV Systems with Compound Uncertainties
by Bowen Chen, Mengji Shi, Zhiqiang Li and Kaiyu Qin
Drones 2025, 9(8), 573; https://doi.org/10.3390/drones9080573 - 13 Aug 2025
Viewed by 330
Abstract
With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper designs a data-driven optimal bipartite containment tracking control scheme [...] Read more.
With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper designs a data-driven optimal bipartite containment tracking control scheme for multi-UAV systems under compound uncertainties. A novel Dynamic Iteration Regulation Strategy (DIRS) is proposed, which enables real-time adjustment of the learning iteration step according to the task-specific demands. Compared with conventional fixed-step data-driven algorithms, the DIRS provides greater flexibility and computational efficiency, allowing for better trade-offs between the performance and cost. First, the optimal bipartite containment tracking control problem is formulated, and the associated coupled Hamilton–Jacobi–Bellman (HJB) equations are established. Then, a data-driven iterative policy learning algorithm equipped with the DIRS is developed to solve the optimal control law online. The stability and convergence of the proposed control scheme are rigorously analyzed. Furthermore, the control law is approximated via the neural network framework without requiring full knowledge of the model. Finally, numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed DIRS-based optimal containment tracking scheme for multi-UAV systems, which can reduce the number of iterations by 88.27% compared to that for the conventional algorithm. Full article
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24 pages, 4473 KB  
Article
Reliability Analysis of Multi-Rotor Drone Electric Propulsion System Considering Controllability and FDEP
by Nve Xiao, Xianrun Qiao, Xi Chen and Boyang Li
Drones 2025, 9(8), 572; https://doi.org/10.3390/drones9080572 - 13 Aug 2025
Viewed by 651
Abstract
The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with [...] Read more.
The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with functional dependence (FDEP). With the insufficient basis for selecting k-values, the problem of incalculable reliability caused by computational space explosion due to voting gates, and the uncertain impact of functional dependence on system reliability, we propose a reliability evaluation method based on controllability theory and BN (Bayesian network) reconstruction. The drone is dynamically modeled, and a control model is built, and k-values are selected through different failure combination controllability evaluations. We model the system with BN, use functional dependent components as BN node inputs, and reconstruct BN via an adder model to solve the problem of exponential growth in the conditional probability table. This paper analyzes system reliability, safety, and the impact of FDEP on the system, and conducts component importance analysis. The result provides important reference for the reliability, safety assessment, and dynamic maintenance processes of multi-rotor drone. Full article
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11 pages, 742 KB  
Article
Evaluating UAVs for Non-Directional Beacon Calibration: A Cost-Effective Alternative to Manned Flight Inspections
by Andrej Novák and Patrik Veľký
Drones 2025, 9(8), 571; https://doi.org/10.3390/drones9080571 - 13 Aug 2025
Viewed by 494
Abstract
The increasing demand for efficient aviation navigation system inspections has led to the use of Unmanned Aerial Vehicles (UAVs) as a flexible and cost-effective alternative to traditional manned aircraft. This study emphasizes the operational advantages of UAVs in transforming flight inspections, including Non-Directional [...] Read more.
The increasing demand for efficient aviation navigation system inspections has led to the use of Unmanned Aerial Vehicles (UAVs) as a flexible and cost-effective alternative to traditional manned aircraft. This study emphasizes the operational advantages of UAVs in transforming flight inspections, including Non-Directional Beacon (NDB) calibration. Following the successful performance evaluation of an NDB system in Banská Bystrica, Slovakia, using a manned aircraft, a UAV was deployed on the same flight path to validate its ability to replicate the procedure in terms of trajectory only, without performing any signal measurement. The UAV maintained accurate flight paths and continuous communication throughout the mission. A specialized rotatory system, operating at 868 MHz, enabled real-time tracking and ensured stable communication over long distances. The manned aircraft test revealed a maximum bearing deviation of 13.47° at 3.37 NM and a minimum received signal strength of −90 dBm, which approaches the ICAO threshold for en route navigation (±10°) but remains usable for diagnostic purposes. The UAV flight did not include signal capture but successfully completed the 40 NM profile with a circular error probable (CEP95) of 2.8 m and communication link uptime of 99.8%, confirming that the vehicle can meet procedural trajectory fidelity. These findings support the feasibility of UAV-based NDB inspections and provide the foundation for future test phases with onboard signal monitoring systems. Full article
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22 pages, 3506 KB  
Article
UAV Navigation Using EKF-MonoSLAM Aided by Range-to-Base Measurements
by Rodrigo Munguia, Juan-Carlos Trujillo and Antoni Grau
Drones 2025, 9(8), 570; https://doi.org/10.3390/drones9080570 - 12 Aug 2025
Viewed by 579
Abstract
This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial [...] Read more.
This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial vehicles (UAVs) to mitigate error accumulation, preserve map consistency, and operate reliably in environments without GPS. This integration facilitates sustained autonomous navigation with estimation error remaining bounded over extended trajectories. Theoretical validation is provided through a nonlinear observability analysis, highlighting the general benefits of integrating range data into the SLAM framework. The system’s performance is evaluated through both virtual experiments and real-world flight data. The real-data experiments confirm the practical relevance of the approach and its ability to improve estimation accuracy in realistic scenarios. Full article
(This article belongs to the Section Drone Design and Development)
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20 pages, 27363 KB  
Article
A Heterogeneous Time-Series Soft Actor–Critic Method for Quadruped Locomotion
by Zhaoxu Wang, Zhuoying Chen and Huiping Li
Drones 2025, 9(8), 569; https://doi.org/10.3390/drones9080569 - 12 Aug 2025
Viewed by 744
Abstract
The locomotion control of unmanned quadruped robots has been one of the greatest challenges in robotics. Deep reinforcement learning has made great achievements in robot control. However, extracting effective features from historical information to improve locomotion agility is still an open and challenging [...] Read more.
The locomotion control of unmanned quadruped robots has been one of the greatest challenges in robotics. Deep reinforcement learning has made great achievements in robot control. However, extracting effective features from historical information to improve locomotion agility is still an open and challenging problem. In this paper, a heterogeneous time-series soft actor–critic (HTS-SAC) method is proposed to enable better policy learning from historical data. Firstly, four mutual information decision conditions are developed for feature selection, which can analyze the correlation between input states and motion performance, obtaining the importance of temporal features of different lengths. Then, according to the results of feature optimization, a novel heterogeneous time-series neural network and the HTS-SAC locomotion control method are designed. Finally, the effectiveness of the proposed method is validated on different terrains using a Laikago quadruped robot simulation model. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Drones and Its Applications: 2nd Edition)
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19 pages, 1476 KB  
Article
Network Design and Content Deployment Optimization for Cache-Enabled Multi-UAV Socially Aware Networks
by Yikun Zou, Gang Wang, Guanyi Chen, Jinlong Wang, Siyuan Yu, Chenxu Wang and Zhiquan Zhou
Drones 2025, 9(8), 568; https://doi.org/10.3390/drones9080568 - 12 Aug 2025
Viewed by 435
Abstract
Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the [...] Read more.
Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the utilization of the storage capacity of UAVs without a proper UAV coordination mechanism. This work proposes a multi-UAV-enabled caching socially aware network (SAN) where UAVs can switch roles by adjusting the social attributes, effectively enhancing data interaction within the UAVs. The proposed network breaks down communication barriers at the UAV layer and integrates the collective storage resources by incorporating social awareness mechanisms to mitigate these imbalances. Furthermore, we formulate a multi-objective optimization problem (MOOP) with the objectives of maximizing both the diversity of cached content and the total request probability (RP) of the network, while employing a multi-objective particle swarm optimization (MOPSO) algorithm with a mutation strategy to approximate the Pareto front. Finally, the impact of key parameters on the Pareto front is analyzed under various scenarios. Simulation results validate the benefits of leveraging social attributes for resource allocation and demonstrate the effectiveness and convergence of the proposed algorithm for the multi-UAV caching strategy. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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32 pages, 3055 KB  
Article
Research on Scheduling Return Communication Tasks for UAV Swarms in Disaster Relief Scenarios
by Zhangquan Tang, Yuanyuan Jiao, Xiao Wang, Xiaogang Pan and Jiawu Peng
Drones 2025, 9(8), 567; https://doi.org/10.3390/drones9080567 - 12 Aug 2025
Viewed by 734
Abstract
This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that [...] Read more.
This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs. Full article
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27 pages, 15885 KB  
Article
Model-Free UAV Navigation in Unknown Complex Environments Using Vision-Based Reinforcement Learning
by Hao Wu, Wei Wang, Tong Wang and Satoshi Suzuki
Drones 2025, 9(8), 566; https://doi.org/10.3390/drones9080566 - 12 Aug 2025
Viewed by 1963
Abstract
Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency [...] Read more.
Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency on accurate environment models. Moreover, many deep-learning-based solutions either use RGB images prone to visual noise or optimize only a single objective. In contrast, this paper proposes a unified, model-free vision-based DRL framework that directly maps onboard depth images and UAV state information to continuous navigation commands through a single convolutional policy network. This end-to-end architecture eliminates the need for explicit mapping and modular coordination, significantly improving responsiveness and robustness. A novel multi-objective reward function is designed to jointly optimize path efficiency, safety, and energy consumption, enabling adaptive flight behavior in unknown complex environments. The trained policy demonstrates generalization in diverse simulated scenarios and transfers effectively to real-world UAV flights. Experiments show that our approach achieves stable navigation and low latency. Full article
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19 pages, 2982 KB  
Article
Immersion and Invariance Adaptive Control for Unmanned Helicopter Under Maneuvering Flight
by Xu Zhou, Yousong Xu, Siliang Du and Qijun Zhao
Drones 2025, 9(8), 565; https://doi.org/10.3390/drones9080565 - 12 Aug 2025
Cited by 1 | Viewed by 592
Abstract
An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap [...] Read more.
An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap response of blades. The consummate flight dynamic model is complemented by wind tunnel-validated fuselage/tail rotor load regressions. Secondly, a linear state–space equation is derived via the small perturbation linearization method based on the flight dynamic model within the body coordinate system. A decoupled model is formulated based on the linear state–space equation by employing the implicit model approach. Subsequently, a system of ordinary differential equations is constructed, which is related to the deviation between actual velocity and its expected value, along with higher-order derivatives of this discrepancy. The I&I (immersion and invariance) theory is then employed to facilitate the design of a non-cascade control loop. Finally, the response of desired velocity in longitudinal channel is simulated with step signal to compare the control effect with a PID (proportional–integral–derivative) controller. By adjusting the coefficients, the response progress of the PID controller is similar to the effect of adaptive controller with I&I theory. However, there is no obvious overshoot in the process with I&I adaptive controller, and the average response amplitude accounts for 16.69% of the random white noise, which is 7.38% of the oscillation level under the PID controller. The parameter tuning complexity when employing I&I theory is significantly lower than that of the PID controller, which is evaluated by mathematical derivations and simulations. Meanwhile, the sidestep and pirouette maneuvers are simulated and analyzed to examine the controller in accordance with the performance criteria outlined in the ADS-33E-REF standards. The simulation results demonstrate that the speed expectation-oriented asymptotic stability control can achieve a fast response. Both sidestep and pirouette maneuvers can satisfy the desired performance requirements stipulated by ADS-33E-REF. Full article
(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)
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26 pages, 5933 KB  
Article
Optimizing Data Distribution Service Discovery for Swarm Unmanned Aerial Vehicles Through Preloading and Network Awareness
by HyeonGyu Lee, Doyoon Kim and SungTae Moon
Drones 2025, 9(8), 564; https://doi.org/10.3390/drones9080564 - 11 Aug 2025
Viewed by 1525
Abstract
Collaborative unmanned aerial vehicle (UAV) swarm operations using the open-source PX4–ROS2 system have been extensively studied for reconnaissance and autonomous missions. PX4–ROS2 utilizes data distribution service (DDS) middleware to ensure network flexibility and support scalable operations. DDS enables decentralized information exchange through its [...] Read more.
Collaborative unmanned aerial vehicle (UAV) swarm operations using the open-source PX4–ROS2 system have been extensively studied for reconnaissance and autonomous missions. PX4–ROS2 utilizes data distribution service (DDS) middleware to ensure network flexibility and support scalable operations. DDS enables decentralized information exchange through its discovery protocol. However, in dense swarm environments, the default initialization process of this protocol generates considerable communication overhead, which hinders reliable peer detection among UAVs. This study introduces an optimized DDS discovery scheme incorporating two key strategies: a preloading method that embeds known participant data before deployment, and a dynamic network awareness approach that regulates discovery behavior based on real-time connectivity. Integrated into PX4–ROS2, the proposed scheme was assessed through both simulations and real-world testing. Results demonstrate that the optimized discovery process reduced peak packet traffic by over 90% during the initial exchange phase, thereby facilitating more stable and scalable swarm operations in wireless environments. Full article
(This article belongs to the Section Drone Communications)
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17 pages, 2749 KB  
Article
Real-Time Wind Estimation for Fixed-Wing UAVs
by Yifan Fu, Weigang An, Xingtao Su and Bifeng Song
Drones 2025, 9(8), 563; https://doi.org/10.3390/drones9080563 - 11 Aug 2025
Viewed by 1548
Abstract
Wind estimation plays a crucial role in atmospheric boundary layer research and aviation flight safety. Fixed-wing UAVs enable rapid and flexible detection across extensive boundary layer regions. Traditional meteorological fixed-wing UAVs require either additional wind measurement sensors or sustained turning maneuvers for wind [...] Read more.
Wind estimation plays a crucial role in atmospheric boundary layer research and aviation flight safety. Fixed-wing UAVs enable rapid and flexible detection across extensive boundary layer regions. Traditional meteorological fixed-wing UAVs require either additional wind measurement sensors or sustained turning maneuvers for wind estimation, increasing operational costs while inevitably reducing mission duration and coverage per flight. This paper proposes a real-time wind estimation method based on an Unscented Kalman Filter (UKF) without aerodynamic sensors. The approach utilizes only standard UAV avionics—GNSS, pitot tube, and Inertial Measurement Unit (IMU)—to estimate wind fields. To validate accuracy, the method was integrated into a meteorological UAV equipped with a wind vane sensor, followed by multiple flight tests. Comparison with wind vane measurements shows real-time wind speed errors below 1 m/s and wind direction errors within 20° (0.349 rad). Results demonstrate the algorithm’s effectiveness for real-time atmospheric boundary layer wind estimation using conventional fixed-wing UAVs. Full article
(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)
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