Drones

34 pages, 3638 KB

Open AccessArticle

Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability Under Diverse Urban Scenarios

by Yinying Liu, Jianmeng Liu, Xin Shi and Cheng Tang

Drones 2026, 10(4), 269; https://doi.org/10.3390/drones10040269 - 8 Apr 2026

Abstract

Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely [...] Read more.

Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely on idealized assumptions, overlooking heterogeneous cooperation under multiple stations, multiple time windows, and real-world transport conditions. To address these gaps, we propose the Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability (MSUUCDSP) to minimize the total travel and waiting time of UAVs and UGVs. To solve the problem, we propose a mixed-integer linear programming (MILP) model with a novel mathematical approach and a Hybrid Large Neighborhood Search (HLNS) algorithm. Additionally, we adopt a Hidden Markov Model (HMM)-based map-matching method and big data techniques to capture realistic operational characteristics. Computational experiments are conducted on various realistic instances under four diverse scenarios. Results show that UAV–UGV cooperation significantly improves efficiency, reducing total time cost by 17.12% compared with single-mode delivery, and they reveal substantial discrepancies between idealized assumptions and realistic scenarios. We further develop an ArcGIS-based simulation to support practical implementation. The findings provide valuable insights for decision-making and engineering applications for logistics operators. Full article

(This article belongs to the Special Issue Advances in Drone Applications for Last-Mile Delivery Operations)

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28 pages, 7099 KB

Open AccessArticle

AI-Driven Tethered Drone Surveillance for Maritime Security in Ports and Coastal Areas

by Alberto Belmonte-Hernández, Briac Grauby, Anaida Fernández García, Solange Tardi, Torbjørn Houge, Hidalgo García Bango and Álvaro Gutiérrez

Drones 2026, 10(4), 268; https://doi.org/10.3390/drones10040268 - 8 Apr 2026

Abstract

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted [...] Read more.

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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25 pages, 1501 KB

Open AccessArticle

MA-JTATO: Multi-Agent Joint Task Association and Trajectory Optimization in UAV-Assisted Edge Computing System

by Yunxi Zhang and Zhigang Wen

Drones 2026, 10(4), 267; https://doi.org/10.3390/drones10040267 - 7 Apr 2026

Abstract

With the rapid development of applications such as smart cities and the industrial internet, the computation-intensive tasks generated by massive sensing devices pose significant challenges to traditional cloud computing paradigms. Unmanned aerial vehicle (UAV)-assisted edge computing systems, leveraging their high mobility and wide-area [...] Read more.

With the rapid development of applications such as smart cities and the industrial internet, the computation-intensive tasks generated by massive sensing devices pose significant challenges to traditional cloud computing paradigms. Unmanned aerial vehicle (UAV)-assisted edge computing systems, leveraging their high mobility and wide-area coverage capabilities, offer an innovative architecture for low-latency and highly reliable edge services. However, the practical deployment of such systems faces a highly complex multi-objective optimization problem featured by the tight coupling of task offloading decisions, UAV trajectory planning, and edge server resource allocation. Conventional optimization methods are difficult to adapt to the dynamic and high-dimensional characteristics of this problem, leading to suboptimal system performance. To address this critical challenge, this paper constructs an intelligent collaborative optimization framework for UAV-assisted edge computing systems and formulates the system quality of service (QoS) optimization problem as a mixed-integer non-convex programming problem with the dual objectives of minimizing task processing latency and reducing overall system energy consumption. A multi-agent joint task association and trajectory optimization (MA-JTATO) algorithm based on hybrid reinforcement learning is proposed to solve this intractable problem, which innovatively decouples the original coupled optimization problem into three interrelated subproblems and realizes their collaborative and efficient solution. Specifically, the Advantage Actor-Critic (A2C) algorithm is adopted to realize dynamic and optimal task association between UAVs and edge servers for discrete decision-making requirements; the multi-agent deep deterministic policy gradient (MADDPG) method is employed to achieve cooperative and energy-efficient trajectory planning for multiple UAVs to meet the needs of continuous control in dynamic environments; and convex optimization theory is applied to obtain a closed-form optimal solution for the efficient allocation of computational resources on edge servers. Simulation results demonstrate that the proposed MA-JTATO algorithm significantly outperforms traditional baseline algorithms in enhancing overall QoS, effectively validating the framework’s superior performance and robustness in dynamic and complex scenarios. Full article

(This article belongs to the Section Drone Communications)

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19 pages, 4855 KB

Open AccessArticle

Development of a Thermal Helipad for UAVs and Detection with Deep Learning

by Ersin Demiray, Mehmet Konar and Seda Arık Hatipoğlu

Drones 2026, 10(4), 266; https://doi.org/10.3390/drones10040266 - 7 Apr 2026

Abstract

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this [...] Read more.

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

(This article belongs to the Topic Advanced Technologies and Applications for Unmanned Systems)

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Graphical abstract

25 pages, 3586 KB

Open AccessArticle

A Classification Algorithm of UAV and Bird Target Based on L/K Dual-Band Micro-Doppler and Mamba

by Tao Zhang and Xiaoru Song

Drones 2026, 10(4), 265; https://doi.org/10.3390/drones10040265 - 6 Apr 2026

Abstract

To address the challenge of accurately distinguishing UAVs and birds in a low-altitude detection field, this paper proposes a classification algorithm of UAVs and birds based on L/K dual-band micro-Doppler spectrograms and Mamba. We establish a dual-band radar detection model for unmanned aerial [...] Read more.

To address the challenge of accurately distinguishing UAVs and birds in a low-altitude detection field, this paper proposes a classification algorithm of UAVs and birds based on L/K dual-band micro-Doppler spectrograms and Mamba. We establish a dual-band radar detection model for unmanned aerial vehicles (UAVs) and birds, provide a method for characterizing the Doppler parameters of the echo signals, and research a UAV and bird target classification network model that integrates micro-Doppler and Mamba. Based on a dual-branch encoding framework, we use Patch block decomposition to design a classification model to serialize the two-dimensional spectrogram of the echo signal, and introduce the Mamba state-space backbone network to extract the long-term sequence features of the target’s micro-motion. The main breakthrough of the proposed classification algorithm lies in the feature fusion stage, where a late fusion strategy is adopted to integrate the dual-path high-level representation measures, fully leveraging the sensitivity of the K-band to high-frequency textures and the scale complementarity of the L-band. Then, according to the joint loss function of mutual learning and contrastive learning, we improve the model’s prediction consistency and representation discriminability. Through experimental testing, the results show that the proposed method can effectively classify UAVs and birds, and compared with other algorithms, the accuracy rate reaches 97.5%. Full article

(This article belongs to the Section Drone Communications)

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32 pages, 43664 KB

Open AccessArticle

MVFF: Multi-View Feature Fusion Network for Small UAV Detection

by Kunlin Zou, Haitao Zhao, Xingwei Yan, Wei Wang, Yan Zhang and Yaxiu Zhang

Drones 2026, 10(4), 264; https://doi.org/10.3390/drones10040264 - 4 Apr 2026

Abstract

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, [...] Read more.

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones: 2nd Edition)

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24 pages, 4159 KB

Open AccessArticle

A UAV–Satellite Hybrid Pipeline for Wildfire Detection and Dynamic Perimeter Prediction

by Hossein Keshmiri and Khan A. Wahid

Drones 2026, 10(4), 263; https://doi.org/10.3390/drones10040263 - 4 Apr 2026

Abstract

Effective wildfire management demands seamless integration of real-time detection and long-term spread forecasting. This paper proposes a novel power-efficient UAV–satellite hybrid pipeline that synergizes the agility of UAVs with the scale of satellite intelligence. The system begins with a dashboard-guided, multi-UAV detection module [...] Read more.

Effective wildfire management demands seamless integration of real-time detection and long-term spread forecasting. This paper proposes a novel power-efficient UAV–satellite hybrid pipeline that synergizes the agility of UAVs with the scale of satellite intelligence. The system begins with a dashboard-guided, multi-UAV detection module that scores fire likelihood from historical satellite data and enables scalable, energy-efficient deployment with low-latency onboard processing. This aerial component ensures persistent surveillance and reliable ignition detection, supported by a Dual LoRa (Long Range) communication scheme for robust and low-power connectivity. It achieves an F1-score of 97.4% while minimizing power consumption to extend operational flight times. Following detection, the pipeline transitions to a dynamic perimeter-prediction phase utilizing a custom Canadian boreal dataset. We employ a Squeeze-and-Excitation Residual U-Net (SE-ResUNet) to model spatiotemporal fire propagation based on static terrain and dynamic environmental features. The model was validated using a dynamic simulation framework that evaluates temporal consistency and convergence behavior against final cumulative burned-area masks, effectively addressing the absence of daily ground truth. Under these conditions, the model achieves a recall of 84% and an AUC of 0.97, demonstrating a strong capability to delineate active fire fronts. By coupling dashboard-driven UAV sensing with satellite-based predictive modeling, this work establishes a modular, foundational framework to support data-scarce forecasting in modern wildfire management. Full article

(This article belongs to the Special Issue UAVs and UGVs Robotics for Emergency Response in a Changing Climate)

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30 pages, 1668 KB

Open AccessArticle

Joint Optimization for Energy Efficiency in UAV-Enabled Networks

by Cheru Haile Tesfay, Zheng Xiang, Long Yang, Jabar Mahmood, Shehzad Ashraf Chaudhry and Ashok Kumar Das

Drones 2026, 10(4), 262; https://doi.org/10.3390/drones10040262 - 4 Apr 2026

Abstract

Unmanned Aerial Vehicles (UAVs) were originally designed for military and surveillance applications but are now significant in smart agriculture, wireless communication, and product delivery. In contrast to an Internet Service Provider (ISP), which typically relies on fixed base stations, which can fail in [...] Read more.

Unmanned Aerial Vehicles (UAVs) were originally designed for military and surveillance applications but are now significant in smart agriculture, wireless communication, and product delivery. In contrast to an Internet Service Provider (ISP), which typically relies on fixed base stations, which can fail in the event of a disaster, UAVs offer more stable alternatives. Because IoT devices, sensors, and ground users have limited processing power and battery life, there is a need for energy-efficient solutions. Meanwhile, users still expect high data rates. UAV-based wireless networks can meet these needs, even in harsh or disaster-hit areas. Current research focuses on improving energy efficiency and data transmission by optimizing UAV flight paths and scheduling. In this work, we tackle these issues by formulating a mixed-integer non-convex optimization problem that jointly considers device scheduling and UAV trajectory. We further decompose it into the following two parts: energy-efficient scheduling among ground users (

P_{2}

) and the trajectory optimization of UAVs (

P_{3}

). To address these issues, we develop a linear programming relaxation approach, a Quadratically Constrained Quadratic Programming (QCQP)-based Successive Convex Approximation (SCA) scheme, and the Block Coordinate Descent (BCD) algorithm. Experimental results demonstrate that our approach outperforms the state of the art in both power consumption and transmission rate. Full article

(This article belongs to the Special Issue Drone Communication, Networking, and Trajectory Control in Urban Environments)

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35 pages, 3925 KB

Open AccessReview

A Scoping Review of the Crazyflie Ecosystem: An Evaluation of an Open-Source Platform for Nano-Aerial Robotics Research

by Rareș Crăciun and Adrian Burlacu

Drones 2026, 10(4), 261; https://doi.org/10.3390/drones10040261 - 3 Apr 2026

Abstract

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone [...] Read more.

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone application development in research and academia. The Crazyflie quadcopter has emerged as a leading open-source platform for education and research in aerial robotics due to its modularity and low cost. Despite its rapid evolution, there is currently no comprehensive synthesis mapping its diverse applications across hardware configurations and research domains. This evaluation systematically charts existing research on the Crazyflie platform, outlining its development, identifying relevant hardware and software configurations, categorizing major research topics, and identifying knowledge gaps. A systematic search was performed on three major databases, Scopus, Web of Science and Google Scholar, for studies published between 2015 and 2025. The results indicate a rapid growth in scientific production, an involved research community and very diverse thematic approaches. Expansion decks for the Crazyflie have been analyzed together with their relation to specific fields of research. While control systems remain the primary research theme, there is a significant shift toward artificial intelligence and swarm robotics. Full article

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

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28 pages, 3555 KB

Open AccessArticle

Safety Control with Adaptive Safety Sets and Belief Dynamics for LAV Under Uncertainty

by Meijiao Zhao, Yidi Wang and Wei Zheng

Drones 2026, 10(4), 260; https://doi.org/10.3390/drones10040260 - 3 Apr 2026

Abstract

This paper presents a novel safety control framework for Loitering Aerial Vehicles (LAVs) operating under non-Gaussian measurement uncertainty. The approach integrates variational inference-based belief dynamics with adaptive buffered half-space constraints, transforming complex probabilistic collision avoidance into tractable convex geometric conditions. This ensures rigorous [...] Read more.

This paper presents a novel safety control framework for Loitering Aerial Vehicles (LAVs) operating under non-Gaussian measurement uncertainty. The approach integrates variational inference-based belief dynamics with adaptive buffered half-space constraints, transforming complex probabilistic collision avoidance into tractable convex geometric conditions. This ensures rigorous safety guarantees while avoiding the conservatism of robust methods. An event-triggered hierarchical planner further balances global optimality with local responsiveness, enabling rapid navigation in dynamic environments. Validated through 1000 Monte Carlo simulations, the framework achieves a 95.4% success rate. Comparative analysis demonstrates that the proposed method compares favorably with state-of-the-art safety-set approaches by effectively resolving local infeasibility issues and maintaining real-time efficiency without compromising probabilistic safety assurance. Full article

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35 pages, 2955 KB

Open AccessArticle

Research on Autonomous Navigation and Obstacle Avoidance Methods for High-Speed Large-Inertia Rotor UAV

by Huajie Xiong, Baoguo Yu and Yunlong Zhang

Drones 2026, 10(4), 259; https://doi.org/10.3390/drones10040259 - 3 Apr 2026

Abstract

High-speed near-ground flight presents critical challenges for large-inertia UAVs carrying payloads, including complex obstacles and communication-denied environments. Unlike agile small drones, these platforms require both rapid path planning and strict adherence to trajectory tracking constraints for safe obstacle avoidance. This paper proposes a [...] Read more.

High-speed near-ground flight presents critical challenges for large-inertia UAVs carrying payloads, including complex obstacles and communication-denied environments. Unlike agile small drones, these platforms require both rapid path planning and strict adherence to trajectory tracking constraints for safe obstacle avoidance. This paper proposes a two-stage autonomous navigation framework tailored for large-inertia UAVs. The framework integrates: (1) an enhanced LiDAR model with physical optical noise for improved simulation fidelity; (2) an ESDF + OctoMap dual-map construction method supporting global search and local optimization; and (3) a global BIT* planner combined with a B-spline local optimizer embedding dynamic, smoothness, and tracking accuracy constraints to ensure path feasibility and trackability. Simulation results demonstrate an average planning time of 0.86 ms, outperforming NAVIGATION, Informed RRT*, MPC Planner, and ESDF Optimization by 29.6–52.0%, with a 100% obstacle avoidance success rate and trajectory tracking RMSE of 0.28 m over a 350 m flight distance, along with strong parameter and noise robustness. Actual flight tests on a 9.4 kg quadrotor UAV confirm the algorithm’s effectiveness in map construction, path planning, and obstacle avoidance in environments with 15 obstacles, while maintaining computational overhead suitable for onboard deployment. These results establish the proposed framework as an effective solution for high-speed autonomous navigation of large-inertia UAVs in complex near-ground environments. Full article

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Graphical abstract

33 pages, 15356 KB

Open AccessArticle

Active Acoustic Sensing of Ground Surface Condition Using a Drone-Mounted Speaker–Microphone Array

by Kotaro Hoshiba, Kai Shirota, Yuta Tsukamoto and Hiroshi Yamaura

Drones 2026, 10(4), 258; https://doi.org/10.3390/drones10040258 - 3 Apr 2026

Abstract

Rapid assessment of ground surface conditions is essential in disaster response and search-and-rescue operations, where drones are increasingly deployed for aerial inspection and victim localization. This paper proposes an active acoustic sensing method for estimating ground surface conditions using a drone-mounted speaker and [...] Read more.

Rapid assessment of ground surface conditions is essential in disaster response and search-and-rescue operations, where drones are increasingly deployed for aerial inspection and victim localization. This paper proposes an active acoustic sensing method for estimating ground surface conditions using a drone-mounted speaker and microphone array. The method is based on the multiple signal classification framework and enables three-dimensional localization of reflection points according to the principle of echolocation. A key feature of the proposed approach is that it shares both hardware and signal processing components with acoustic-based victim search, allowing simultaneous execution of surface sensing and sound source localization (SSL) on a single drone platform without increasing system complexity. Outdoor experiments were conducted to evaluate sensing performance for ground surface anomalies, specifically ground surface depressions and cracks. The experimental results clarify the achievable sensing performance and coverage in real environments and reveal key factors affecting detection performance. The feasibility of simultaneous execution of active acoustic sensing and SSL was also investigated, and the mutual interactions between sensing and localization performance were clarified. These findings highlight both the potential and the practical limitations of integrating environmental sensing and victim localization on a single drone platform. Full article

(This article belongs to the Special Issue Advances in UAV Guidance, Navigation, and Control Through Acoustic Technologies)

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26 pages, 12902 KB

Open AccessArticle

Soft Threshold Denoising-Based Environmental Adaptive UAV Signal Modulation Recognition for Small-Sample Scenarios

by Fang Jin, Yang Shao, Yunhong He, Zhihao Ye, Fangmin He, Zhipeng Lin and Han Xiao

Drones 2026, 10(4), 257; https://doi.org/10.3390/drones10040257 - 3 Apr 2026

Abstract

As a key technology for wireless signal identification, modulation recognition plays an important role in the fields of unmanned aerial vehicle (UAV) communications, low-altitude spectrum management, etc. However, the accuracy of modulation recognition often cannot be guaranteed in scenarios with serious noise interference [...] Read more.

As a key technology for wireless signal identification, modulation recognition plays an important role in the fields of unmanned aerial vehicle (UAV) communications, low-altitude spectrum management, etc. However, the accuracy of modulation recognition often cannot be guaranteed in scenarios with serious noise interference when a few samples are available. In this paper, we propose an intelligent modulation recognition method for UAV signals based on small-sample augmentation and soft threshold denoising. We first propose a new dual-driven dataset expansion method by combining the UAV air–ground channel propagation model with the received data samples. Then, we construct a background learning-based long short-term memory (BL-LSTM) model to extract the environmental background features embedded in the UAV signal, including Line-of-Sight (LoS) state, multi-scale fading parameters and Doppler shift characteristics. We integrate environmental background information into the data training model and optimize the authenticity of data distribution. As a result, the model adaptability can be enhanced. Finally, we construct a deep residual shrinkage network based on the soft threshold function (STF-DRSN). By leveraging the capability of the soft threshold that resists noise interference, we integrate it into each residual block of the deep residual shrinkage network. Simulation results show that compared with the state of the art, our method can improve the modulation recognition accuracy of UAV signals in small-sample scenarios. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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20 pages, 4690 KB

Open AccessArticle

Optimal Power Management Research on a Flight Range-Lengthened Multirotor Aircraft

by Siqi An, Mengxuan Wang, Xiaoyang Qiu, Yufei Zhao, Guichao Cai, Yaoming Fu and Xu Peng

Drones 2026, 10(4), 256; https://doi.org/10.3390/drones10040256 - 3 Apr 2026

Abstract

The multirotor configuration unmanned aerial vehicle faces a significant challenge in simultaneously achieving long-range operation and high payload capacity. This paper investigates the power management strategy for a novel fuel–electric hybrid aircraft that incorporates lifting wings to reduce rotor load and a range-extend [...] Read more.

The multirotor configuration unmanned aerial vehicle faces a significant challenge in simultaneously achieving long-range operation and high payload capacity. This paper investigates the power management strategy for a novel fuel–electric hybrid aircraft that incorporates lifting wings to reduce rotor load and a range-extend system to enhance energy supply. An equivalent consumption minimization strategy is developed to optimize, in real time, the power distribution between the internal combustion engine and the battery. The primary innovation of this paper lies in the application and rigorous validation of the equivalent consumption minimization strategy on this new aircraft configuration, which effectively minimizes total energy cost by optimally balancing fuel consumption and battery degradation, resulting in significantly reduced fuel usage and a more stable power output compared to conventional approaches. Full article

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

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14 pages, 1601 KB

Open AccessArticle

Real-Time UAV-Based Oil Pipeline and Visual Anomaly Detection Using YOLOv26n: A Dataset and Edge-Deployment Study

by Hatem Keshk and Ayman Abdallah

Drones 2026, 10(4), 255; https://doi.org/10.3390/drones10040255 - 3 Apr 2026

Abstract

Ensuring the structural integrity and operational safety of oil and gas pipelines is a critical challenge due to their extensive geographical coverage and exposure to environmental and anthropogenic risks. Traditional inspection approaches including ground patrols and manned aerial surveys are labor-intensive, costly, and [...] Read more.

Ensuring the structural integrity and operational safety of oil and gas pipelines is a critical challenge due to their extensive geographical coverage and exposure to environmental and anthropogenic risks. Traditional inspection approaches including ground patrols and manned aerial surveys are labor-intensive, costly, and often lack real-time responsiveness. While unmanned aerial vehicles (UAVs) enable flexible and high-resolution monitoring, their practical deployment requires lightweight, robust detection models capable of real-time inference on embedded edge hardware under heterogeneous environmental conditions. This paper presents an end-to-end, edge-deployable UAV inspection framework for simultaneous detection of above-ground pipelines and visually observable anomaly/leak indicators using the official Ultralytics YOLOv26n object detector. A curated dataset of 6127 UAV images acquired across desert, semi-urban, and industrial environments was annotated with two classes (Pipeline and Anomaly/Leak) and partitioned into training 87.5%, validation 8.3%, and testing 4.2% subsets. The detector was fine-tuned from COCO-pretrained weights for 300 epochs at 600 × 600 resolution and evaluated using COCO-style metrics. On the held-out test set, the proposed model achieved 92.4% mAP@0.5 and 75.0% mAP@0.5:0.95, with 89.7% precision, 90.2% recall, and 89.9% F1-score at the selected operating threshold. Optimized TensorRT deployment on an NVIDIA Jetson Xavier NX sustained real-time inference at 18 FPS, demonstrating suitability for onboard UAV processing. Rather than proposing a new detector architecture, the study contributes a domain-specific annotated UAV dataset, deployment-oriented benchmarking, and an end-to-end edge inference workflow for corridor-scale monitoring. The proposed framework can help reduce environmental contamination risk and improve personnel safety during pipeline inspection. Full article

(This article belongs to the Special Issue Autonomy Challenges in Unmanned Aviation)

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31 pages, 12121 KB

Open AccessArticle

Momentum-Accelerated Phase Synchronization for UAV Swarm Collaborative Beamforming

by Fei Xie, Longqing Li, Chan Liu, Zhiping Huang, Yongjie Zhao and Junyu Wei

Drones 2026, 10(4), 254; https://doi.org/10.3390/drones10040254 - 2 Apr 2026

Abstract

Distributed beamforming in UAV swarms requires fast and accurate carrier-phase alignment under sparse connectivity and propagation-induced phase bias. This paper proposes a physics-aware decentralized synchronization framework for quasi-static UAV swarm beamforming by integrating momentum-accelerated Metropolis–Hastings consensus with position-aided phase pre-compensation. To preserve phase [...] Read more.

Distributed beamforming in UAV swarms requires fast and accurate carrier-phase alignment under sparse connectivity and propagation-induced phase bias. This paper proposes a physics-aware decentralized synchronization framework for quasi-static UAV swarm beamforming by integrating momentum-accelerated Metropolis–Hastings consensus with position-aided phase pre-compensation. To preserve phase evolution on the circular manifold, a sinusoidal coupling law is adopted, while the momentum term improves convergence in sparse random geometric graphs. A propagation model is further established to characterize how geometric separation and ranging uncertainty translate into residual phase error and coherent power loss. Under small-signal conditions, local stability is analyzed, and Monte Carlo simulations are conducted to evaluate convergence, synchronization accuracy, robustness, and beam-focusing performance. Results show that, at 2.4 GHz with low-centimeter ranging uncertainty, the proposed method achieves sub-wavelength synchronization accuracy while providing an effective balance among convergence speed, accuracy, and complexity. Compared with standard Metropolis–Hastings, fixed-weight, and other accelerated consensus methods, the proposed scheme converges faster over most sparse topologies. Although its steady-state accuracy is slightly lower than that of filter-based predictive methods such as KF-DFPC in some cases, those schemes incur higher implementation and computational overhead. Therefore, from the perspectives of decentralized realization and practical deployment, the proposed method is more suitable for lightweight phase synchronization in distributed UAV swarms. Full article

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21 pages, 6478 KB

Open AccessArticle

Experimental Investigation of Distributed Array Adaptive Beamforming for Interference Suppression in UAV Swarms

by Rio King, Gregory Huff, Trevor Bois and Bailey Campbell

Drones 2026, 10(4), 253; https://doi.org/10.3390/drones10040253 - 1 Apr 2026

Abstract

This paper investigates the use of adaptive beamforming algorithms for communication systems and sensing networks using motion-dynamic distributed random arrays. These distributed arrays include swarms of unmanned aerial vehicles (UAVs) and are formed by unconnected antennas mounted on independent mobile platforms. This paper [...] Read more.

This paper investigates the use of adaptive beamforming algorithms for communication systems and sensing networks using motion-dynamic distributed random arrays. These distributed arrays include swarms of unmanned aerial vehicles (UAVs) and are formed by unconnected antennas mounted on independent mobile platforms. This paper investigates the robustness of adaptive beamforming algorithms subject to nonidealities intrinsic to distributed random arrays such as positional error, hardware noise variations, and non-uniform elements. A simulation framework developed to evaluate various beamforming algorithms in the presence of non-idealities demonstrates that minimum variance distortionless response (MVDR) beamforming is sensitive to nominal positional errors, while minimum mean squared error (MMSE) beamforming maintains interference suppression regardless of positional error and is robust to non-uniform elements. Experiments confirm that MMSE beamforming demonstrates interference suppression in real-world channels with heterogeneous hardware. These results establish adaptive mean-squared-error-based beamforming as a robust solution for distributed random arrays. Full article

(This article belongs to the Section Drone Communications)

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26 pages, 8867 KB

Open AccessArticle

A Physics-Guided Aeromagnetic Interference Compensation Method for Geomagnetic Sensing in GNSS-Denied UAV Swarm Systems

by Shiyao Wang, Liran Ma, Yue Wang, Dongguang Li and Jianbin Luo

Drones 2026, 10(4), 252; https://doi.org/10.3390/drones10040252 - 31 Mar 2026

Abstract

Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV [...] Read more.

Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV swarm navigation. To address this challenge, this paper proposes PG-TLNet, a physics-guided aeromagnetic interference compensation framework based on the extended Tolles–Lawson (T–L) model. By integrating onboard state information (current, voltage, and attitude) with magnetic measurements through physics-consistency constraints and a lightweight multi-branch convolutional neural network, the framework enables robust real-time compensation under strong and time-varying interference while remaining suitable for resource-constrained UAV nodes. Experimental validation using multiple scalar magnetometers under heterogeneous interference conditions, with amplitudes up to 1000 nT, shows that PG-TLNet consistently outperforms the conventional T–L model across all sensing nodes, maintaining residual magnetic interference at approximately 0–30 nT under long-duration and highly dynamic operations. The proposed method achieves an improvement ratio (IR) of up to 15 with an end-to-end inference latency below 94 μs. These results indicate that PG-TLNet meets the practical measurement fidelity requirements for geomagnetic navigation in GNSS-denied environments. By ensuring reliable and consistent magnetic measurements at the individual UAV node level, the proposed framework establishes a practical sensing foundation for geomagnetic navigation and distributed magnetic sensing in UAV swarm systems operating in GNSS-denied environments. Full article

(This article belongs to the Special Issue Intelligent Cooperative Technologies of UAV Swarm Systems)

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22 pages, 3401 KB

Open AccessArticle

TACOS: Task Agnostic Coordinator of a Multi-Drone System

by Alessandro Nazzari, Roberto Rubinacci and Marco Lovera

Drones 2026, 10(4), 251; https://doi.org/10.3390/drones10040251 - 31 Mar 2026

Abstract

When a single pilot is responsible for managing a multi-drone system, the task may demand varying levels of autonomy, from direct control of individual UAVs to group-level coordination to fully autonomous swarm behaviors for accomplishing high-level tasks. Enabling such interaction requires a framework [...] Read more.

When a single pilot is responsible for managing a multi-drone system, the task may demand varying levels of autonomy, from direct control of individual UAVs to group-level coordination to fully autonomous swarm behaviors for accomplishing high-level tasks. Enabling such interaction requires a framework that supports multiple modes of shared autonomy. As language models continue to improve in reasoning and planning, they provide a natural foundation for such systems. In this paper, we present TACOS (Task-Agnostic COordinator of a multi-drone System), a unified framework that enables high-level natural language control of multi-UAV systems through a Large Language Model (LLM). TACOS integrates three key capabilities into a single architecture: a one-to-many natural language interface for intuitive user interaction, an intelligent coordinator for translating user intent into structured task plans, and an autonomous agent that executes plans and interacts with the real world. TACOS allows an LLM to interact with a library of executable APIs, bridging semantic reasoning with real-time multi-robot coordination. We demonstrate the system on a real-world multi-drone system and conduct an ablation study to assess the contribution of each module. Full article

(This article belongs to the Special Issue Advanced Optimization Strategies for UAV Mission Planning and Operation)

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