Drones

20 pages, 5264 KB

Open AccessReview

Drone-Based Surveillance Methods for Non-Lethal Shark Mitigation in Nearshore Environments: Current Applications, Challenges, and Future Directions

by Kim I. Monteforte, Paul A. Butcher and Brendan P. Kelaher

Drones 2026, 10(7), 556; https://doi.org/10.3390/drones10070556 - 22 Jul 2026

Viewed by 616

Abstract

Unprovoked shark bites are one of the most recognised human–wildlife conflicts and present a significant concern for beach safety. Lethal methods of shark mitigation have previously been implemented to reduce the risk of such incidents; however, due to their destructive impacts on vulnerable [...] Read more.

Unprovoked shark bites are one of the most recognised human–wildlife conflicts and present a significant concern for beach safety. Lethal methods of shark mitigation have previously been implemented to reduce the risk of such incidents; however, due to their destructive impacts on vulnerable marine wildlife, non-lethal approaches are increasingly preferred. In recent years, drones have emerged as an effective, minimally invasive tool for real-time shark surveillance in surf zones. Drones are also used to collect valuable data on shark ecology and behaviour in nearshore environments, which can inform evidence-based policies. This review examines the utility of drones for shark surveillance programs by identifying key operational parameters and associated challenges of drone-based methods. We investigate emerging technologies, including long-range drones, remotely operated or autonomous flight missions, and the use of artificial intelligence for shark detection and species identification. We also outline current drone licensing, laws, and regulations, noting that these vary across administrative regions (i.e., countries and states). Overall, this review provides insight into the expansion of drone-based shark surveillance in nearshore areas and its potential to enhance beach safety, support management decisions, and advance scientific knowledge without negatively impacting shark populations. Full article

(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)

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25 pages, 2935 KB

Open AccessArticle

Certification-Oriented Requirements and Model Verification Methodology for UAV Systems

by Jan M. Kelner, Sławomir Klimaszewski, Sławomir Brzózka and Wojciech Stecz

Drones 2026, 10(7), 555; https://doi.org/10.3390/drones10070555 - 22 Jul 2026

Viewed by 369

Abstract

The paper presents a certification-oriented methodology for requirements management and model verification in the design of unmanned aerial vehicle (UAV) systems. The proposed approach addresses challenges in certifying safety-critical UAV platforms whose architectures include custom-developed hardware and software components. The methodology integrates requirements [...] Read more.

The paper presents a certification-oriented methodology for requirements management and model verification in the design of unmanned aerial vehicle (UAV) systems. The proposed approach addresses challenges in certifying safety-critical UAV platforms whose architectures include custom-developed hardware and software components. The methodology integrates requirements engineering, functional hazard analysis (FHA), and formal model verification into a unified design and validation workflow. Methods for categorizing, prioritizing, and decomposing system requirements are presented to support the development of reliable UAV software and operational procedures. A systematic approach for mapping certification requirements and FHA safety functions to UAV operational scenarios and system use cases is introduced, enabling traceability between safety requirements, system behavior, and verification artifacts. The proposed framework employs Unified Modeling Language (UML) state-machine models, validated using linear temporal logic (LTL) and computation tree logic (CTL). Model verification is performed using the NuSMV symbolic model checker to assess the correctness, completeness, consistency, and safety properties of operational scenarios. A practical case study concerning UAV handover between ground control stations (GCSs) demonstrates the applicability of the proposed method. The analysis identifies inconsistencies in the operational model and shows how formal verification supports the early detection of unsafe or incomplete system behaviors. The presented approach supports the development of certification-ready UAV systems by improving requirements traceability, reducing verification ambiguities, and facilitating the validation of safety-critical flight-control procedures. Full article

(This article belongs to the Special Issue The Conceptual Design Methodology for UAV: New Research and New Development (2nd Edition))

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28 pages, 3665 KB

Open AccessArticle

Predicting Rural Acceptance of Drone Delivery: An LLM-Enhanced Empirical Analysis for Equitable Service Design

by Ziping Wang, Henan Zhu, Kofi Nyarko and Xiaozheng He

Drones 2026, 10(7), 554; https://doi.org/10.3390/drones10070554 - 22 Jul 2026

Viewed by 436

Abstract

While drone delivery has gained significant scholarly and industrial interest, rural residents’ acceptance of these systems remains underexplored, despite the region’s acute logistics challenges and service inequities. Using survey data from rural U.S. residents, this study first estimates an ordered logistic regression (OLR) [...] Read more.

While drone delivery has gained significant scholarly and industrial interest, rural residents’ acceptance of these systems remains underexplored, despite the region’s acute logistics challenges and service inequities. Using survey data from rural U.S. residents, this study first estimates an ordered logistic regression (OLR) model to identify factors associated with five-level drone delivery acceptance. The study then compares OLR, multinomial logistic regression (MNL), Random Forest (RF), XGBoost, and LightGBM under matched feature sets to evaluate whether nonlinear machine-learning models improve prediction beyond the interpretable statistical baseline. Open-ended responses are coded into LLM-derived sentiment labels and added as supplementary predictors to test whether unstructured feedback improves acceptance prediction. Results show that willingness to pay is the strongest predictor of acceptance, while equitable same-day delivery demand and post-pandemic attitude adjustment are also positively associated with higher acceptance. Household disability status and urban accessibility are not significant after adjustment. In the five-level analysis, OLR provides a strong ordinal baseline, while XGBoost and other tree-based models improve selected class-level prediction metrics. In the binary high-acceptance analysis, machine-learning models show stronger predictive performance, especially when structured predictors are combined with sentiment features. This study contributes to rural drone-delivery literature by linking service equity, perceived value, and LLM-derived sentiment within a comparable statistical and machine-learning framework for rural service design. Full article

(This article belongs to the Section Innovative Urban Mobility)

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33 pages, 4743 KB

Open AccessReview

Advances in Trajectory Prediction for High-Speed UAVs: A Review

by Wenqin Han, Shuangxi Liu, Xianyu Wu and Wei Zhao

Drones 2026, 10(7), 553; https://doi.org/10.3390/drones10070553 - 21 Jul 2026

Viewed by 399

Abstract

High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, [...] Read more.

High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, and hybrid-driven methods have not yet been comprehensively examined under a unified framework, limiting the understanding of their relative strengths and applicability. To address this gap, this paper systematically reviews the evolution and current technical status of these paradigms, categorized by the core logic underlying prediction methods. The principles and applicability limits of physical process modeling and state estimation algorithms are analyzed, along with emerging applications of machine learning and deep learning for trajectory feature extraction and pattern recognition. State-of-the-art architectures involving the integration of physical constraints and data-driven learning are discussed. Standard evaluation metrics are introduced to facilitate performance benchmarking of existing methods. Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements. Lastly, key challenges are summarized, and future research directions are outlined to advance trajectory prediction methodologies. The provided insights can inform method selection and promote the development of high-accuracy prediction systems. Full article

(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition)

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30 pages, 1591 KB

Open AccessArticle

BIM-Constrained Elastic Fusion Navigation Framework for UAV Bridge Inspection Under Intermittent GNSS Outage

by Zeyu Li, Hui Li, Chenhong Xiangli, Yuanyuan Shen, Yi Yu and Fei Li

Drones 2026, 10(7), 552; https://doi.org/10.3390/drones10070552 - 21 Jul 2026

Viewed by 355

Abstract

UAV bridge inspection requires centimeter-level global positioning in the engineering coordinate frame to associate detected defects with BIM component IDs and mileage stakes. Intermittent GNSS outages beneath beams, inside box girders, and in pier-dense regions cause conventional navigation methods to accumulate drift or [...] Read more.

UAV bridge inspection requires centimeter-level global positioning in the engineering coordinate frame to associate detected defects with BIM component IDs and mileage stakes. Intermittent GNSS outages beneath beams, inside box girders, and in pier-dense regions cause conventional navigation methods to accumulate drift or produce discontinuous pose estimates. This paper presents BCEF-Nav, a BIM-constrained elastic fusion navigation framework for UAV bridge inspection. The framework adaptively adjusts GNSS constraints according to signal availability and replaces degraded GNSS references with semantic–geometric constraints from a high-precision as-built BIM model within a sliding-window optimizer. Semantic-guided feature matching suppresses false correspondences in repetitive bridge structures. BCEF-Nav uses only low-cost commercial sensors and is compatible with mainstream inspection UAVs. Digital twin simulations and field experiments achieved an ATE RMSE of 5.7 cm after 120 s of complete GNSS occlusion and a 100% positioning success rate with positioning errors below 10 cm, outperforming seven recent state-of-the-art baselines. Full article

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26 pages, 524 KB

Open AccessArticle

Synchronization-Free Underwater Acoustic Localization for Autonomous Platforms: A Neural Network TDOA Approach and the Role of Receiver Geometry

by Yigit Mahmutoglu

Drones 2026, 10(7), 551; https://doi.org/10.3390/drones10070551 - 20 Jul 2026

Viewed by 318

Abstract

Accurate underwater acoustic localization is a key enabling capability for autonomous underwater vehicles and underwater drones, which cannot rely on satellite positioning while submerged and therefore depend on acoustic methods to determine their position. Localization based on time-of-arrival (TOA) measurements requires precise time [...] Read more.

Accurate underwater acoustic localization is a key enabling capability for autonomous underwater vehicles and underwater drones, which cannot rely on satellite positioning while submerged and therefore depend on acoustic methods to determine their position. Localization based on time-of-arrival (TOA) measurements requires precise time synchronization between the source and the receivers, which is difficult to maintain in practical deployments. The time-difference-of-arrival (TDOA) representation removes this requirement but discards part of the absolute timing information, reducing localization accuracy. This study investigates a physics-based feedforward multilayer perceptron (FF-MLP) framework for two-dimensional range–depth underwater localization that learns directly from the arrival-time structure induced by sound-speed variability and multipath, with the receiver-array geometry treated as a central design variable for improving synchronization-free TDOA localization. Using multi-receiver arrival times generated with the BELLHOP beam-tracing model under a representative Mediterranean underwater environment, synchronous TOA, biased TOA, and TDOA measurement representations are compared on a common footing, and the effects of the receiver depth distribution, the number of receivers, and the reference-receiver position are systematically examined through Monte Carlo evaluation. The results show that the receiver-array geometry, rather than the measurement representation alone, is decisive for TDOA-based localization: with an appropriately designed geometry, synchronization-free TDOA localization achieves a median two-dimensional RMSE of 11.28 m, approaching the accuracy attainable with synchronous TOA, which requires precise time synchronization. These findings indicate that careful receiver-geometry design can make synchronization-free TDOA a practical alternative to synchronous TOA for the acoustic localization of autonomous underwater vehicles. Full article

(This article belongs to the Section Unmanned Surface and Underwater Drones)

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34 pages, 9079 KB

Open AccessArticle

A Dynamic Multi-Priority Unmanned Aerial Vehicle Assignment Algorithm Integrating an Improved Discrete Particle Swarm Optimization and Greedy Strategy

by Mei You, Huihui Xu, Zhangsong Shi, Xiaopeng Bao, Chengfei Wang and Hao Wu

Drones 2026, 10(7), 550; https://doi.org/10.3390/drones10070550 - 20 Jul 2026

Viewed by 349

Abstract

To address the fundamental trade-off between real-time responsiveness to high-priority missions and long-term overall economic efficiency of the system in multi-UAV dynamic task assignment, we propose a hybrid intelligent scheduling algorithm abbreviated as DPSO-Greedy. The algorithm performs periodic global batch optimization for regular [...] Read more.

To address the fundamental trade-off between real-time responsiveness to high-priority missions and long-term overall economic efficiency of the system in multi-UAV dynamic task assignment, we propose a hybrid intelligent scheduling algorithm abbreviated as DPSO-Greedy. The algorithm performs periodic global batch optimization for regular orders using an improved discrete particle swarm optimization (DPSO) method, and realizes instantaneous allocation of emergency orders via an adaptive multi-factor Greedy strategy, thus enabling efficient collaborative processing of differentiated tasks. Targeting the trade-off between real-time response and long-term system efficiency, this paper proposes a hybrid DPSO-Greedy algorithm with decoupled task scheduling mechanisms. Comparative simulation results demonstrate that compared with mainstream metaheuristic algorithms (Greedy, SSA, GWO and RHS), the proposed method reduces the average response time of emergency orders by 33.2–68.2%, achieves an emergency order completion rate exceeding 90%, and improves system load balancing performance by 24–35% in dynamic scenarios characterized by burst and tidal demands. This study provides a promising solution for dynamic UAV assignment problems and offers valuable insights for a broader range of real-time resource collaborative decision-making applications. Full article

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27 pages, 2113 KB

Open AccessArticle

A Comparative Study of Machine Learning and Deep Learning Models for State of Charge and Remaining Useful Life Estimation on a Rotary-Wing UAV Battery

by Mehmet Konar, Seda Arık Hatipoğlu, İsmail Erol, Ömer Çam, Sümeyra Tuna and Mustafa Fenerci

Drones 2026, 10(7), 549; https://doi.org/10.3390/drones10070549 - 18 Jul 2026

Viewed by 405

Abstract

Battery state estimation is the main safety constraint for electric rotary-wing unmanned aerial vehicles (UAVs): mission decisions depend on both the instantaneous State of Charge (SOC) and the Remaining Useful Life (RUL). The present study compares seven machine learning and deep learning models [...] Read more.

Battery state estimation is the main safety constraint for electric rotary-wing unmanned aerial vehicles (UAVs): mission decisions depend on both the instantaneous State of Charge (SOC) and the Remaining Useful Life (RUL). The present study compares seven machine learning and deep learning models (LR, SVM, k-NN, GBT, EL, LSTM, and a simplified RWKV) on real flight data from a rotary-wing helicopter testbed with a Pixhawk autopilot and an NVIDIA Jetson Nano mission computer. The dataset has 1310 samples (∼262 s) of nine on-board sensor signals. Mission-based RUL is defined as the projected time until SOC reaches a

20 %

safe-landing threshold. All models use an 80/20 random split, five regression metrics (RMSE, MAE,

R^{2}

, MSE, PRMSE), and five random seeds. GBT wins on SOC with

R^{2} = 0.9943 \pm 0.0014

, MAE

= 0.25 %

, and

3.8 μ

s per-sample inference on a workstation CPU; this latency leaves headroom for on-board mission planning. Battery temperature and voltage together carry over

90 %

of the predictive signal. GBT wins again on RUL (

R^{2} = 0.596 \pm 0.042

, MAE

= 583

s). The same ordering (tree ensemble ≻ recurrent ≻ linear) holds for both tasks; the remaining RUL gap reflects the single-flight dataset. The SOC labels originate from the on-board autopilot’s Coulomb-counting-based fuel-gauge estimator, so the SOC numbers should be read as a reproduction of that on-board trace at sub-microsecond inference latency rather than as independent accuracy; the calibration-free Coulomb-counting baseline reaches a marginally higher

R^{2}

(

0.9950

, MAE

= 0.35 %

) on the same task. Full article

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

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51 pages, 1455 KB

Open AccessReview

Graph Neural Network-Enabled Intelligence for Unmanned Aerial Vehicle Systems: A Comprehensive Review

by Rinkuben Patel and Areej Salaymeh

Drones 2026, 10(7), 548; https://doi.org/10.3390/drones10070548 - 18 Jul 2026

Viewed by 450

Abstract

Coordinating multiple unmanned aerial vehicles (UAVs) at scale remains challenging through centralized control or fixed rule sets, particularly when vehicles must operate under intermittent communication links, incomplete observability, and constrained onboard computational resources. Graph Neural Networks (GNNs) have emerged as a promising framework [...] Read more.

Coordinating multiple unmanned aerial vehicles (UAVs) at scale remains challenging through centralized control or fixed rule sets, particularly when vehicles must operate under intermittent communication links, incomplete observability, and constrained onboard computational resources. Graph Neural Networks (GNNs) have emerged as a promising framework for addressing these challenges; however, existing surveys do not systematically relate GNN architectural decisions to the operational constraints imposed by UAV platforms during deployment. This survey reviews 196 scholarly studies published between 1987 and 2026 to develop such a framework. A GNN architecture and deployment taxonomy is organized into six major categories—Convolutional, Attentional, Sampling-Based, Spatio-Temporal, Distributed, and Resource-Efficient—each examined through dedicated architectural subsections and evaluated in the context of UAV system constraints. Four primary application domains are examined: multi-UAV trajectory planning, cooperative target tracking, communication-aware network optimization in Flying Ad Hoc Network (FANET) environments, and spatio-temporal airspace traffic prediction. Within these domains, the analysis highlights how architectural choices influence scalability, adaptability to dynamic conditions, and computational efficiency. Several deployment challenges consistently emerge, including maintaining tractable inference as swarm size increases, adapting graph representations under high mobility, and operating within the limitations of onboard computational resources. Based on these findings, a set of architecture-selection guidelines is derived to support deployment under varying operational conditions. Emerging research directions are also discussed, particularly the integration of GNNs with reinforcement learning, federated edge computing, and next-generation wireless communication systems. Overall, this survey bridges the gap between methodological development and practical deployment, providing a structured foundation for evaluating GNN suitability in real-world multi-UAV environments. Full article

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24 pages, 7071 KB

Open AccessArticle

AdaRisk-Agent: LLM-Orchestrated Adaptive Risk Calibration for Cost-Sensitive Active Learning in UAV Weed Detection

by Ali Güneş

Drones 2026, 10(7), 547; https://doi.org/10.3390/drones10070547 - 17 Jul 2026

Viewed by 253

Abstract

UAV-based weed detection in precision agriculture is constrained by asymmetric error costs: a missed weed patch causes herbicide under-treatment and yield loss, whereas a false alarm only prompts an unnecessary spot treatment. Cost-sensitive active learning (cAL) addresses this through an asymmetric misclassification penalty [...] Read more.

UAV-based weed detection in precision agriculture is constrained by asymmetric error costs: a missed weed patch causes herbicide under-treatment and yield loss, whereas a false alarm only prompts an unnecessary spot treatment. Cost-sensitive active learning (cAL) addresses this through an asymmetric misclassification penalty

r^{+}

, but the optimal value is scene-dependent and cannot be determined without domain expertise or advance knowledge of scene difficulty—a fundamental barrier to autonomous UAV monitoring workflows. We propose AdaRisk-Agent, the first LLM-orchestrated framework for adaptive

r^{+}

calibration in cAL-based UAV weed detection. We validate the framework on four UAV multispectral scenes from two public datasets—WeedsGalore (Germany, five-band maize) and WeedyRice (Vietnam, four-band paddy)—spanning two crop types, two sensor configurations, and weed prevalence from 3.1% to 30.5%. Adaptive calibration reduces the false-negative rate (FNR) by up to

80 %

relative to symmetric-cost baselines across all scenes. The deterministic surrogate (AdaRisk-Rule) surpasses the fixed-policy oracle (cAL

r^{+} = 7

) on two of four scenes without advance scene knowledge, achieving a

50 %

FNR reduction on the most spectrally challenging scene. A context-feature ablation confirms that budget urgency is the primary calibration signal and that test-set-independent deployment is feasible. Each calibration decision is accompanied by a natural-language justification, enabling auditable deployment in operational precision agriculture workflows. Future work will extend AdaRisk-Agent to multi-class weed species detection and multi-scene meta-learning for compact offline surrogate policies. Full article

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25 pages, 6504 KB

Open AccessArticle

Vision-Based Multi-View Cooperative Perception for UAV Swarms in GNSS-Denied Transportation Hub Reconnaissance

by Zhi Liu, Yong Xian, Shaopeng Li, Ming Wang and Liying Qian

Drones 2026, 10(7), 546; https://doi.org/10.3390/drones10070546 - 17 Jul 2026

Viewed by 396

Abstract

Rapid preliminary reconnaissance of Critical Transportation Hubs (CTHs) by UAV swarms is vital during post-disaster rescue operations. However, limited camera fields of view, large heading variances, and GNSS multipath errors near massive steel-concrete structures complicate multi-view cooperative perception. This paper introduces a discrete, [...] Read more.

Rapid preliminary reconnaissance of Critical Transportation Hubs (CTHs) by UAV swarms is vital during post-disaster rescue operations. However, limited camera fields of view, large heading variances, and GNSS multipath errors near massive steel-concrete structures complicate multi-view cooperative perception. This paper introduces a discrete, vision-based cooperative perception framework utilizing a decentralized anchor-wingman architecture. The pipeline integrates a Prob-IoU-optimized YOLO26m-OBB detector to extract oriented infrastructure footprints. To handle severe rotational discrepancies without IMU priors, a global scene registration cascade—combining SuperPoint and an Optimal Transport-driven LightGlue—is employed to establish robust geometric correspondences. Furthermore, a Projected Polygon Intersection over Union (Proj-IoU) mechanism, coupled with an RMSE-weighted spatial fusion strategy, dynamically associates and deduplicates overlapping targets across distributed views. Experimental results indicate that the framework achieves a low pixel-level RMSE of 2.12 pixels on the source domain and maintains a highly stable 2.36 pixels during zero-shot cross-domain testing (SUES-200 dataset), successfully resolving extreme heading variances up to 270°. The Proj-IoU mechanism resolves multi-source redundancies—collapsing overlapping projections by over 50%—bounding the localization error to approximately 1.06 m. Operating at 6.7 FPS on edge hardware via low-bandwidth tensor transmission, this system provides a rigorous geometric foundation for autonomous swarms, enabling downstream collision-free trajectory planning and Multi-Target Task Allocation (MTTA) in GNSS-denied environments. Full article

(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology: 2nd Edition)

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35 pages, 3735 KB

Open AccessArticle

Real-Time Adaptive Control for Quadrotor UAV Trajectory Tracking: Hardware-in-the-Loop Validation and Performance Evaluation

by Mohamed Fawzy El-Khatib, M. Abdelfattah, Mohamed M. El-Sotouhy, S. Shaaban and A. Abdellatif

Drones 2026, 10(7), 545; https://doi.org/10.3390/drones10070545 - 16 Jul 2026

Viewed by 440

Abstract

Accurate trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) remains a very challenging problem because of their inherent nonlinear, strongly coupled and underactuated dynamics. In order to overcome these limitations, a real-time Model Reference Adaptive Control (MRAC) strategy is proposed in this paper [...] Read more.

Accurate trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) remains a very challenging problem because of their inherent nonlinear, strongly coupled and underactuated dynamics. In order to overcome these limitations, a real-time Model Reference Adaptive Control (MRAC) strategy is proposed in this paper for better tracking performance in the presence of parametric uncertainties and external disturbances. The controller is cascaded, and adaptive laws based on Lyapunov stability theory are used to control the translational and rotational motions separately and guarantee closed-loop stability. The proposed approach is benchmarked against a tuned Particle Swarm Optimisation (PSO) PID controller under the same operating conditions to evaluate its efficacy. The validation is performed via extensive numerical simulations and real-time Hardware-in-the-Loop (HIL) experiments on an OPAL-RT platform, confirming enhanced disturbance rejection and transient response in the studied deterministic HIL conditions. The results show that the MRAC controller converges faster and has higher tracking accuracy than the PSO-based PID controller. Settling times are reduced from 9–12 s to 5–7 s with negligible steady-state error in setpoint tracking tests. The tracking errors for the multi-axis trajectory-tracking experiments, including the square and three-dimensional trajectories, are kept within 0.1–0.3 m; larger tracking deviations are observed with the benchmark controller. The quantitative performance evaluation demonstrates approximately 60–70% reduction in RMSE together with lower MAE, IAE, and ITAE values compared with the optimised PSO-based PID controller. Also, disturbance experiments under 1 N external force demonstrate the improved disturbance-rejection performance of the adaptive controller with performance degradation of about 25–30% compared to 38–45% for the PSO-based PID controller. The overall results obtained under deterministic real-time HIL conditions indicate that the proposed MRAC strategy provides improved trajectory-tracking performance compared to the benchmark PSO-based PID controller. Further statistical validation and physical flight experiments will be considered in future works. Full article

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38 pages, 28207 KB

Open AccessArticle

QoS-Aware Deployment Optimization for Capsule Airport–UAV Emergency Communication Networks

by Chaofeng Wang, Longfei Zhang, Jie Luo and Shengming Dai

Drones 2026, 10(7), 544; https://doi.org/10.3390/drones10070544 - 16 Jul 2026

Viewed by 278

Abstract

When natural disasters strike, the destruction of terrestrial communication infrastructure creates urgent demands for emergency networks. Efficient UAV deployment in capsule airport–UAV hierarchical networks has emerged as a critical challenge due to limited aerial resources and stringent quality-of-service requirements. This paper develops a [...] Read more.

When natural disasters strike, the destruction of terrestrial communication infrastructure creates urgent demands for emergency networks. Efficient UAV deployment in capsule airport–UAV hierarchical networks has emerged as a critical challenge due to limited aerial resources and stringent quality-of-service requirements. This paper develops a QoS-aware joint optimization model for UAV deployment, integrating air-to-ground (A2G) channel modeling with resource allocation, where upper-level position optimization is coordinated with lower-level frequency allocation and power control through a hierarchical decomposition strategy. The proposed QoS-TLK-VNS-K algorithm combines graph coloring for interference mitigation with iterative power control for SINR guarantee. Empirical evaluation using multi-scenario simulations demonstrates that the proposed approach significantly outperforms the traditional distance-based coverage method. Statistical validation over 30 independent runs demonstrates significant improvements in QoS satisfaction (

+ 23.8 %

,

p < 0.001

), average SINR (

+ 104.0 %

,

p < 0.001

), minimum user rate (

+ 194.9 %

,

p < 0.001

), and Jain’s fairness index (

+ 16.2 %

,

p < 0.001

) compared to the distance-based baseline. These results demonstrate that the framework effectively addresses the trade-off between interference suppression and network connectivity in multi-UAV emergency communication systems. Full article

(This article belongs to the Section Drone Communications)

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42 pages, 4351 KB

Open AccessReview

A Review of Micro Gas Engines for UAV Propulsion: Fundamentals and Emerging Technologies

by Emilia Georgiana Prisăcariu, Raluca Andreea Roșu, Oana Dumitrescu and Romeo Robert Ciobanu

Drones 2026, 10(7), 543; https://doi.org/10.3390/drones10070543 - 16 Jul 2026

Cited by 1 | Viewed by 740

Abstract

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its [...] Read more.

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its limited energy density restricts operational range and mission flexibility. As a result, micro gas engines have emerged as a viable alternative for applications requiring high power-to-weight ratios and sustained high-speed operation. This review examines the fundamentals, scaling effects, and classification of micro gas turbine propulsion systems used in UAV applications, with emphasis on micro turbojets and related hybrid configurations. The paper discusses the thermodynamic principles governing micro gas engines and analyzes the aerodynamic, thermal, and combustion challenges associated with miniaturization, including low Reynolds number effects, tip leakage losses, thermal management limitations, and combustion instability. Furthermore, the study reviews the operational characteristics and mission suitability of different propulsion architectures for reconnaissance UAVs, high-speed UAVs, including reconnaissance and loitering platforms, target drones, and hybrid-electric aerial platforms. Recent developments involving additive manufacturing, advanced control systems, recuperated cycles, and hybrid-electric integration are also evaluated as enabling technologies for next-generation UAV propulsion. The findings demonstrate that although micro gas turbines continue to face important efficiency and manufacturing challenges at reduced scales, they remain essential for mission profiles that exceed the capabilities of purely electric propulsion systems. Full article

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26 pages, 22276 KB

Open AccessArticle

Effects of Terrain Slope and Flight Patterns on Downwash Airflow and Droplet Deposition of UASS Spraying in Hilly Orchards

by Ziqi Geng, Haixin Tian, Ye Jin and Jianli Song

Drones 2026, 10(7), 542; https://doi.org/10.3390/drones10070542 - 16 Jul 2026

Viewed by 252

Abstract

The application of unmanned aerial spraying systems (UASS) in hilly orchards is challenged by terrain-induced airflow variability, which affects droplet transport and deposition. This study investigated the effects of terrain slope and flight patterns on rotor downwash airflow and droplet deposition using airflow [...] Read more.

The application of unmanned aerial spraying systems (UASS) in hilly orchards is challenged by terrain-induced airflow variability, which affects droplet transport and deposition. This study investigated the effects of terrain slope and flight patterns on rotor downwash airflow and droplet deposition using airflow measurements, computational fluid dynamics (CFD) simulations, and field experiments. Adjustable slope platforms (0°, 10°, 20°, and 30°) were used to characterize airflow behavior, while droplet deposition was evaluated under flat, uphill, downhill, and contour-parallel flight conditions, both outside and within citrus canopies. Results showed that increasing slope transformed the downwash airflow from an axisymmetric structure to a downslope-biased asymmetric pattern. Flight patterns significantly influenced deposition distribution. Uphill flight enhanced deposition in upslope and upper-canopy regions, whereas downhill flight increased deposition in rear and lower-canopy regions due to stronger recirculation. During contour-parallel flight, airflow shifted downslope, resulting in higher deposition on the downslope side of the canopy. Under the experimental conditions investigated in this study, the effective spray swath width during single-flight-line operations perpendicular to the contour lines (uphill and downhill flights) was approximately equivalent to the width of one individual tree canopy, whereas contour-parallel flight resulted in a narrower effective spray swath width due to terrain-induced airflow redistribution. An upslope route offset of 0.3–0.5 m improved droplet deposition uniformity between the upslope and downslope canopy regions. These findings provide guidance for optimizing UASS spraying strategies by adjusting flight trajectories, route offsets, and operational parameters according to terrain slope and canopy position. Full article

(This article belongs to the Section Drones in Agriculture and Forestry)

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37 pages, 23095 KB

Open AccessArticle

Distributed Real-Time Trajectory Planning for Multiple UAVs in Complex Unknown Environments

by Yang Zhao, Mingying Huo, Naiming Qi, Liguang Wang, Zongquan Xia, Bo Qi and Ge Yang

Drones 2026, 10(7), 541; https://doi.org/10.3390/drones10070541 - 16 Jul 2026

Viewed by 316

Abstract

Challenges in trajectory planning are encountered by fixed-wing unmanned aerial vehicle (UAV) swarms operating in environments with unknown obstacles. In this study, a distributed real-time trajectory-planning method that integrates a distributed model predictive control (DMPC) framework with an adaptive Gaussian collocation strategy (DA-GCMPC) [...] Read more.

Challenges in trajectory planning are encountered by fixed-wing unmanned aerial vehicle (UAV) swarms operating in environments with unknown obstacles. In this study, a distributed real-time trajectory-planning method that integrates a distributed model predictive control (DMPC) framework with an adaptive Gaussian collocation strategy (DA-GCMPC) was developed. This method leverages a distributed iterative computational framework based on DMPC to reformulate trajectory planning as an optimal control problem. To address the fixed-resolution limitation of conventional distributed MPC formulations, a complexity-aware adaptive collocation mechanism is introduced. The novelty of the method lies in adapting the collocation transcription resolution of each local MPC problem according to the instantaneous planning complexity. This mechanism selects the collocation type online according to maneuvering demand, obstacle density risk, and neighboring-UAV interaction risk, enabling the planner to balance real-time computation and constraint-handling capability under limited perception. We decomposed the UAV energy consumption and formulated the total energy consumption of the swarm as the objective function. An optimal control sequence was derived using the Gaussian collocation method by integrating obstacle avoidance constraints for fixed-wing UAVs and environmental limitations. Comparative simulations against the implemented fixed-discretization interior-point and SQP baselines showed that the proposed DA-GCMPC method achieved lower computation time and better trajectory quality metrics under the tested simulation settings, with average per-step computation times below 80 ms. In addition, an eight-UAV semi-physical hardware-in-the-loop validation was conducted to verify the real-time executability of the proposed method in a closed-loop flight control system. Full article

(This article belongs to the Topic Advanced Methods in Unmanned Aerial Vehicle Control, Navigation, and Safety)

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23 pages, 1305 KB

Open AccessArticle

A Probabilistic Flow Framework for Decentralized Cooperative Active Area Defense in Swarm-on-Swarm Interceptions

by Tong Jiang, Xuechen Gu, Jianchuan Ye, Zengzhen Mi and Tao Jiang

Drones 2026, 10(7), 540; https://doi.org/10.3390/drones10070540 - 16 Jul 2026

Viewed by 436

Abstract

Active area protection against unauthorized UAV swarms requires coordinated target assignment strategies that account for both low-level physical capabilities and the stochastic, consumptive nature of physical interceptions. This paper presents a decentralized target assignment framework based on probabilistic flow optimization. By utilizing an [...] Read more.

Active area protection against unauthorized UAV swarms requires coordinated target assignment strategies that account for both low-level physical capabilities and the stochastic, consumptive nature of physical interceptions. This paper presents a decentralized target assignment framework based on probabilistic flow optimization. By utilizing an aerodynamics-aware flight model, we derive a probabilistic prior to capture the geometric dependency of terminal interception success under high-velocity maneuvers. Modeling the defense process as a probabilistic consumption flow couples initial tactical assignments with conditional transition flows, allowing surviving defensive assets to be proactively redistributed to secondary unauthorized intrusions. To resolve this problem under practical communication and sensing constraints, we develop the Distributed Flow-regularized Market-based Consensus (DFMC) algorithm. The proposed algorithm decomposes the global optimization into localized subproblems and employs a water-filling projection to plan secondary paths. Simulation results demonstrate that the proposed framework yields improved interception rates and better spatial resource dispersion compared to conventional auction-based baselines, while maintaining stable scalability in dense interception scenarios. Full article

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27 pages, 718 KB

Open AccessArticle

Efficient Multi-UAV Path Planning Using Locally Refinable Channel Graph in Cluttered Environments

by Haoyu Tian, Linghao Li, Guanghui Sun and Weiran Yao

Drones 2026, 10(7), 539; https://doi.org/10.3390/drones10070539 - 15 Jul 2026

Viewed by 309

Abstract

In multi-unmanned-aerial-vehicle (UAV) collaborative task scenarios, congestion in cluttered environments significantly hinder operational efficiency, leading to the multi-UAV path planning problem. To reduce the scale of the search space while ensuring the quality of the planned paths, this paper proposes a locally refinable [...] Read more.

In multi-unmanned-aerial-vehicle (UAV) collaborative task scenarios, congestion in cluttered environments significantly hinder operational efficiency, leading to the multi-UAV path planning problem. To reduce the scale of the search space while ensuring the quality of the planned paths, this paper proposes a locally refinable channel graph (LRCG) model to describe the accessible region of the occupancy grid maps, aiming to address the issue of large search spaces adversely affecting planning efficiency. As the basic element of the LRCG, the channel edge represents the local accessible region in the form of an intersecting circle sequence, and can be expanded into multiple collision-free sub-paths in a targeted manner. An LRCG hybrid conflict-based search (LRCG-HCBS) algorithm is proposed to refine the LRCG oriented to conflicts and plan a set of initial collision-free paths. Further, this paper designs a post-processing optimization algorithm for a single path, a constrained dynamic programming search (CDPS) algorithm, which is also employed for the preprocessing refinement of the LRCG. Simulation results show that, compared with existing algorithms, the LRCG-based planning algorithm proposed in this paper demonstrates higher computational efficiency. Full article

(This article belongs to the Special Issue Mission Planning, Perception and Control for Drones in Wide-Area Operations: 2nd Edition)

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41 pages, 4315 KB

Open AccessArticle

Flight Performance Analysis of Industrial-Grade Logistics Slung-Load Unmanned Aerial Vehicles and Research on Flight Operations for Improving Slung-Load System Stability

by Wen Zhang, Rui Wang, Peng Jing, Qinsheng Bi, Yuan Wang and Qing Liu

Drones 2026, 10(7), 538; https://doi.org/10.3390/drones10070538 - 15 Jul 2026

Viewed by 304

Abstract

Industrial-grade suspended-load logistics drones have now been widely used in commercial activities. The swing of their suspended loads has always been a major challenge in flight control. At present, most related studies take small quadrotor drones as the research object and employ approaches [...] Read more.

Industrial-grade suspended-load logistics drones have now been widely used in commercial activities. The swing of their suspended loads has always been a major challenge in flight control. At present, most related studies take small quadrotor drones as the research object and employ approaches such as designing novel flight control systems, followed by analysis through theoretical and simulation-based validation. However, research on industrial-grade drones remains lacking, and the results of such studies cannot be quickly applied in engineering practice. Therefore, this paper proposes a set of flight operation guidelines for the existing flight control system of industrial-grade logistics drones. This study conducts flight experiments on commonly used industrial-grade logistics drones to investigate slung-load stability under varying built-in parameters, velocity profiles, and payload weights. The swing parameters are measured and analyzed. The results show that by adjusting relevant parameters, the swing angle and settling time are significantly improved. Finally, based on the experimental analysis results, a set of flight strategies is proposed, which can quickly improve the stability of the slung load of industrial-grade logistics drones using the existing conditions in engineering applications. Full article

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21 pages, 5044 KB

Open AccessArticle

Risk-Aware Cooperative Planning for Multiple UAVs in Non-Stationary Maritime Missions via a Scenario-Switching-Aware LinUCB Hyper-Heuristic

by Jian Wu, Shengchang Liu, Wenxi Ni, Junqi Wang and Daming Zhou

Drones 2026, 10(7), 537; https://doi.org/10.3390/drones10070537 - 15 Jul 2026

Viewed by 353

Abstract

Maritime unmanned aerial vehicle (UAV) missions such as ship inspection, search and rescue, environmental monitoring, and emergency response often involve multi-wave task releases, time-sensitive deadlines, constrained support vessel positions, and spatially heterogeneous risk. These factors couple task allocation with path planning and make [...] Read more.

Maritime unmanned aerial vehicle (UAV) missions such as ship inspection, search and rescue, environmental monitoring, and emergency response often involve multi-wave task releases, time-sensitive deadlines, constrained support vessel positions, and spatially heterogeneous risk. These factors couple task allocation with path planning and make fixed dispatching rules fragile under changing mission profiles. This study develops a hierarchical cooperative planning framework for multiple UAVs over a maritime risk field. A risk-cost A* layer generates feasible routes from support vessels to task points and estimates path length, risk exposure, and sortie duration. A rolling scheduler constructs feasible UAV task candidates, while a scenario-switching-aware LinUCB hyper-heuristic selects online among deadline-first, distance-first, risk-aware, and endurance-balancing rules. A forgetting-update, one-step look-ahead, scenario memory, and lightweight switching detection are used to improve adaptation to mission profile changes. Simulations on a 28 × 40 maritime grid with two support vessels, six UAVs, 40 tasks, and nine release waves show that the proposed framework achieves the highest average effective reward (370.18), the lowest average value regret (0.61), and a best reward ratio of 0.46 over 24 random scenarios. The results should be interpreted as evidence from an idealized simulation benchmark. The main benefit is improved reward robustness under non-stationary and high-risk profiles, rather than uniform gains across all metrics or direct field-deployment validation. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Drones Systems for Marine Engineering Applications)

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37 pages, 7926 KB

Open AccessArticle

Three-Dimensional Dynamic UAV Threat Assessment Using Approach Directionality and Historical-Trend Correction for Multi-Asset Protection

by Ze Zhang, Lin Zhang, Kai Huang, Zhaoxuan Jia, Min Wu, Lin Cui and Mingang Zhang

Drones 2026, 10(7), 536; https://doi.org/10.3390/drones10070536 - 14 Jul 2026

Viewed by 299

Abstract

In multi-asset UAV safety monitoring, dynamic threat assessment is challenged by delayed recognition of changes in the potentially threatened asset and unstable threat rankings under noisy observations. This paper proposes an interpretable three-dimensional dynamic threat assessment method integrating approach directionality, historical trend correction, [...] Read more.

In multi-asset UAV safety monitoring, dynamic threat assessment is challenged by delayed recognition of changes in the potentially threatened asset and unstable threat rankings under noisy observations. This paper proposes an interpretable three-dimensional dynamic threat assessment method integrating approach directionality, historical trend correction, and adaptive exponential moving average smoothing. Each UAV-protected asset pair is treated as an assessment unit to construct a many-to-many threat matrix. A basic threat score is first derived from UAV type, three-dimensional closing velocity, distance, altitude difference, and vertical approach motion. Approach directionality is then characterized using the geometric alignment between the UAV velocity and the bearing to each protected asset, with optional attitude and reachability information. The temporal trend of directionality is estimated from historical observations to capture persistent changes in approach tendency, while adaptive smoothing balances responsiveness to genuine threat transitions against suppression of noise-induced fluctuations. Comprehensive simulation experiments, including three-dimensional discrimination, Monte Carlo evaluation, observation noise, latency, missed detections, clutter, difficult motion conditions, and ablation studies, demonstrate that the proposed method provides accurate and timely threat identification while substantially improving score and ranking stability. The resulting framework offers an interpretable and computationally efficient basis for multi-UAV threat prioritization and safety-oriented situational awareness. Full article

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32 pages, 9798 KB

Open AccessArticle

uVGS-2: The Micro Video Guidance Sensor: A 6-DoF Robust Pose Estimator for Autonomous Proximity Maneuvers in Drones, Spacecraft and Mobile Robot Navigation

by Hector Gutierrez, Jose Cornejo and Ivan Bertaska

Drones 2026, 10(7), 535; https://doi.org/10.3390/drones10070535 - 14 Jul 2026

Cited by 1 | Viewed by 530

Abstract

This paper presents the Micro Video Guidance Sensor Version 2 (uVGS-2), a ROS-based vision navigation framework for real-time six-degrees-of-freedom pose estimation in drones, spacecraft, and autonomous robotic platforms operating in GNSS-denied environments. The system evolves from the previous Smartphone Video Guidance Sensor (SVGS) [...] Read more.

This paper presents the Micro Video Guidance Sensor Version 2 (uVGS-2), a ROS-based vision navigation framework for real-time six-degrees-of-freedom pose estimation in drones, spacecraft, and autonomous robotic platforms operating in GNSS-denied environments. The system evolves from the previous Smartphone Video Guidance Sensor (SVGS) architecture through a modular C++ implementation, including advanced image preprocessing, deterministic blob sorting, and an optimized perspective-4-point solver using a Lie-algebra-based analytical Jacobian formulation. The proposed architecture achieves computationally efficient photogrammetric state estimation using onboard camera and processor resources, enabling deployment in resource-constrained systems. Experimental validation was conducted in NASA’s Astrobee free-flying robot, both at the International Space Station (ISS), for SVGS, and by ground testing through real-time sensor-fusion with Astrobee’s graph-based localizer (Astroloc), for uVGS-2. Results demonstrate robust centimeter-level accuracy in relative position and attitude estimation under illumination disturbances, partial occlusions, and intermittent loss of line-of-sight. The framework can be used in robotic platforms and autonomous UAV operations, including precision landing, formation flight, and cooperative navigation in environments where GNSS signals are unavailable or intermittent. Full article

(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)

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47 pages, 9649 KB

Open AccessArticle

A Hybrid A*–APF Path Planning Framework with Payload Stability Constraints for Cargo UAVs in Continuous Heterogeneous Environments

by Yong Wang, Dayuan Zhang, Xi Vincent Wang and Lihui Wang

Drones 2026, 10(7), 534; https://doi.org/10.3390/drones10070534 - 14 Jul 2026

Viewed by 338

Abstract

Path planning for cargo unmanned aerial vehicles (UAVs) in continuous indoor–outdoor heterogeneous environments poses a critical challenge: promoting payload stability under sharp turns and abrupt altitude variations while maintaining navigational efficiency. To address this issue, this paper proposes a hybrid A*–APF path planning [...] Read more.

Path planning for cargo unmanned aerial vehicles (UAVs) in continuous indoor–outdoor heterogeneous environments poses a critical challenge: promoting payload stability under sharp turns and abrupt altitude variations while maintaining navigational efficiency. To address this issue, this paper proposes a hybrid A*–APF path planning framework that embeds trajectory smoothness optimization directly into the planning process rather than treating it as a post-processing step. An improved A* algorithm is developed by incorporating a trajectory smoothness term into its cost function to penalize sharp turns during global path generation. The resulting path is further refined using an enhanced artificial potential field (APF) method with virtual target points and multi-field force synthesis to mitigate local minima. In addition, the Ramer–Douglas–Peucker algorithm is employed to remove redundant waypoints, and a trajectory generation module based on B-spline interpolation and minimum snap optimization is introduced to produce smooth and dynamically feasible trajectories. Numerical simulation results demonstrate that, in indoor warehouse environments, the proposed method reduces the average turning angle by 88.4% (to 23.1°) compared with the standard A* algorithm while maintaining a comparable path length of 135.11 m. In large-scale outdoor urban scenarios, it achieves a path smoothness of 0.0124 with an average turning angle of 40.0°, substantially outperforming the Genetic Algorithm (104.6°) and Particle Swarm Optimization (83.5°) on turning angle while delivering competitive computation times of 0.52–1.51 s. An ablation study confirms that the improved A* and enhanced APF components each contribute independently to turning angle reduction and local minima avoidance, respectively, and that their integration yields the optimal balance across all metrics. These results indicate the proposed framework’s effectiveness for UAV-based last-mile delivery in scenarios requiring seamless indoor–outdoor transitions under payload stability constraints. Full article

(This article belongs to the Section Artificial Intelligence in Drones (AID))

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24 pages, 1514 KB

Open AccessArticle

Mamba-2-Based Continuous-Discrete Extended Kalman Filter for Passive UAV Bearings-Only Tracking with Uncertain Measurement Noise

by Hao Wu, Guoxu Zeng, Ali Mehmood, Yijie Zhao, Chaoqi Li and Mingbo Yang

Drones 2026, 10(7), 533; https://doi.org/10.3390/drones10070533 - 14 Jul 2026

Viewed by 353

Abstract

Using UAVs for bearings-only tracking (BOT) of moving targets is a key challenging issue due to weak observability and time-varying measurement noises. To overcome these limitations, we introduce a hybrid framework that integrates a square-root continuous-discrete extended Kalman filter (MSCDEKF) with the Mamba-2 [...] Read more.

Using UAVs for bearings-only tracking (BOT) of moving targets is a key challenging issue due to weak observability and time-varying measurement noises. To overcome these limitations, we introduce a hybrid framework that integrates a square-root continuous-discrete extended Kalman filter (MSCDEKF) with the Mamba-2 neural network. The MSCDEKF improves estimation accuracy and numerical stability under weak observability conditions by adopting continuous-time state update while ensuring numerical stability through square-root covariance implementation. Meanwhile, the state-space modeling of the Mamba-2 network provides superior continuous measurement noise prediction compared to conventional recurrent neural network approaches by explicitly capturing signal evolution dynamics. Experimental validation in constant velocity (CV) and constant turn (CT) UAV-based BOT scenarios demonstrates that our framework achieves the following: (1) at least a 34.5% improvement in state estimation accuracy (measured by average root-mean-square error, ARMSE) compared with variational Bayesian filters; (2) significant improvements in noise variance estimation; and (3) parameter-free operation with minimal manual tuning. This work establishes a different paradigm for robust UAV-based BOT in complex environments characterized by unknown and time-varying noise conditions. Full article

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47 pages, 8928 KB

Open AccessArticle

Robust Attitude Control of Fixed-Wing UAVs Under Near-Envelope Conditions Using a Hierarchical Adaptive NSGA-II

by Wanli Chen, Shijun Guo, Xinyu Li, Song Wang, Zhiqiang Wan, Xishuo Jia and Zhuoer Yao

Drones 2026, 10(7), 532; https://doi.org/10.3390/drones10070532 - 13 Jul 2026

Viewed by 296

Abstract

To address the strong aerodynamic coupling and insufficient control robustness of fixed-wing unmanned aerial vehicles (UAVs) under near-envelope conditions, this study focuses on three-axis attitude stabilization and develops a two-level control architecture consisting of an attitude/load-factor stability-augmentation loop and an angular-rate stability-augmentation loop. [...] Read more.

To address the strong aerodynamic coupling and insufficient control robustness of fixed-wing unmanned aerial vehicles (UAVs) under near-envelope conditions, this study focuses on three-axis attitude stabilization and develops a two-level control architecture consisting of an attitude/load-factor stability-augmentation loop and an angular-rate stability-augmentation loop. The outer loop adopts an attitude-angle dynamic-inversion and load-factor correction structure, whereas the inner loop employs linear active disturbance rejection control (LADRC). An improved non-dominated sorting genetic algorithm II (NSGA-II) integrating hierarchical pre-optimization and a threefold adaptive mechanism is proposed for multi-objective controller-parameter tuning. Hierarchical single-objective pre-optimization is followed by multi-objective coordinated optimization to reduce premature convergence in high-dimensional parameter optimization. A threefold adaptive mechanism comprising parameter-range adaptation, crossover and mutation probability adaptation, and objective-weight adaptation balances global exploration and local convergence. Nonlinear simulations and field tests show that hierarchical pre-optimization accounts for the main fitness reduction relative to standard NSGA-II (approximately 4.41%), whereas the adaptive mechanism lowers the post-convergence coefficient of variation from 0.0020 to 0.0006 and adds a 0.083% reduction beyond hierarchical pre-optimization alone. Under combined multi-source perturbations in aerodynamic, inertial, and actuator parameters, the optimized three-channel closed-loop controller remains stable within the tested ranges and maintains attitude-tracking performance. This study supports offline tuning under the tested near-envelope conditions. Full article

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47 pages, 23966 KB

Open AccessArticle

An Open MCU-Embedded Platform for Real-Time Onboard Vision on Resource-Constrained UAV Systems

by Bogdan Nedelcu and Adina Magda Florea

Drones 2026, 10(7), 531; https://doi.org/10.3390/drones10070531 - 13 Jul 2026

Viewed by 544

Abstract

This paper presents a lightweight MCU–EdgeTPU platform—a microcontroller unit (MCU) paired with an Edge Tensor Processing Unit (EdgeTPU) accelerator—for onboard drone-perception experiments, extended from an open-source baseline originally limited to Quarter Video Graphics Array (QVGA) single-camera operation. Rather than treating hardware, runtime, model, [...] Read more.

This paper presents a lightweight MCU–EdgeTPU platform—a microcontroller unit (MCU) paired with an Edge Tensor Processing Unit (EdgeTPU) accelerator—for onboard drone-perception experiments, extended from an open-source baseline originally limited to Quarter Video Graphics Array (QVGA) single-camera operation. Rather than treating hardware, runtime, model, and data as separate problems, they are developed as parts of the same continuous perception pipeline. The platform extends the hardware baseline toward dual 5 Mpx sensing, onboard inertial measurement unit (IMU) support, real-time embedded inference, and a high-level MicroPython control layer. In parallel, lightweight You Only Look Once (YOLO) detectors are trained and selected on a synthetic aerial-person dataset generated under the visual conditions expected by the drone camera, including target resolution, viewpoint, object scale, weather, lighting, and time-of-day variation. The resulting workflow starts from both ends: the detector must be small and quantization-stable enough for the EdgeTPU path, while the dataset must match the images that the onboard sensor is expected to observe. To evaluate the system, the full path from camera capture and image conversion to TPU transfer, model execution, and post-inference processing is analyzed. In the tested setup, the optimized single-camera pipeline runs stably with no timeouts or inference failures at about 26 detections per second with standard RGB input; because each EdgeTPU invocation is bounded by the USB transfer of the input image, feeding the camera’s native YUV420 format instead halves that transfer and raises throughput to about 40 detections per second at the same accuracy, while the selected 8-bit-integer (INT8) person detector preserves most of its 32-bit floating-point (FP32) accuracy. Detections are exposed to drone-control workflows (MAVLink/PX4 and Crazyflie) through the scriptable layer as an integration interface rather than a validated autonomy stack. The central contribution is therefore a co-designed embedded perception pipeline in which the board, runtime, detector, dataset, and even the camera pixel format are aligned around the same operating conditions. Full article

(This article belongs to the Special Issue Air–Ground Integrated Perception and Cooperative Control for UAVs and UGVs)

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49 pages, 5966 KB

Open AccessArticle

Capability-Aware Hierarchical Control with Priority-Based Lateral Recovery for Deadline-Guided Fixed-Wing UAV Formation Tracking

by Xiuyuan Feng, Hao Cheng, Hua Wang, Xudong Xie, Wei Wei, Tao Han and Siyuan Liu

Drones 2026, 10(7), 530; https://doi.org/10.3390/drones10070530 - 12 Jul 2026

Viewed by 251

Abstract

This paper studies deadline-guided formation tracking for fixed-wing unmanned aerial vehicles subject to nonholonomic coupling, input saturation, and time-varying uncertainties. The central difficulty is that the mission layer prescribes a nominal deadline-guided performance boundary, whereas the control layer may be temporarily unable to [...] Read more.

This paper studies deadline-guided formation tracking for fixed-wing unmanned aerial vehicles subject to nonholonomic coupling, input saturation, and time-varying uncertainties. The central difficulty is that the mission layer prescribes a nominal deadline-guided performance boundary, whereas the control layer may be temporarily unable to enforce it because the available maneuvering capability varies online. To address this mismatch, a capability-aware executable-boundary recovery controller, abbreviated as CAEBR, is developed. CAEBR distinguishes between a nominal deadline boundary and an executable boundary; the latter is adjusted according to online estimates of maneuvering capability and is recovered toward the nominal boundary when sufficient authority becomes available. The architecture contains three components: a bounded fast layer for executable-boundary regulation, priority-based lateral recovery, and matched residual compensation; a slow residual-aware predictive coordination layer for boundary recovery and heading-authority adjustment; and an adaptive residual-information layer for matched residual estimation and conservative residual-load indication. The fast-layer feedback uses bounded transformed-error terms, thereby avoiding circular boundedness arguments caused by unbounded commands under input saturation. Under admissible coordination, conservative residual-load, and saturation-compatible executability conditions, the analysis establishes forward invariance of the executable boundary and heading-support tube, boundedness of closed-loop signals, and conditional recovery of the nominal boundary. Simulations demonstrate effective deadline-guided formation tracking, capability-dependent boundary relaxation under saturated recovery, improved lateral tracking in the more demanding scenario, Aerosonde-class 6-DoF command-execution consistency, and the remaining performance limitations when the selected executable-boundary budget is insufficient to remove all exceedance. Full article

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42 pages, 3542 KB

Open AccessArticle

A Risk-Averse Two-Stage Stochastic Programming Model for Emergency UAV Task Allocation

by Shumeng Xu, Lili Wan, Jiahui Huang, Qingyang Zhang, Zhenyu Yuan and Zhan Wang

Drones 2026, 10(7), 529; https://doi.org/10.3390/drones10070529 - 12 Jul 2026

Viewed by 242

Abstract

As UAVs are increasingly used in emergency rescue, task allocation under uncertainty still faces tail delay risk. Existing studies mainly optimize expected cost and pay insufficient attention to task temporal relations and delay losses under extreme scenarios. To address this issue, this study [...] Read more.

As UAVs are increasingly used in emergency rescue, task allocation under uncertainty still faces tail delay risk. Existing studies mainly optimize expected cost and pay insufficient attention to task temporal relations and delay losses under extreme scenarios. To address this issue, this study develops a risk-averse two-stage stochastic programming model that incorporates the precedence relation between reconnaissance and delivery tasks, UAV routes, and task execution sequences into a unified decision process. The first stage determines task assignment, route selection, and visit order, while the second stage evaluates waiting, delay, and recourse costs under stochastic scenarios. A Mean-CVaR risk measure is adopted to characterize both average performance and tail risk. To solve the resulting multi-scenario risk-averse model, this study develops a problem-tailored Enhanced BD framework based on the classical Benders decomposition structure. The proposed framework integrates partial scenario embedding, heuristic warm start, and dynamic cut-pool management to strengthen early master problem information, improve feasible-route search, and control the growth of scenario-wise cuts. Numerical experiments based on a Nanjing emergency rescue instance evaluate the model and algorithm in terms of solution performance, acceleration ablation, optimized scheduling results, and parameter sensitivity. The results show that the proposed model can identify tail delay risk concentrated at a small number of demand points and downstream nodes in task chains. Across ten independent replications, Enhanced BD achieves a higher convergence success rate and lower final BD Gap than Basic BD in the medium-sized and largest tested instances. Parameter analysis shows that moderate risk aversion improves out-of-sample performance, whereas excessive risk aversion or resource allocation may reduce overall scheduling efficiency. The proposed method improves tail risk identification and solution capability for emergency UAV task allocation under time uncertainty and provides a methodological reference for risk-aware UAV emergency scheduling. Full article

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27 pages, 9654 KB

Open AccessArticle

Secure Self-Triggered Time-Varying Formation Control for Quadrotor Swarms Against Sequential Multi-Link Scaling Attacks

by Miao Zhao, Fan Gui, Hao Wu, Jianxiang Xi and Yuanshi Zheng

Drones 2026, 10(7), 528; https://doi.org/10.3390/drones10070528 - 12 Jul 2026

Viewed by 276

Abstract

This paper investigates secure self-triggered time-varying formation control for quadrotor swarm systems against sequential multi-link scaling attacks, which can be implemented in a self-triggered and fully distributed manner. Firstly, based on the outer-loop position and velocity control model of quadrotors, a fully distributed [...] Read more.

This paper investigates secure self-triggered time-varying formation control for quadrotor swarm systems against sequential multi-link scaling attacks, which can be implemented in a self-triggered and fully distributed manner. Firstly, based on the outer-loop position and velocity control model of quadrotors, a fully distributed secure time-varying formation control protocol is constructed under sequential multi-link scaling attacks with three characteristics: distributed, sequential, and scalable, and the design criteria for fully distributed secure time-varying formation control are provided. Then, combining the inner and outer-loop control principles of quadrotors, by constructing Euler angle loop controllers and angular velocity controllers, the conversion of the control input of the outer-loop position and velocity to the inner-loop attitude control is achieved, and fully distributed secure time-varying formation control algorithms for quadrotor UAV swarm systems under attacks are proposed. Finally, the effectiveness and applicability of the fully distributed secure consensus method in the formation control of quadrotor UAV swarms is verified through flight experiments using a quadrotor UAV swarm flight test platform. The research results provide a useful reference for the practical application of the fully distributed secure cooperative control theory. Full article

(This article belongs to the Section Artificial Intelligence in Drones (AID))

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23 pages, 1783 KB

Open AccessArticle

Energy-Aware Edge Vision for Event-Level Fire Detection with YOLO-Equipped UAVs

by Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Joan Garcia-Haro and Antonio-Javier Garcia-Sanchez

Drones 2026, 10(7), 527; https://doi.org/10.3390/drones10070527 - 11 Jul 2026

Viewed by 423

Abstract

Unmanned aerial vehicles (UAVs) are increasingly being used for early wildfire monitoring in remote areas, but UAV endurance is fundamentally constrained by the battery capacity. This work presents an energy-aware edge-vision framework for UAV fire detection that jointly models neural inference and wireless [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly being used for early wildfire monitoring in remote areas, but UAV endurance is fundamentally constrained by the battery capacity. This work presents an energy-aware edge-vision framework for UAV fire detection that jointly models neural inference and wireless communication and optimizes the operating point of the complete onboard pipeline. Five you only look once (YOLO)v5 scales were fine-tuned on a YOLO-formatted version of the FLAME aerial fire dataset, which was extended with multi-frame fire tracking to enable event-level evaluations. We jointly optimized the model scale, detection confidence threshold, and inference stride using theoretical and empirical estimators that balance energy consumption against the probability of detecting fire events. The results showed that compact YOLOv5 models provide the best trade-off between energy and accuracy for this UAV application: larger variants increase the inference cost without consistent recall gains on the evaluated dataset. In addition, temporal subsampling reduces the total energy approximately in proportion to the stride while preserving near-perfect event-level detection for fires of a moderate duration. The optimized configuration lowers energy consumption by up to 4.4 times with only a 0.03% reduction in recall, supporting longer-endurance UAV missions for wildfire monitoring. Full article

(This article belongs to the Section Drones in Agriculture and Forestry)

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