Drones

35 pages, 1068 KB

Open AccessEditor’s ChoiceReview

UAV-Based Remote Sensing and Artificial Intelligence for Climate-Smart Agriculture: A Systematic Review of Technologies, Analytics, and Applications in Smallholder Systems

by Andrew Manu, Jeff Dacosta Osei and Thomas Lawler

Drones 2026, 10(6), 451; https://doi.org/10.3390/drones10060451 - 9 Jun 2026

Cited by 1 | Viewed by 972

Abstract

Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a [...] Read more.

Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a PRISMA-guided systematic review of 59 peer-reviewed studies examining UAV–AI applications in agricultural systems. The synthesis categorizes platform configurations, sensor modalities, analytical architectures, geographic distribution, and data integration strategies, and evaluates their alignment with CSA objectives. Results indicate that productivity-oriented applications, including yield estimation, biomass mapping, and nutrient assessment, are the most mature, while adaptation-focused stress detection is also well established. In contrast, mitigation-oriented applications, such as carbon quantification and greenhouse gas monitoring, remain comparatively underrepresented. The analysis further reveals a growing convergence toward multimodal sensing and cross-scale data integration linking UAV observations with satellite and environmental datasets. However, substantial variability in validation approaches and dataset representativeness limits generalizability and scalability. Advancing UAV–AI contributions to CSA therefore requires methodological standardization, interoperable data governance, and strengthened institutional capacity. Collectively, the findings position UAV–AI systems as emerging components of climate-smart agricultural intelligence infrastructure rather than isolated monitoring tools. Full article

(This article belongs to the Special Issue Advances in UAV-Based Remote Sensing for Climate-Smart Agriculture)

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32 pages, 1670 KB

Open AccessEditor’s ChoiceArticle

Uncertainty-Calibrated UAV Trajectory Prediction for Beam Management in UAV-Assisted ISAC Scenarios

by Qing Cheng, Wenwen Wu and Ziwei Zhao

Drones 2026, 10(6), 434; https://doi.org/10.3390/drones10060434 - 3 Jun 2026

Viewed by 775

Abstract

Reliable beam management in Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing and Communication (ISAC) systems needs accurate trajectory prediction and a clear sense of prediction risk. Most existing methods use deterministic future positions or raw uncalibrated uncertainty. Under high mobility and uncertainty, this leads [...] Read more.

Reliable beam management in Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing and Communication (ISAC) systems needs accurate trajectory prediction and a clear sense of prediction risk. Most existing methods use deterministic future positions or raw uncalibrated uncertainty. Under high mobility and uncertainty, this leads to unreliable beam decisions. We design a control-oriented probabilistic trajectory prediction framework. It uses calibrated trajectory uncertainty as a risk signal for adaptive beam management. The framework first combines motion history and visual context to predict trajectory distributions. Split conformal calibration turns raw Gaussian uncertainty into statistically reliable risk bounds. A codebook-constrained beam management strategy adjusts beamwidth based on the calibrated spatial risk. This balances beamforming gain, coverage robustness, and switching stability. Tests on UAV data show better prediction accuracy than representative probabilistic baselines. The raw uncertainty remains under-calibrated, and conformal calibration is therefore applied to improve its reliability before beam-control decisions. Using the calibrated uncertainty for beam control improves communication coverage and cuts outages and severe misalignment in high-risk situations. Calibrated predictive uncertainty can serve as an actionable control variable for robust beam management in dynamic UAV-assisted ISAC environments. Full article

(This article belongs to the Section Drone Communications)

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29 pages, 4445 KB

Open AccessEditor’s ChoiceArticle

A Hierarchical Cooperative Interception Framework for Multi-UAV Defense Against Large-Scale Swarm Intrusions

by Lei Zuo, Ying Wang, Jialu Liu, Yu Lu and Ruiwen Gu

Drones 2026, 10(6), 418; https://doi.org/10.3390/drones10060418 - 28 May 2026

Viewed by 697

Abstract

To address the challenges of unbalanced task allocation, high inter-UAV collision risks, and lagging interception guidance in multi-UAV cooperative missions within complex urban low-altitude environments, a cooperative interception strategy integrating load-balanced allocation, k-nearest neighbor (k-NN) cooperative obstacle avoidance, and adaptive predictive guidance is [...] Read more.

To address the challenges of unbalanced task allocation, high inter-UAV collision risks, and lagging interception guidance in multi-UAV cooperative missions within complex urban low-altitude environments, a cooperative interception strategy integrating load-balanced allocation, k-nearest neighbor (k-NN) cooperative obstacle avoidance, and adaptive predictive guidance is proposed. First, a load-balanced Hungarian algorithm is developed at the task allocation layer. The integration of a multi-dimensional distance-angle threat assessment model and a nonlinear load penalty mechanism resolves the issues of resource idling and target overloading inherent in traditional one-to-one allocation, thereby achieving optimal resource configuration for saturated cooperative interception. Second, at the path planning layer, a cooperative obstacle avoidance algorithm based on k-NN nonlinear repulsion is introduced. By exclusively considering the dynamic repulsive fields of local nearest neighbors alongside scale-adaptive parameter regulation, this approach maintains safe formation spacing while reducing the computational complexity from

O (n^{2})

to

O (k) (k ≪ n)

, significantly enhancing flight robustness in dense airspaces. Finally, at the terminal guidance layer, an adaptive look-ahead guidance model incorporating motion prediction is constructed to mitigate the overshoot and lag defects associated with classical pure pursuit algorithms during the interception of highly maneuverable targets. The implementation of linear extrapolation and dynamic gain regulation facilitates a paradigm shift from “passive pursuit” to “active interception.” Simulation results demonstrate that the proposed algorithm yields substantial improvements in task allocation efficiency, collision risk mitigation, and overall success rates across red-blue UAV swarm confrontation scenarios of varying scales. These findings provide a viable cooperative defense framework against large-scale, highly maneuverable unmanned aerial vehicle (UAV) swarm intrusions. Full article

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

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29 pages, 13942 KB

Open AccessEditor’s ChoiceArticle

Hierarchical Reinforcement Learning for Large-Scale Heterogeneous UAV Mission Planning via MCTS and Transformer

by Yuan Zang, Dengwei Gao, Zeyang Yin and Caisheng Wei

Drones 2026, 10(6), 414; https://doi.org/10.3390/drones10060414 - 27 May 2026

Viewed by 959

Abstract

Post-disaster Search and Rescue (SAR) missions demand rapid coordination of Heterogeneous Unmanned Aerial Vehicle (UAV) fleets under stringent payload and flight range limitations. Traditional heuristic solvers struggle to solve the Large-Scale Heterogeneous Team Orienteering Problem (LSH-TOP) within operational time limits due to the [...] Read more.

Post-disaster Search and Rescue (SAR) missions demand rapid coordination of Heterogeneous Unmanned Aerial Vehicle (UAV) fleets under stringent payload and flight range limitations. Traditional heuristic solvers struggle to solve the Large-Scale Heterogeneous Team Orienteering Problem (LSH-TOP) within operational time limits due to the coupled complexity of task allocation and route planning. A Hierarchical Deep Reinforcement Learning framework decomposes this high-dimensional combinatorial problem into tractable sub-problems. An upper-level policy, guided by Monte Carlo Tree Search (MCTS), partitions the global target set to balance fleet workload distribution, whereas a lower-level Transformer-based model constructs near-optimal trajectories for individual agents. A Curriculum-Integrated Alternating Cooperative Training (C-ACT) protocol resolves the convergence difficulties associated with sparse feasible solutions in constrained environments. This protocol incorporates a dynamic constraint annealing strategy and a virtual agent buffer to progressively shape the solution space from relaxed to strictly constrained formulations. Experiments conducted on real-world geographic data demonstrate the proposed approach consistently outperforms all baselines across scales of 80 to 300 targets, improving over the strongest competitor by 0.63–8.51% and over conventional heuristics by up to 53.27% in objective value. Results indicate a task completion rate of 27.5% at the 300-target scale (versus 25.1% for the strongest baseline MCTS + OR) and balanced workload distribution, validating framework adaptability to complex emergency response scenarios. Full article

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

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22 pages, 2112 KB

Open AccessEditor’s ChoiceArticle

System Design and Evaluation of a Lightweight Micro-UAV for Emergency Response

by Roya Salehzadeh, Corbin Ortolan, Abhinandan Reddy Mogulla, Ahmed Khan Mohammed Zia, Samuel Stepanek, Yeen K. Lee and James A. Mynderse

Drones 2026, 10(6), 413; https://doi.org/10.3390/drones10060413 - 27 May 2026

Cited by 1 | Viewed by 701

Abstract

Firefighting and urban search operations occur in hazardous, rapidly changing environments where timely situational awareness is critical. In indoor firefighting scenarios, responders often operate in smoke-filled and structurally complex environments with limited visibility and communication. While UAVs have been widely used in wildfire [...] Read more.

Firefighting and urban search operations occur in hazardous, rapidly changing environments where timely situational awareness is critical. In indoor firefighting scenarios, responders often operate in smoke-filled and structurally complex environments with limited visibility and communication. While UAVs have been widely used in wildfire response, their deployment inside buildings remains limited due to constraints in system mass, cost, and operational complexity. This paper presents the design and preliminary validation of an attritable micro-UAV as a proof-of-concept platform for indoor search support and post-fire inspection and assessment. The platform emphasizes portability, durability, and multi-sensor integration, enabling deployment by minimally trained personnel. System requirements were derived in collaboration with the Southfield Fire Department. The finalized design achieved a total mass of 247.34 g at a cost of $2969. Experimental evaluation demonstrated reliable sensing and communication performance at the subsystem level and confirmed structural robustness through drop tests from heights up to 3 m. Endurance testing yielded a maximum flight time of 28 min, slightly below the targeted 30 min requirement. While full task-level validation in operational firefighting scenarios has not been conducted, the proposed platform establishes a foundation for future development, including system-level validation, post-fire structural assessment, and enhanced visualization interfaces for improved situational awareness in emergency response operations. Full article

(This article belongs to the Section Innovative Urban Mobility)

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28 pages, 12613 KB

Open AccessEditor’s ChoiceArticle

A2C-LLM: An Actor-Critic-Enhanced Large Language Model for UAV Swarm Multi-Target Task Allocation

by Jie Bao, Yuping Zhang, Ronghao Zhang and Peng Zhang

Drones 2026, 10(6), 398; https://doi.org/10.3390/drones10060398 - 22 May 2026

Viewed by 860

Abstract

In UAV swarm adversarial applications, multi-agent task allocation requires high-level reasoning and accurate decision-making in dynamic environments. Although large language models (LLMs) have shown strong performance in zero-shot reasoning, they cannot generate optimal allocation strategies without environmental objective feedback. To address this problem, [...] Read more.

In UAV swarm adversarial applications, multi-agent task allocation requires high-level reasoning and accurate decision-making in dynamic environments. Although large language models (LLMs) have shown strong performance in zero-shot reasoning, they cannot generate optimal allocation strategies without environmental objective feedback. To address this problem, we present A2C-LLM, an Actor-Critic-enhanced large language model for adversarial UAV swarm task allocation. Unlike traditional methods that adopt sequential tracking, we adopt a single-step decision process for macro allocation to improve the efficiency of immediate allocation. In A2C-LLM, the LLM serves as the Actor network to understand the adversarial environment and generate coordination strategies, while a lightweight neural network serves as the Critic network to estimate expected rewards and calculate TD advantage for fine-tuning. Experimental results demonstrate that A2C-LLM significantly outperforms traditional heuristic algorithms and pure LLM baselines in task completion rate and robustness across various adversarial scenarios, showcasing the potential of integrating reinforcement learning feedback with foundation models for autonomous aerial systems. Full article

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

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31 pages, 9128 KB

Open AccessEditor’s ChoiceArticle

Surround and Tracking: An Innovative Multi-UAV Collaborative Search Approach for Maritime Rescue Under Imperfect Information

by Lang Ruan, Haotian Yu, Liuhao Chen and Xiao Yi

Drones 2026, 10(5), 386; https://doi.org/10.3390/drones10050386 - 18 May 2026

Viewed by 433

Abstract

Collaborative search of multiple uncrewed aerial vehicles (UAVs) is a critical technology for maritime rescue operations. To address the challenge posed by an unknown target motion direction, we present an innovative framework, “Dynamic Response-Intelligent Coverage,” and develop a multi-UAV collaborative search model. This [...] Read more.

Collaborative search of multiple uncrewed aerial vehicles (UAVs) is a critical technology for maritime rescue operations. To address the challenge posed by an unknown target motion direction, we present an innovative framework, “Dynamic Response-Intelligent Coverage,” and develop a multi-UAV collaborative search model. This study employs a hybrid methodology combining theoretical analysis and simulation optimization. By leveraging the geometric properties of logarithmic spiral (LS) curves, rigorous kinematic modeling and mathematical derivations were conducted to obtain the theoretically optimal solutions for single- and dual-UAV collaborative search. Furthermore, to address the traditional analytical methods’ “curse of dimensionality” issue through a strategy space search and adaptive adjustment mechanism, the genetic-optimization-based multi-UAV collaborative search strategy optimization algorithm (GA-MCSSO) is developed for scenarios involving three or more UAVs. Simulation results demonstrate that: (1) In the dual-UAV search scenario, the simulation optimization results closely align with the theoretically optimal solutions, with highly consistent convergence trajectories; (2) In multi-UAV search scenarios, Compared with SSB and GA-MCSSO-Seq, GA-MCSSO reduces the total coverage time by approximately 32% and improves the cumulative detection probability by approximately 18% under idealized spiral planning conditions. When evaluated under realistic constraints, the absolute improvement in total coverage time averages 0.1–0.2 s, with a maximum gain of nearly 1 s. The theoretical-simulation complementary framework established in this study provides a systematic solution for collaborative search from single UAV to multi-UAV scenarios. The methodology offers technical insights for multi-agent dynamic optimization problems and provides significant theoretical support for practical search operations. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)

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13 pages, 3466 KB

Open AccessEditor’s ChoiceArticle

Aerodynamic Wing Design for an Unmanned Aerial Vehicle for Agricultural Applications

by Gibran Antonio Yáñez Juárez, Adrián Alberto Castro De La Cruz, Luis Pérez-Domínguez and Arturo Paz Pérez

Drones 2026, 10(5), 373; https://doi.org/10.3390/drones10050373 - 13 May 2026

Cited by 1 | Viewed by 934

Abstract

This study presents the aerodynamic design of the wing system for a fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV), developed to enhance energy efficiency and operational performance in agricultural applications. The design responds to the limitations of conventional multirotor drones, [...] Read more.

This study presents the aerodynamic design of the wing system for a fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV), developed to enhance energy efficiency and operational performance in agricultural applications. The design responds to the limitations of conventional multirotor drones, which are limited by low endurance and high energy consumption, and crop-dusting aircraft, which are unsuitable for irregular terrain such as that found in Chihuahua, Mexico. A comprehensive methodology was adopted, integrating the selection of airfoils optimized for low-Reynolds-number conditions, computational fluid dynamics (CFD) simulations, winglet incorporation, and experimental validation through wind tunnel testing. The SELIG 1223 airfoil was selected for its superior aerodynamic efficiency, demonstrating a potential reduction of up to 55% in power requirements compared to multirotor configurations. Despite some variability in experimental results, the proposed design demonstrated consistent feasibility and reliability. Future work will focus on field validation and geometric adaptation to diverse operational scenarios, reinforcing its applicability across heterogeneous agricultural landscapes. Full article

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

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30 pages, 15045 KB

Open AccessEditor’s ChoiceArticle

Assessing the Carbon Mitigation Potential of UAV-Based Last-Mile Delivery Using 3D Path Planning: A Case Study of Shanghai

by Ruiqi Wang and Yang Liu

Drones 2026, 10(5), 364; https://doi.org/10.3390/drones10050364 - 11 May 2026

Cited by 1 | Viewed by 850

Abstract

Urban last-mile delivery is an increasingly important source of transport-related emissions, yet evidence on low-altitude logistics under real-order demand and urban spatial constraints remains limited. Taking Shanghai as a representative megacity, this study integrates 185,673 real parcel orders with 3D urban spatial data [...] Read more.

Urban last-mile delivery is an increasingly important source of transport-related emissions, yet evidence on low-altitude logistics under real-order demand and urban spatial constraints remains limited. Taking Shanghai as a representative megacity, this study integrates 185,673 real parcel orders with 3D urban spatial data to develop a unified unmanned aerial vehicle (UAV)–courier carbon accounting framework. The framework combines 3D UAV route-planning algorithms, UAV energy-consumption models, electric courier-vehicle energy models, and grid emission factors to compare carbon emissions between UAV and conventional delivery modes. The results show that, under the modeled operating assumptions, UAV delivery tends to provide lower per-delivery carbon emissions under lightweight and high-speed operating conditions. Scenario analysis further suggests that UAV deployment in Shanghai could reduce carbon emissions by approximately 343,300 t CO₂ annually by 2030. These findings provide quantitative support for urban low-altitude logistics planning, infrastructure deployment, and policy design for low-carbon last-mile delivery. The framework is transferable to other Chinese cities with similar urban conditions, but the numerical results require local recalibration of parcel demand, urban morphology, airspace constraints, and electricity-related carbon factors. Full article

(This article belongs to the Section Innovative Urban Mobility)

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19 pages, 3753 KB

Open AccessEditor’s ChoiceArticle

Cooperative UAV Swarm Communication Networks for Rapid Disaster Assessment in GPS-Denied Environments

by Pinglu Wang, Jiahao Li, Jiahua Wei, Lei Shi, Bei Hou and Fei Xie

Drones 2026, 10(5), 355; https://doi.org/10.3390/drones10050355 - 7 May 2026

Viewed by 1111

Abstract

Timely situational awareness is essential in disaster management but normal Unmanned Aerial Vehicle (UAV) flight cannot take place when the Global Positioning System (GPS) signals are blocked or jammed. This paper addresses the issue of swarm cohesion and localization in these hostile conditions. [...] Read more.

Timely situational awareness is essential in disaster management but normal Unmanned Aerial Vehicle (UAV) flight cannot take place when the Global Positioning System (GPS) signals are blocked or jammed. This paper addresses the issue of swarm cohesion and localization in these hostile conditions. We present a Cooperative Swarm-Mesh Network (CSMN), a hybrid structure that can alternate between an implicit Silent Mode and an explicit Leader–Follower mode based on distributed Extended Kalman Filters (DEKFs) in the face of communication failures. The system takes advantage of convex polygon decomposition to optimize the coverage in the area. The use of simulation studies with NS-3 and ROS has shown that the proposed framework can retain sub-meter localization error (RMSE < 0.9 m) in GPS-denied environments and provide 92% coverage of the area, which is 35% higher than the coverage with other baseline approaches. Within the simulated conditions evaluated using Gazebo/NS-3, sensor drift and network vulnerability are effectively addressed by the CSMN framework. These simulation-based results offer a promising blueprint for autonomous disaster evaluation, pending hardware-in-the-loop and field validation. Validation is conducted across two qualitatively distinct simulated environments: dense urban rubble and a sparse open field. Performance advantages generalise beyond a single test configuration, with mean localization RMSE remaining below 0.85 m in both scenarios. Full article

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

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29 pages, 5890 KB

Open AccessEditor’s ChoiceArticle

A Cooperative Keypoint–Sparse Cache and Improved PPO Framework for Rapid 3D UAV Path Planning

by Yonggang Wang, Genwei Wang, Zehua Chen, Jiang Wang and Pu Huang

Drones 2026, 10(5), 330; https://doi.org/10.3390/drones10050330 - 28 Apr 2026

Cited by 1 | Viewed by 785

Abstract

UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint–Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both [...] Read more.

UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint–Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both static terrain and dynamic obstacles. The KSC strategy reduces search complexity through orthogonal slice-based sparse keypoint extraction and path caching reuse, thereby improving the efficiency of global path planning. On this basis, PPO-based local obstacle avoidance is activated only when safety thresholds are exceeded, while the remaining path is replanned globally after threat clearance, which confines avoidance computation to a local scope while preserving global path quality. Experiments in static mountainous environments show that KSC requires substantially less computation time than RRT* and Informed RRT* while maintaining competitive path efficiency, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. Hybrid navigation validation experiments further show that KSC-PPO achieves high mission success, low collision rates, and low avoidance overhead in dynamic mountainous environments. Experiments demonstrate that KSC-PPO decomposes exponential global search space into controllable linear subproblems, significantly enhancing efficiency while ensuring path quality, providing an effective solution for UAV navigation in complex terrain. Full article

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

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23 pages, 4407 KB

Open AccessEditor’s ChoiceArticle

Measurement-Informed Latency Limits for Real-Time UAV Swarm Coordination

by Rodolfo Vera-Amaro, Alberto Luviano-Juárez, Mario E. Rivero-Ángeles, Diego Márquez-González and Danna P. Suárez-Ángeles

Drones 2026, 10(4), 310; https://doi.org/10.3390/drones10040310 - 21 Apr 2026

Cited by 1 | Viewed by 1395

Abstract

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation [...] Read more.

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

(This article belongs to the Special Issue Low-Latency Communication for Real-Time UAV Applications)

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20 pages, 17293 KB

Open AccessEditor’s ChoiceArticle

Acoustic Effects of Differential Rotor Speeds on Twin-Propeller UAV System

by Burak Buda Turhan, Djamel Rezgui and Mahdi Azarpeyvand

Drones 2026, 10(4), 302; https://doi.org/10.3390/drones10040302 - 18 Apr 2026

Cited by 4 | Viewed by 944

Abstract

This study investigates the aerodynamic, aeroacoustic, and psychoacoustic behaviour of a side-by-side twin-propeller Unmanned Aerial Vehicle (UAV) system operating under both static and forward-flight conditions, with particular focus on the effects of asynchronous rotational speeds. Experiments were conducted using two identical five-bladed constant [...] Read more.

This study investigates the aerodynamic, aeroacoustic, and psychoacoustic behaviour of a side-by-side twin-propeller Unmanned Aerial Vehicle (UAV) system operating under both static and forward-flight conditions, with particular focus on the effects of asynchronous rotational speeds. Experiments were conducted using two identical five-bladed constant pitch propellers with a diameter of 9 in (228.6 mm) and a pitch to diameter ratio of 1. Rotational speed differences between 0 and 300 rpm were examined in 50 rpm increments at inflow velocities of 0 m/s, 14 m/s and 24 m/s. The results show that variations in rotational speed have a significant influence on both acoustic levels and perceived annoyance. Asynchronous operation causes the dominant tonal peak at the blade passing frequency to split into two components, reducing tonal reinforcement. This produces noise level reductions of approximately 2 dB in static and high advance ratio conditions, increasing to about 5 dB reduction at low advance ratios. Psychoacoustic metrics show greater sensitivity to tonal structure than to overall sound pressure level, with annoyance reductions of about 5% in static conditions and up to 15% at low advance ratios. A modest aerodynamic penalty of about 5% at

Δ N = 50

rpm is observed, increasing with larger speed mismatches. Full article

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

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25 pages, 1271 KB

Open AccessEditor’s ChoiceReview

Recent Advances for Generative AI-Enabled Unmanned Aerial Vehicle Systems and Applicable Technologies

by Hyunbum Kim

Drones 2026, 10(4), 292; https://doi.org/10.3390/drones10040292 - 16 Apr 2026

Cited by 1 | Viewed by 2563

Abstract

Unmanned Aerial Vehicles (UAVs) have been key platforms to perform sensing, analytics and automation across intelligent transportation, construction, smart agriculture, logistics and defense. Generative AI (GenAI) accelerates intelligence of UAVs by creating synthetic data, simulating environments and improving learning with restricted data conditions. [...] Read more.

Unmanned Aerial Vehicles (UAVs) have been key platforms to perform sensing, analytics and automation across intelligent transportation, construction, smart agriculture, logistics and defense. Generative AI (GenAI) accelerates intelligence of UAVs by creating synthetic data, simulating environments and improving learning with restricted data conditions. When integrated with digital twin and AI frameworks, GenAI enables advanced design, modeling, adaptation and making a decision. In this paper, we survey recent advances for generative AI-enabled UAVs systems and applicable scenarios. Then, we categorize four applicable research branches using generative AI-enabled UAVs for intelligent transportation systems, digital twin and smart infrastructure, smart agriculture, last-mile logistics and delivery. Full article

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

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19 pages, 11440 KB

Open AccessEditor’s ChoiceArticle

Mapping Coastal Marine Habitats with RGB and Multispectral UAS Imagery to Support Seaweed Aquaculture Management and Ecosystem Conservation

by Isabel Urbina-Barreto, Evangelos Alevizos, Telina Minolalaina Randrianary, Manon Museux, Ravo A. Mahandrisoa Randriamaroson, Anne Chauvin, Solofoarisoa Rakotoniaina, Sébastien Jan, Laurent Barillé and Aline Tribollet

Drones 2026, 10(4), 276; https://doi.org/10.3390/drones10040276 - 10 Apr 2026

Cited by 2 | Viewed by 1879

Abstract

Madagascar’s expanding blue economy is largely underpinned by seaweed aquaculture, particularly Kappaphycus alvarezii (Cottonii), which offers an alternative to declining small-scale fisheries and strengthens the resilience of coastal socio-ecosystems. Ensuring the sustainability of this economic activity requires effective ecological monitoring of aquaculture sites [...] Read more.

Madagascar’s expanding blue economy is largely underpinned by seaweed aquaculture, particularly Kappaphycus alvarezii (Cottonii), which offers an alternative to declining small-scale fisheries and strengthens the resilience of coastal socio-ecosystems. Ensuring the sustainability of this economic activity requires effective ecological monitoring of aquaculture sites and surrounding habitats. This study examines and compares the performance of two imaging configurations—an RGB composite derived from a subset of multispectral images capturing red (650 nm), green (560 nm), and blue (450 nm) bands; and a five-band multispectral (MS) image encompassing blue, green, red, red-edge (730 nm), and near-infrared (840 nm) bands—combined with a Random Forest (RF) classification model, for benthic habitat mapping in a seaweed cultivation context. High-resolution orthomosaics (2 cm/pixel) enabled the discrimination of Kappaphycus cultivation plots from three shallow-water habitats: (i) ‘benthic macrophytes’, which comprise: seagrass meadows and benthic macroalgal; (ii) ‘sandy bottom’ and (iii) ‘green algae’. The RF classification achieved an overall accuracy of 87% (Kappa = 0.82) across ~10 hectares. Producer’s accuracy exceeded 80% for Kappaphycus cultivation, green algae, and sandy bottom for both the RGB and MS datasets, indicating strong classification performance. However, early-stage seaweed was occasionally misclassified as benthic macrophytes, likely due to its low biomass and weak spectral signature. This UAS-based approach provided a robust and cost-effective framework for monitoring off-bottom seaweed farms and associated natural habitats. This approach supports sustainable aquaculture development and integrated coastal management in Madagascar and comparable tropical reef socio-ecosystems. Full article

(This article belongs to the Section Drones in Ecology)

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36 pages, 2147 KB

Open AccessEditor’s ChoiceArticle

Regulatory Frameworks and Development Standards for Civilian Unmanned Aircraft Systems: From Regulatory Safety Intent to Development Lifecycles

by Adina Aniculaesei

Drones 2026, 10(4), 271; https://doi.org/10.3390/drones10040271 - 9 Apr 2026

Viewed by 1759

Abstract

The rapid growth of civilian unmanned aircraft systems (UAS) for various applications, such as logistics, inspection and surveillance has enabled increasingly complex UAS operations in shared airspace and in close proximity to third parties. European regulations for civilian UAS provide a comprehensive framework [...] Read more.

The rapid growth of civilian unmanned aircraft systems (UAS) for various applications, such as logistics, inspection and surveillance has enabled increasingly complex UAS operations in shared airspace and in close proximity to third parties. European regulations for civilian UAS provide a comprehensive framework for operational approval, based on operational rules, risk-based approval processes, and airspace management concepts. While regulatory frameworks and current international standards provide detailed guidance for operational authorization for UAS, they do not prescribe how UAS should be developed and verified at a system and software level to support safety assurance in a structured and traceable manner. This paper addresses this gap by proposing a method for extracting system-level and software-level safety requirements from regulatory artifacts. The method interprets regulatory safety intent–expressed through operational constraints, mitigation measures, and robustness expectations–and translates it into development-relevant safety requirements under explicit operational assumptions. Building on these requirements, the paper introduces a software-centered system lifecycle for UAS development. The proposed lifecycle integrates regulatory safety intent, risk-proportionate assurance, and staged verification. Finally, through a cross-domain analysis, the paper positions the proposed approach relative to established practices from the automotive and the avionics domains, aiming to identify transferable and necessary adaptations for the development of unmanned aircraft systems. Full article

(This article belongs to the Section Innovative Urban Mobility)

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21 pages, 6478 KB

Open AccessEditor’s ChoiceArticle

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

Viewed by 1233

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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23 pages, 888 KB

Open AccessEditor’s ChoiceArticle

“For Us, Drones Mean Health”: How Medical Drone Delivery Affects Healthcare Outcomes, Accessibility, and Trust in Remote Regions of Madagascar

by Brianne O’Sullivan, Christallin Lydovick Rakotoasy, Lorie Donelle, Nicole Haggerty and Elysée Nouvet

Drones 2026, 10(4), 228; https://doi.org/10.3390/drones10040228 - 24 Mar 2026

Viewed by 1918

Abstract

Medical drone delivery (MDD), defined as the use of uncrewed aerial vehicles to transport medical products, is an emerging technological innovation responding to persistent health supply chain challenges in rural and low-resource settings. Within sub-Saharan Africa, MDD systems have demonstrated large-scale success in [...] Read more.

Medical drone delivery (MDD), defined as the use of uncrewed aerial vehicles to transport medical products, is an emerging technological innovation responding to persistent health supply chain challenges in rural and low-resource settings. Within sub-Saharan Africa, MDD systems have demonstrated large-scale success in improving key health outcomes, health supply chain efficiency, and reductions in medical product stockouts and wastage. However, the existing evidence base on the effectiveness of this technology is dominated by quantitative, performance-based evaluations, with limited emphasis on the community-driven mechanisms that shape such outcomes. Drawing on original qualitative research, this article presents a qualitative secondary analysis (QSA) of interview data collected as part of a larger case study on MDD in Madagascar. The QSA, guided by socio-technical systems theory, analyzes a subset of 18 interviews with 23 community-level stakeholders to understand how MDD affects healthcare services in remote regions of the country. Participants reported that MDD led to downstream healthcare improvements in vaccination coverage and malaria-related health outcomes. These improvements were enabled through four interconnected socio-technical mechanisms: (1) improved medical product availability through the mitigation of geographic and transportation barriers, (2) stabilization of vaccine and cold chain transportation, (3) building trust and healthcare-seeking behaviours through predictable service delivery, and (4) reduced physical, mental, and financial burdens experienced by healthcare workers. A final, cross-cutting theme emphasized was the criticality of MDD program continuity, with participants noting that operation disruptions or withdrawals risked reversing benefits and breaking communities’ trust in the health system. By centering lived realities, perceptions, and social processes, this article bridges the gap between predominantly quantitative evidence on MDD systems and the experiences of the communities they are intended to serve. Full article

(This article belongs to the Section Innovative Urban Mobility)

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33 pages, 3743 KB

Open AccessEditor’s ChoiceArticle

Distributed Task Allocation Algorithm for Heterogeneous UAVs Based on Reinforcement Learning

by Peng Sun, Guangwei Yang, Xin Xu, Jieyong Zhang, Xida Deng, Yongzhuang Zhang and Jie Cui

Drones 2026, 10(3), 220; https://doi.org/10.3390/drones10030220 - 20 Mar 2026

Cited by 2 | Viewed by 1407

Abstract

To address the challenges faced by heterogeneous Unmanned Aerial Vehicle (UAV) systems in complex task allocation, including over-reliance on centralized scheduling, training deadlock, inadequate capture of temporal collaboration, and unstable training under sparse reward conditions, this paper proposes a distributed task allocation algorithm [...] Read more.

To address the challenges faced by heterogeneous Unmanned Aerial Vehicle (UAV) systems in complex task allocation, including over-reliance on centralized scheduling, training deadlock, inadequate capture of temporal collaboration, and unstable training under sparse reward conditions, this paper proposes a distributed task allocation algorithm based on reinforcement learning. The algorithm adopts a decentralized decision-making architecture, which enables the autonomous formation of UAV collaborative groups without the need for a global scheduling center. A cascaded submission timeout mechanism is introduced to prevent training deadlock; the combination of Long Short-Term Memory (LSTM) and attention mechanism is employed to accurately model temporal correlations and collaborative dependencies; and the Proximal Policy Optimization (PPO) algorithm is leveraged to optimize the training stability under sparse reward conditions. Experimental results demonstrate that the proposed algorithm achieves a 100% task success rate in scenarios of different scales, and its key metrics, including makespan, time cost and waiting time, are significantly superior to those of mainstream baseline methods such as the Genetic Algorithm (GA) and the Hungarian Algorithm (HA). Moreover, the algorithm still maintains excellent robustness under the conditions of UAV failures, parameter variations, and dynamic task perturbations. This method supports zero-shot generalization for any number of UAVs and tasks and provides an efficient and reliable solution for the real-time collaborative scheduling of heterogeneous UAV systems. Full article

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

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25 pages, 30697 KB

Open AccessEditor’s ChoiceArticle

A Collaborative Navigation Algorithm for Unmanned Aerial Vehicles Based on Joint Cognition and Risk Perception

by Chenkang Huang, Ruixuan Wei, Benqi Jiang, Pengfei Wei and Qirui Zhang

Drones 2026, 10(3), 186; https://doi.org/10.3390/drones10030186 - 9 Mar 2026

Cited by 1 | Viewed by 1038

Abstract

Addressing the challenges of cooperative navigation for unmanned aerial vehicles (UAVs) in dynamic unknown environments, this paper proposes a collaborative method based on Joint Cognition and Risk Perception (JCRP). The method employs a sequential cooperative framework, where a pioneer UAV constructs a transferable [...] Read more.

Addressing the challenges of cooperative navigation for unmanned aerial vehicles (UAVs) in dynamic unknown environments, this paper proposes a collaborative method based on Joint Cognition and Risk Perception (JCRP). The method employs a sequential cooperative framework, where a pioneer UAV constructs a transferable environmental map, while successor UAVs integrate this prior knowledge with real-time perceptions to form a joint cognitive representation. A dynamic trust mechanism quantitatively evaluates cognitive reliability, enabling risk-aware path planning that balances safety and efficiency. Simulations and physical experiments demonstrate that JCRP reduces the path length of follower UAVs by approximately 41.39% and improves the safe decision ratio by 10.9 percentage points over baseline methods. These results validate the method’s robustness in complex scenarios, such as maze-like environments, highlighting its potential for applications in search-and-rescue. Full article

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

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29 pages, 7593 KB

Open AccessEditor’s ChoiceArticle

UAV-Based Visual Detection and Tracking of Drowning Victims in Maritime Rescue Operations

by Thanh Binh Ngo, Long Ngo, Danh Thanh Nguyen, Anh Vu Phi, Asanka Perera and Andy Nguyen

Drones 2026, 10(2), 146; https://doi.org/10.3390/drones10020146 - 19 Feb 2026

Cited by 6 | Viewed by 2543

Abstract

Maritime search and rescue (SAR) operations are challenged by vast search areas, poor visibility, and the time-critical nature of victim survival, particularly in dynamic coastal areas. This study presents an intelligent unmanned aerial vehicle (UAV) framework for real-time detection, tracking, and prioritization of [...] Read more.

Maritime search and rescue (SAR) operations are challenged by vast search areas, poor visibility, and the time-critical nature of victim survival, particularly in dynamic coastal areas. This study presents an intelligent unmanned aerial vehicle (UAV) framework for real-time detection, tracking, and prioritization of people in distress at sea. Unlike existing UAV-based SAR systems that rely on visual sensing or offline human intervention, the proposed framework integrates RGB-thermal multimodal sensing and posture recognition to enhance victim prioritization and survivability estimation. Visual-thermal data support human posture detection, inference of physiological indicators, and autonomous UAV navigation. Metadata are transmitted to a ground control station to enable adaptive altitude control, trajectory rejoining, and multi-target prioritization. Field-inspired experiments in Quang Ninh Province, Vietnam demonstrated robust real-time performance, achieving 23 FPS with detection accuracy up to 84% for swimming subjects and over 50% for drowning postures. These findings demonstrate that Edge-AI-enabled UAVs can serve as a practical and efficient solution for maritime SAR, reducing response times and improving mission outcomes. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)

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54 pages, 35798 KB

Open AccessEditor’s ChoiceArticle

Simulation-Based Airspace Accessibility Analysis for Integrating Regional Unmanned Aircraft Systems into Non-Towered Airport Traffic Patterns

by Tim Felix Sievers

Drones 2026, 10(2), 141; https://doi.org/10.3390/drones10020141 - 17 Feb 2026

Cited by 1 | Viewed by 1064

Abstract

Unmanned aircraft systems for regional operations are assumed to frequently operate at non-towered airports, where routine integration remains challenging due to limited separation principles and partially observable manned traffic intent. This research investigates tactical procedures for integrating unmanned aircraft into non-towered airport environments, [...] Read more.

Unmanned aircraft systems for regional operations are assumed to frequently operate at non-towered airports, where routine integration remains challenging due to limited separation principles and partially observable manned traffic intent. This research investigates tactical procedures for integrating unmanned aircraft into non-towered airport environments, where unmanned aircraft must interact with manned traffic under procedural constraints. A simulation framework is developed that combines historical traffic data with standard traffic pattern procedures and rule-based decision-making to integrate unmanned aircraft at non-towered airports. The simulation logic includes detection of manned traffic activities, rule-based queuing, and airspace capacity constraints. By varying detection look-ahead times (60/120/180 s) and unmanned aircraft traffic rates (15/30 min), the simulation quantifies terminal airspace accessibility and derives metrics that capture throughput (no conflict versus deconflicted holding flights), delay propagation (holding minutes and holding orbit counts), concept feasibility (aborted/denied holdings), and altitude band utilization. The results show a consistent safety versus throughput trade-off with longer look-ahead times increasing holding demand but reducing the share of aborted holdings, while higher traffic volumes amplify holdings and delay. Holdings are predominantly conducted in the lowest available holding altitude at 2500 feet above the ground, with occasional multi-layer use to handle traffic peaks. Full article

(This article belongs to the Section Innovative Urban Mobility)

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25 pages, 2915 KB

Open AccessEditor’s ChoiceArticle

Soft Real-Time Asynchronous Online Learning from Input–Output Data for UAV Model Reference Control Under Uncertain Dynamics and Faulty Actuation

by Mircea-Bogdan Radac

Drones 2026, 10(2), 137; https://doi.org/10.3390/drones10020137 - 15 Feb 2026

Cited by 5 | Viewed by 756

Abstract

An online off-policy asynchronous real-time model reference tracking control (OOART-MRTC) algorithm is proposed and validated for unmanned aerial vehicles (UAVs) characterized by faulty actuation and parametric uncertainty. The optimal control problem is posed based on approximate dynamic programming (ADP) and reinforcement learning (RL) [...] Read more.

An online off-policy asynchronous real-time model reference tracking control (OOART-MRTC) algorithm is proposed and validated for unmanned aerial vehicles (UAVs) characterized by faulty actuation and parametric uncertainty. The optimal control problem is posed based on approximate dynamic programming (ADP) and reinforcement learning (RL) theory, using a virtual state-space representation constructed exclusively on input–output true system data, which exploits the observability theory. OOART-MRTC learns control by interacting with the system, starting from an initial stabilizing controller derived from an approximate uncertain model. Learning convergence and stability under the proposed adaptive behavior are analyzed. Since the learning iterations cannot update within a sampling period, an asynchronous mechanism is proposed for updating the controller parameters, leveraging real-time control and multi-tasking. The complexity associated with the resulting high-dimensional system is solved by efficient linear parameterization and validated on a realistic case study where three coupled double integrators describe the UAV attitude control. Full article

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

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21 pages, 1440 KB

Open AccessEditor’s ChoiceArticle

Uncertainty-Aware Evidential Fusion for Multi-Modal Object Detection in Autonomous Driving

by Qihang Yang, Yang Zhao and Hong Cheng

Drones 2026, 10(2), 130; https://doi.org/10.3390/drones10020130 - 13 Feb 2026

Cited by 3 | Viewed by 2933

Abstract

The advancement of autonomous driving technologies necessitates the development of sophisticated object detection systems capable of integrating heterogeneous sensor data to overcome the inherent limitations of unimodal approaches. While multi-modal fusion strategies offer promising solutions, they confront significant challenges such as data alignment [...] Read more.

The advancement of autonomous driving technologies necessitates the development of sophisticated object detection systems capable of integrating heterogeneous sensor data to overcome the inherent limitations of unimodal approaches. While multi-modal fusion strategies offer promising solutions, they confront significant challenges such as data alignment complexities in early fusion and computational burdens coupled with overfitting risks in deep fusion methodologies. To address these issues, we propose a Multi-modal Multi-class Late Fusion (MMLF) framework that operates at the decision level. This late-fusion strategy preserves the architectural integrity of individual detectors and facilitates the flexible integration of diverse modalities. A key innovation of our approach is the incorporation of an evidence-theoretic uncertainty quantification mechanism, based on Dempster-Shafer theory, which provides a mathematically grounded confidence measure. Comprehensive offline evaluations on the KITTI benchmark dataset demonstrate the effectiveness of our framework, showing substantial performance improvements across multiple metrics (including 2D detection, 3D detection, and bird’s-eye view tasks) while simultaneously achieving significant reductions in uncertainty estimates—by approximately 77% for cars, 76% for pedestrians, and 67% for cyclists. These results collectively enhance both the reliability and interpretability of object detection outcomes. This work provides a versatile and scalable solution for multi-modal object detection that effectively addresses critical challenges in autonomous driving applications. Full article

(This article belongs to the Special Issue Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems)

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16 pages, 1208 KB

Open AccessEditor’s ChoiceArticle

The Efficacy of Drone-In-A-Box Technology for Marine Megafauna Surveillance off Coastal Beaches

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

Drones 2026, 10(2), 122; https://doi.org/10.3390/drones10020122 - 11 Feb 2026

Cited by 1 | Viewed by 1762

Abstract

Drones are increasingly used in marine science for detecting and monitoring large megafauna in nearshore areas. Remotely operated, autonomous drone missions have the potential to improve the overall efficiency of drone-based research. We assessed the utility of autonomous drone operations by comparing real-time [...] Read more.

Drones are increasingly used in marine science for detecting and monitoring large megafauna in nearshore areas. Remotely operated, autonomous drone missions have the potential to improve the overall efficiency of drone-based research. We assessed the utility of autonomous drone operations by comparing real-time detection rates of marine megafauna (i.e., dolphins, rays, sharks, turtles) between a remotely operated Drone-In-A-Box (DIAB) system using pre-programmed missions and standard site-operated manual flight procedures. Megafauna were identified in real time during each drone mission, and missed detections were quantified through post-analysis of drone footage. A total of 71 missions were completed, with autonomous and manual flights operating concurrently at either 60 m or 80 m altitude, and a flight speed of 8 m/s. There were 107 and 117 real-time megafauna observations recorded for autonomous and manual operations, respectively. Post-flight analysis determined an overall missed detection of 52.4% for autonomous and 30.4% for manual operations, with undercounting higher for autonomous operations across all faunal groups. Dolphin detection in real time had the highest agreement with post-flight analysis, while real-time turtle detection proved the most difficult. Cloud cover, sea state, time of day, and water clarity significantly affected real-time false negative detection rates, though their relative importance varied across faunal groups and between flight procedures. Overall, remotely operated, autonomous drones have the potential to enhance long-term marine megafauna research, particularly when combined with post-flight analysis. Integrating artificial intelligence into autonomous drone operations will also be beneficial, especially for shark surveillance programs where real-time detection is essential for beach-user safety. Full article

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

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39 pages, 8880 KB

Open AccessEditor’s ChoiceSystematic Review

UAV Technologies for Precision Agriculture: Capabilities, Constraints, and Deployment Models for Smallholder Systems in Sub-Saharan Africa

by Wasiu Akande Ahmed, Joel Segun Ojerinde, Seyi Festus Olatoyinbo and Friday John Ogaleye

Drones 2026, 10(2), 115; https://doi.org/10.3390/drones10020115 - 5 Feb 2026

Cited by 4 | Viewed by 2265

Abstract

Sub-Saharan Africa’s cereal yields remain ~60% below global benchmarks, while unmanned aerial vehicle (UAV) adoption in smallholder systems averages below 2–3% across major economies, revealing a performance–adoption disconnect that requires systematic investigation. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 [...] Read more.

Sub-Saharan Africa’s cereal yields remain ~60% below global benchmarks, while unmanned aerial vehicle (UAV) adoption in smallholder systems averages below 2–3% across major economies, revealing a performance–adoption disconnect that requires systematic investigation. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 synthesis of 127 sources quantifies the performance of UAV sensors and identifies mechanisms that constrain their adoption across regional agricultural systems. Random-effects meta-analysis synthesized evidence from 81 quantitative studies, yielding 101 total observations. Pooled detection accuracy was estimated from 49 studies contributing 52 observations (mean 90.2%, 95% confidence interval (CI): 89.8–92.6%). Yield prediction performance was assessed from 32 studies contributing 49 observations (pooled coefficient of determination (R²) = 0.841, 95% CI: 0.827–0.855), validating technical feasibility. Cost-effectiveness analysis reveals significant performance–price differentiation: red-green-blue (RGB) sensors achieve 89.4% accuracy at United States Dollar (USD) 16.50 per percentage point versus hyperspectral systems at 93.7% accuracy but at USD 132.17 per point, resulting in a 25.6 times cost differential. Yield prediction models demonstrate robust performance (R² = 0.81; cereal crops R² = 0.82). Barrier analysis identifies economic constraints as the primary limiter, with capital requirements reaching 0.8–3.1 times the annual smallholder income. Infrastructure deficits impose secondary constraints, particularly in rural electrification, below 50%. Case study synthesis reveals that coordinated interventions addressing multiple barriers simultaneously—cooperative ownership, off-grid infrastructure, and streamlined regulation—achieve substantially higher adoption than isolated approaches. Engineering economics positions RGB platforms for individual deployment and multispectral systems for cooperative scales (20–50 farmers), establishing feasible deployment pathways for tens of million regional smallholder operations. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)

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15 pages, 4560 KB

Open AccessEditor’s ChoiceArticle

Simultaneous A2A and A2G Channel Measurement System for UAV Communications

by Hanwen Xu, Hua Xie, Nan Ming, Hangang Li, Kai Mao, Xiaomin Chen, Zhangfeng Ma, Boyu Hua and Qiuming Zhu

Drones 2026, 10(2), 104; https://doi.org/10.3390/drones10020104 - 2 Feb 2026

Cited by 1 | Viewed by 1245

Abstract

Air-to-air (A2A) and air-to-ground (A2G) communication links are typical link types for unmanned aerial vehicle (UAV) communication networks, where radio propagation channels are fundamental for the design and optimization of corresponding communication systems. In this paper, a UAV channel measurement system based on [...] Read more.

Air-to-air (A2A) and air-to-ground (A2G) communication links are typical link types for unmanned aerial vehicle (UAV) communication networks, where radio propagation channels are fundamental for the design and optimization of corresponding communication systems. In this paper, a UAV channel measurement system based on two unmanned aerial vehicles (UAVs) is developed, which is capable of simultaneous A2A and A2G measurements. This system adopts an integrated hardware and signal processing architecture that ensures time and frequency synchronization among multiple aerial and ground nodes. Several data postprocessing steps, including the back-to-back calibration, sliding-correlation-based channel impulse response (CIR) extraction, and constant false alarm rate (CFAR)-based multi-path extraction, are performed to achieve accurate channel data. A channel emulator is used to validate the accuracy of the developed system. Finally, the developed channel measurement system is applied to conduct field channel measurements in a campus scenario. Measured channel characteristics, including path loss (PL), shadow fading (SF), Rician K-factor, root mean square delay spread (RMS-DS), and small-scale fading (SSF) are analyzed, which reveal distinct propagation behaviors between the A2A and A2G channels. These results provide valuable experimental insights and channel measurement data for modeling UAV channels. Full article

(This article belongs to the Section Drone Communications)

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23 pages, 5771 KB

Open AccessEditor’s ChoiceArticle

Intelligent Control for Quadrotors Based on a Novel Method: TD3-ADRC

by Runyu Cai, Liang Zhang, Wutao Qin and Jie Yan

Drones 2026, 10(2), 110; https://doi.org/10.3390/drones10020110 - 2 Feb 2026

Cited by 1 | Viewed by 1081

Abstract

To address the requirements for multi-channel decoupling and high-precision control in quadrotor UAV systems, this paper proposes a novel intelligent controller (TD3-ADRC) which integrates Active Disturbance Rejection Control (ADRC) with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Firstly, the dynamic model [...] Read more.

To address the requirements for multi-channel decoupling and high-precision control in quadrotor UAV systems, this paper proposes a novel intelligent controller (TD3-ADRC) which integrates Active Disturbance Rejection Control (ADRC) with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Firstly, the dynamic model of the quadrotor is established. Secondly, a parameterized tanh function is introduced and applied to design the tracking differentiator, extended state observer, and nonlinear feedback control law. Then, the TD3 learning mechanism is incorporated to automatically learn and optimize controller parameters, thereby significantly enhancing the system’s disturbance rejection capability. Finally, simulation studies comparing conventional PID, ADRC, DDPG and the proposed TD3-ADRC algorithms are conducted in Simulink. In addition, a bench test system is developed using the PX4 flight controller. Experimental results show that, under complex environmental conditions, the proposed TD3-ADRC controller outperforms both conventional PID and linear ADRC methods in terms of reliability and adaptability, validating the effectiveness of the proposed control approach. Full article

(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)

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32 pages, 16482 KB

Open AccessEditor’s ChoiceArticle

LF-SSM: Lightweight HiPPO-Free State Space Model for Real-Time UAV Tracking

by Tianyu Wang, Xinghua Xu, Shaohua Qiu, Changchong Sheng, Di Wang, Hui Tian and Jiawei Yu

Drones 2026, 10(2), 102; https://doi.org/10.3390/drones10020102 - 31 Jan 2026

Cited by 1 | Viewed by 1105

Abstract

Visual object tracking from unmanned aerial vehicles (UAVs) demands both high accuracy and computational efficiency for real-time deployment on resource-constrained platforms. While state space models (SSMs) offer linear computational complexity, existing methods face critical deployment challenges. They rely on the HiPPO framework with [...] Read more.

Visual object tracking from unmanned aerial vehicles (UAVs) demands both high accuracy and computational efficiency for real-time deployment on resource-constrained platforms. While state space models (SSMs) offer linear computational complexity, existing methods face critical deployment challenges. They rely on the HiPPO framework with complex discretization procedures and employ hardware-aware algorithms optimized for high-performance GPUs, which introduce deployment overhead and are difficult to transfer to edge platforms. Additionally, their fixed polynomial bases may cause information loss for tracking features with complex geometric structures. We propose LF-SSM, a lightweight HiPPO (High-order Polynomial Projection Operators)-free state space model that reformulates state evolution on Riemannian manifolds. The core contribution is the Geodesic State Module (GSM), which performs state updates through tangent space projection and exponential mapping on the unit sphere. This design eliminates complex discretization and specialized hardware kernels while providing adaptive local coordinate systems. Extensive experiments on UAV benchmarks demonstrate that LF-SSM achieves state-of-the-art performance while running at 69 frames per second (FPS) with only 18.5 M parameters, demonstrating superior efficiency for real-time edge deployment. Full article

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

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36 pages, 4336 KB

Open AccessEditor’s ChoiceReview

UAV Positioning Using GNSS: A Review of the Current Status

by Chaopei Jiang, Xingyu Zhou, Hua Chen and Tianjun Liu

Drones 2026, 10(2), 91; https://doi.org/10.3390/drones10020091 - 28 Jan 2026

Cited by 14 | Viewed by 6430

Abstract

Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by [...] Read more.

Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by UAV platform characteristics and complex low-altitude environments. This paper presents a system-level review of GNSS-based UAV positioning. Instead of treating GNSS in isolation, we first link mission requirements and platform constraints, such as aggressive dynamics and Size, Weight, and Power (SWaP) limitations, to specific positioning challenges. We then critically evaluate the spectrum of GNSS techniques, from standalone and Satellite-Based Augmentation System (SBAS) modes to high-precision carrier-phase methods including Real-Time Kinematic (RTK), Post-Processed Kinematic (PPK), Precise Point Positioning (PPP), and PPP-RTK. Furthermore, we discuss multi-sensor fusion with inertial, visual, and Light Detection and Ranging (LiDAR) sensors to mitigate vulnerabilities in urban canyons and GNSS-denied conditions. Finally, we outline key challenges and future directions, highlighting integrity-aware architectures, Artificial Intelligence (AI)-enhanced signal processing, and multi-layer Positioning, Navigation, and Timing (PNT) concepts. The review provides a structured framework and system-level insights to guide resilient navigation for UAV operations in low-altitude airspace. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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25 pages, 4895 KB

Open AccessEditor’s ChoiceArticle

Drone-Enabled Non-Invasive Ultrasound Method for Rodent Deterrence

by Marija Ratković, Vasilije Kovačević, Matija Marijan, Maksim Kostadinov, Tatjana Miljković and Miloš Bjelić

Drones 2026, 10(2), 84; https://doi.org/10.3390/drones10020084 - 25 Jan 2026

Cited by 2 | Viewed by 2501

Abstract

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of [...] Read more.

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)

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23 pages, 4468 KB

Open AccessEditor’s ChoiceArticle

Fixed-Time Target Tracking and Encirclement Control for Multi-UAVs with Bearing-Only Measurements

by Zican Zhou, Jiangping Hu, Xuesong Wu, Shangzhi Liao and Jiao Yuan

Drones 2026, 10(1), 63; https://doi.org/10.3390/drones10010063 - 15 Jan 2026

Cited by 2 | Viewed by 1494

Abstract

This paper introduces a novel fixed-time control framework for simultaneous target tracking and circumnavigation in a multi-UAV system, using only bearing measurements. The proposed approach enables the UAV swarm to rapidly form and maintain a rigid circular formation around a moving target, with [...] Read more.

This paper introduces a novel fixed-time control framework for simultaneous target tracking and circumnavigation in a multi-UAV system, using only bearing measurements. The proposed approach enables the UAV swarm to rapidly form and maintain a rigid circular formation around a moving target, with continuous tracking and uniform angular spacing between agents. A key innovation is the development of a distributed fixed-time estimator, which allows each UAV to localize the target within a fixed time using only local bearing information and limited inter-agent communication. Building on this estimator, a hierarchical control strategy is designed, where a leader UAV guides the formation while followers achieve and maintain uniform distribution along the orbit. The fixed-time stability of the overall closed-loop system is rigorously established through Lyapunov analysis. Numerical simulations confirm the fixed-time convergence of the algorithm. Compared to an existing asymptotic-convergence benchmark, the proposed approach achieves significantly faster and deterministic convergence, with improved formation accuracy. Full article

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

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30 pages, 4879 KB

Open AccessEditor’s ChoiceArticle

Physical Modeling and Data-Driven Hybrid Control for Quadrotor-Robotic-Arm Cable-Suspended Payload Systems

by Lu Lu, Qihua Xiao, Shikang Zhou, Xinhai Wang and Yunhe Meng

Drones 2026, 10(1), 51; https://doi.org/10.3390/drones10010051 - 10 Jan 2026

Cited by 3 | Viewed by 1217

Abstract

This work investigates a quadrotor equipped with dual-stage robotic arms and a cable-suspended payload, developing a unified methodology for modeling and control. A 10-DOF Lagrangian model captures vehicle-arm-payload coupling through structured mass matrices. A hierarchical control architecture combines SO(3)-based attitude regulation with cooperative [...] Read more.

This work investigates a quadrotor equipped with dual-stage robotic arms and a cable-suspended payload, developing a unified methodology for modeling and control. A 10-DOF Lagrangian model captures vehicle-arm-payload coupling through structured mass matrices. A hierarchical control architecture combines SO(3)-based attitude regulation with cooperative swing compensation via partial feedback linearization, exploiting coupling matrices to distribute control between platform and arm actuators. Model accuracy is enhanced through physics-informed system identification, achieving improved prediction correlation with bounded corrections. Lyapunov analysis establishes semi-global practical stability with explicit robustness bounds. High-fidelity simulations in MuJoCo demonstrate a 40–70% swing reduction compared to PD control across multiple scenarios, with low computational overhead at kHz-level control rates, making it suitable for embedded implementation. The framework provides a theoretical foundation and implementation guidelines for cooperative aerial manipulation systems. Full article

(This article belongs to the Special Issue Advanced Flight Dynamics and Decision-Making for UAV Operations)

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20 pages, 6653 KB

Open AccessEditor’s ChoiceArticle

Design and Experimental Validation of a Tailless Flapping-Wing Micro Aerial Vehicle with Long Endurance and High Payload Capability

by Chaofeng Wu, Yiming Xiao, Jiaxin Zhao, Qingcheng Guo, Feng Cui, Xiaosheng Wu and Wu Liu

Drones 2026, 10(1), 26; https://doi.org/10.3390/drones10010026 - 3 Jan 2026

Viewed by 2633

Abstract

The tailless flapping-wing micro aerial vehicle (FW-MAV) exhibits capabilities for hovering and agile six-degree-of-freedom flight, demonstrating potential for missions in complex environments such as forests and indoor spaces. However, limited payload and endurance restrict their practical application. This study presents a novel tailless [...] Read more.

The tailless flapping-wing micro aerial vehicle (FW-MAV) exhibits capabilities for hovering and agile six-degree-of-freedom flight, demonstrating potential for missions in complex environments such as forests and indoor spaces. However, limited payload and endurance restrict their practical application. This study presents a novel tailless FW-MAV named X-fly, incorporating a lightweight crank-rocker mechanism with high thrust-to-weight ratio. The optimized flapping-wing mechanism achieves a maximum single-side lift of 28.7 gf, with a lift-to-power ratio of 6.67 gf/W, outperforming conventional direct-drive propellers using the same motor. The X-fly employs servo-controlled stroke planes for tailless attitude stabilization and rapid disturbance recovery. It features a 36 cm wingspan and a net weight of 18.9 g (without battery). Using a commercially available 1100 mAh battery weighing 21.6 g, it demonstrates a peak lift-to-weight ratio of 1.42 at 3.8 V and achieves a maximum flight endurance of 33.2 min. When equipped with a 250 mAh battery weighing 5.5 g, it can carry an additional payload equal to its own net weight. The X-fly attains a maximum speed of 6 m/s and demonstrates high agility during forest flight. Furthermore, it successfully performs a simulated reconnaissance mission with an onboard camera, confirming its potential for practical applications. Full article

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

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24 pages, 40856 KB

Open AccessEditor’s ChoiceArticle

UTUAV: A Drone Dataset for Urban Traffic Analysis

by Felipe Lepin, Sergio A. Velastin, Roberto León, Jesús García-Herrero, Gonzalo Rojas-Martínez and Jorge Ernesto Espinosa-Oviedo

Drones 2026, 10(1), 15; https://doi.org/10.3390/drones10010015 - 27 Dec 2025

Cited by 3 | Viewed by 2759

Abstract

Vehicle detection from unmanned aerial vehicles (UAVs) has gained increasing attention due to the growing availability and accessibility of these platforms. UAV-captured videos have proven valuable in a variety of applications, including agriculture, security, and search and rescue operations. To support research in [...] Read more.

Vehicle detection from unmanned aerial vehicles (UAVs) has gained increasing attention due to the growing availability and accessibility of these platforms. UAV-captured videos have proven valuable in a variety of applications, including agriculture, security, and search and rescue operations. To support research in UAV-based vehicle detection, this paper introduces UTUAV: Urban Traffic Unmanned Aerial Vehicle, a dataset composed of traffic video images collected over the streets of Medellín, Colombia. The images are recorded from a semi-static position at two different altitudes (100 and 120 m) and include three manually annotated vehicle types: cars, motorcycles, and large vehicles. The analysis focuses on the main characteristics and challenges presented in the dataset. In particular, data leakage occurs when a single video is used to construct the training, validation, and evaluation sets. An inadequate data split can result in highly similar samples leaking into the evaluation set, leading to inflated performance metrics that do not reflect a model’s true generalization ability. Additionally, baseline results from recent state-of-the-art object detection models based on CNNs and Transformers (YOLOv8, YOLOv11, YOLOv12 and RT-DETR) are presented. The experiments highlight several challenges, including the difficulty of detecting small-scale objects, especially motorcycles, and limited generalization capabilities under altitude changes, a phenomenon commonly referred to as domain shift. Full article

(This article belongs to the Section Innovative Urban Mobility)

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34 pages, 3381 KB

Open AccessEditor’s ChoiceReview

Electric Propulsion and Hybrid Energy Systems for Solar-Powered UAVs: Recent Advances and Challenges

by Norliza Ismail, Nadhiya Liyana Mohd Kamal, Nurhakimah Norhashim, Sabarina Abdul Hamid, Zulhilmy Sahwee and Shahrul Ahmad Shah

Drones 2025, 9(12), 846; https://doi.org/10.3390/drones9120846 - 10 Dec 2025

Cited by 11 | Viewed by 4944

Abstract

Unmanned aerial vehicles (UAVs) are increasingly utilized across civilian and defense sectors due to their versatility, efficiency, and cost-effectiveness. However, their operational endurance remains constrained by limited onboard energy storage. Recent research has focused on electric propulsion systems integrated with hybrid energy sources, [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly utilized across civilian and defense sectors due to their versatility, efficiency, and cost-effectiveness. However, their operational endurance remains constrained by limited onboard energy storage. Recent research has focused on electric propulsion systems integrated with hybrid energy sources, particularly the combination of solar cells and advanced battery technologies to overcome this limitation. This review presents a comprehensive analysis of the latest advancements in electric propulsion architecture, solar-based power integration, and hybrid energy management strategies for UAVs. Key components, including motors, electronic speed controllers (ESCs), propellers, and energy storage systems, are examined alongside emerging technologies such as wireless charging and flexible photovoltaic (PV) materials. Power management techniques, including maximum power point tracking (MPPT) and intelligent energy control algorithms, are also discussed in the context of long-endurance missions. Challenges related to energy density, weight constraints, environmental adaptability, and component integration are highlighted, with insights into potential solutions and future directions. The findings of this review aim to guide the development of efficient, sustainable, and high-endurance UAV platforms leveraging electric-solar hybrid propulsion systems. Full article

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23 pages, 4151 KB

Open AccessEditor’s ChoiceArticle

An Improved Hierarchical Framework for Adaptive UAV Trajectory Planning in Dynamic Target Tracking

by Sen Hu, Zhong Tian, Weiyu Zhu and Bangchu Zhang

Drones 2025, 9(12), 847; https://doi.org/10.3390/drones9120847 - 10 Dec 2025

Viewed by 1433

Abstract

Planning trajectories for unmanned aerial vehicles (UAVs) actively tracking a moving target poses a significant challenge. This paper introduces a motion planning system utilizing UAVs to track a dynamic target actively. The system can handle complex tracking tasks, ensuring safety and dynamic feasibility. [...] Read more.

Planning trajectories for unmanned aerial vehicles (UAVs) actively tracking a moving target poses a significant challenge. This paper introduces a motion planning system utilizing UAVs to track a dynamic target actively. The system can handle complex tracking tasks, ensuring safety and dynamic feasibility. Initially, a front-end path search involves employing adaptive Kinodynamic path searching to identify the route. A heuristic approach is applied to determine an initial trajectory, considering the minimum time and control cost. Subsequently, we introduce the cost of combining optimization and use Euclidean Signed Distance Field (ESDF) gradient information to improve trajectory smoothness and dynamic feasibility. Simulation results confirm the consistent superiority of our proposed method over the Fast-Tracker algorithm, resulting in shorter tracking distance and flight time. Our method achieves smoother trajectories while preserving dynamic feasibility. A real-world single-UAV experiment further validates the effectiveness of our approach. Full article

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

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47 pages, 11727 KB

Open AccessEditor’s ChoiceReview

A Systematic Review of Urban Air Mobility Development: eVTOL Drones’ Technological Challenges and Low-Altitude Policies of Shenzhen

by Jinhong Xu, Chenxi Guan, Yunpeng Wang, Junjie Zhuang and Wenbiao Gan

Drones 2025, 9(12), 842; https://doi.org/10.3390/drones9120842 - 8 Dec 2025

Cited by 13 | Viewed by 8255

Abstract

Urban Air Mobility (UAM) is emerging as a transformative solution to urban traffic congestion and inefficient ground travel. This paper presents the UAM development of Shenzhen, a pioneering city of low-altitude economy in China. It focuses on eVTOL drones for Shenzhen UAM, systematically [...] Read more.

Urban Air Mobility (UAM) is emerging as a transformative solution to urban traffic congestion and inefficient ground travel. This paper presents the UAM development of Shenzhen, a pioneering city of low-altitude economy in China. It focuses on eVTOL drones for Shenzhen UAM, systematically reviewing the technical challenges, policy support, and practical progress. Firstly, the technical status of eVTOL drone design and research is reviewed, and the multidimensional technologies and application bottlenecks faced by eVTOL drones are identified. Secondly, by combining flight safety technology and urban air mobility regulation technology, the systematic technical challenges of urban low-altitude traffic based on eVTOL drones are analyzed. Furthermore, from the perspective of coordinated promotion of infrastructure and regulation, the foundation of urban air mobility applications is clarified, among which efficient flight approval and large-scale construction of takeoff and landing sites across the entire city represent prominent advantages of Shenzhen’s future air mobility. Then, given the high correlation between the systemic technological challenges of urban air mobility and low-altitude economic policies, this paper reveals the complementary relationship between technological challenges and low-altitude policies based on the current status of Shenzhen’s policy promotion and its impact on technology and industry. Finally, the technical issues and regulatory trends faced by eVTOL drones in urban air mobility in Shenzhen are summarized, and combined with the global and Chinese commercial prospects of manned eVTOL drones, suggestions for the future development of urban air mobility in Shenzhen are proposed from the following four dimensions: technology research and development, infrastructure, industrial ecology, and regional coordination. Full article

(This article belongs to the Special Issue Electric-Powered and Hybrid-Powered Unmanned Aerial Vehicle Technology and Applications of Low-Altitude Aviation)

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34 pages, 15573 KB

Open AccessEditor’s ChoiceArticle

A Learning-Based Measurement Validation Approach for Cooperative Multi-UAV Navigation Using Kalman Filtering

by Kenan Can Taşan and Ahmet Akbulut

Drones 2025, 9(12), 834; https://doi.org/10.3390/drones9120834 - 2 Dec 2025

Cited by 1 | Viewed by 1402

Abstract

Reliable navigation in cooperative unmanned aerial vehicle (UAV) networks requires adaptively managing measurement degradations within Kalman-filter-based estimation frameworks. This paper introduces a learning-based Kalman approach for real-time detection of degraded measurements in mesh-network-based multi-UAV navigation. The method incorporates a data-driven pre-filtering module that [...] Read more.

Reliable navigation in cooperative unmanned aerial vehicle (UAV) networks requires adaptively managing measurement degradations within Kalman-filter-based estimation frameworks. This paper introduces a learning-based Kalman approach for real-time detection of degraded measurements in mesh-network-based multi-UAV navigation. The method incorporates a data-driven pre-filtering module that assesses measurement reliability prior to the Kalman update, thereby improving the robustness of the estimation process under communication-induced degradations. Within this approach, four measurement fault detection strategies—Innovation Filter (IF), Deep Q-Network (DQN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM)—were implemented and comparatively evaluated through Monte Carlo simulations combining inertial sensors, time-of-arrival, and Doppler-based inter-agent observations. Additional statistical analyses, including ±1σ error bars and a Wilcoxon rank-sum test, were conducted to verify the significance of the performance differences among the methods. The results show that the proposed approach significantly enhances navigation reliability, particularly under degraded or intermittent GNSS and communication conditions. The MLP-based configuration achieved the best balance between fault-detection accuracy and overall filter consistency. These findings confirm the effectiveness of learning-augmented Kalman filtering architectures for robust and scalable cooperative UAV navigation. Full article

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

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25 pages, 8947 KB

Open AccessEditor’s ChoiceArticle

Advancing Real-Time Aerial Wildfire Detection Through Plume Recognition and Knowledge Distillation

by Pirunthan Keerthinathan, Juan Sandino, Sutharsan Mahendren, Anuraj Uthayasooriyan, Julian Galvez, Grant Hamilton and Felipe Gonzalez

Drones 2025, 9(12), 827; https://doi.org/10.3390/drones9120827 - 28 Nov 2025

Cited by 3 | Viewed by 1556

Abstract

Uncrewed aerial systems (UAS)-based remote sensing and artificial intelligence (AI) analysis enable real-time wildfire or bushfire detection, facilitating early response to minimize damage and protect lives and property. However, their effectiveness is limited by three issues: distinguishing smoke from fog, the high cost [...] Read more.

Uncrewed aerial systems (UAS)-based remote sensing and artificial intelligence (AI) analysis enable real-time wildfire or bushfire detection, facilitating early response to minimize damage and protect lives and property. However, their effectiveness is limited by three issues: distinguishing smoke from fog, the high cost of manual annotation, and the computational demands of large models. This study addresses the three key challenges by introducing plume as a new indicator to better distinguish smoke from similar visual elements, and by employing a hybrid annotation method using knowledge distillation (KD) to reduce expert labour and accelerate labelling. Additionally, it leverages lightweight YOLO Nano models trained with pseudo-labels generated from a fine-tuned teacher network to lower computational demands while maintaining high detection accuracy for real-time wildfire monitoring. Controlled pile burns in Canungra, QLD, Australia, were conducted to collect UAS-captured images over deciduous vegetation, which were subsequently augmented with the Flame2 dataset, which contains wildfire images of coniferous vegetation. A Grounding DINO model, fine-tuned using few-shot learning, served as the teacher network to generate pseudo-labels for a significant portion of the Flame2 dataset. These pseudo-labels were then used to train student networks consisting of YOLO Nano architectures, specifically versions 5, 8, and 11 (YOLOv5n, YOLOv8n, YOLOv11n). The experimental results show that YOLOv8n and YOLOv5n achieved an mAP@0.5 of 0.721. Plume detection outperforms smoke indicators (F1: 76.1–85.7% vs. 70%) in fog and wildfire scenarios. These findings underscore the value of incorporating plume as a distinct class and utilizing KD, both of which enhance detection accuracy and scalability, ultimately supporting more reliable and timelier wildfire monitoring and response. Full article

(This article belongs to the Special Issue Rapid Disaster Assessment and Post-Disaster Recovery Using UAV Remote Sensing)

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30 pages, 34352 KB

Open AccessEditor’s ChoiceReview

Infrared and Visible Image Fusion Techniques for UAVs: A Comprehensive Review

by Junjie Li, Cunzheng Fan, Congyang Ou and Haokui Zhang

Drones 2025, 9(12), 811; https://doi.org/10.3390/drones9120811 - 21 Nov 2025

Cited by 12 | Viewed by 4727

Abstract

Infrared–visible (IR–VIS) image fusion is becoming central to unmanned aerial vehicle (UAV) perception, enabling robust operation across day–night cycles, backlighting, haze or smoke, and large viewpoint or scale changes. However, for practical applications some challenges still remain: visible images are illumination-sensitive; infrared imagery [...] Read more.

Infrared–visible (IR–VIS) image fusion is becoming central to unmanned aerial vehicle (UAV) perception, enabling robust operation across day–night cycles, backlighting, haze or smoke, and large viewpoint or scale changes. However, for practical applications some challenges still remain: visible images are illumination-sensitive; infrared imagery suffers thermal crossover and weak texture; motion and parallax cause cross-modal misalignment; UAV scenes contain many small or fast targets; and onboard platforms face strict latency, power, and bandwidth budgets. Given these UAV-specific challenges and constraints, we provide a UAV-centric synthesis of IR–VIS fusion. We: (i) propose a taxonomy linking data compatibility, fusion mechanisms, and task adaptivity; (ii) critically review learning-based methods—including autoencoders, CNNs, GANs, Transformers, and emerging paradigms; (iii) compare explicit/implicit registration strategies and general-purpose fusion frameworks; and (iv) consolidate datasets and evaluation metrics to reveal UAV-specific gaps. We further identify open challenges in benchmarking, metrics, lightweight design, and integration with downstream detection, segmentation, and tracking, offering guidance for real-world deployment. A continuously updated bibliography and resources are provided and discussed in the main text. Full article

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

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28 pages, 5368 KB

Open AccessEditor’s ChoiceArticle

Dynamic Estimation of Formation Wake Flow Fields Based on On-Board Sensing

by Tianhui Guo, Tielin Ma, Haiqiao Liu, Jingcheng Fu, Bingchen Cheng and Lulu Tao

Drones 2025, 9(11), 798; https://doi.org/10.3390/drones9110798 - 17 Nov 2025

Cited by 2 | Viewed by 1153

Abstract

Close formation flight is a practical strategy for fixed-wing unmanned aerial vehicle (UAV) swarms. Maintaining UAVs at aerodynamically optimal positions is essential for efficient formation flight. However, aerodynamic optimization methods based on computational fluid dynamics (CFD) are computationally intensive and difficult to apply [...] Read more.

Close formation flight is a practical strategy for fixed-wing unmanned aerial vehicle (UAV) swarms. Maintaining UAVs at aerodynamically optimal positions is essential for efficient formation flight. However, aerodynamic optimization methods based on computational fluid dynamics (CFD) are computationally intensive and difficult to apply in real time for large-scale formations. Inspired by bio-formation flight, this study proposes an on-board sensing-based method for wake flow field estimation, with potential for extension to complex formations. The method is based on a parameter identification-induced velocity model (PI-Model), which uses only onboard sensors, including two lateral air data systems (ADS), to sample the wake field. By minimizing the residual of the induced velocity, the model identifies key parameters of the wake and provides a dynamic estimation of the wake velocity field. Comparisons between the PI-Model and CFD simulations show that it achieves higher accuracy than the widely used single horseshoe vortex model in both wake velocity and aerodynamic effects. Applied to a two-UAV formation scenario, CFD validation confirms that the trailing UAV achieves a 15–25% drag reduction. These results verify the effectiveness of the proposed method for formation flight and demonstrate its potential for application in complex, dynamic multi-UAV formations. Full article

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

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26 pages, 5139 KB

Open AccessEditor’s ChoiceArticle

Towards Scalable Intelligence: A Low-Complexity Multi-Agent Soft Actor–Critic for Large-Model-Driven UAV Swarms

by Zhaoyu Liu, Wenchu Cheng, Liang Zeng and Xinxin He

Drones 2025, 9(11), 788; https://doi.org/10.3390/drones9110788 - 12 Nov 2025

Cited by 3 | Viewed by 1718

Abstract

Heterogeneous unmanned aerial vehicle (UAV) swarms are becoming critical components of next-generation non-terrestrial networks, enabling tasks such as communication relay, spectrum monitoring, cooperative sensing, and navigation. Yet, their heterogeneity and multifunctionality bring severe challenges in task allocation and resource scheduling, where traditional multi-agent [...] Read more.

Heterogeneous unmanned aerial vehicle (UAV) swarms are becoming critical components of next-generation non-terrestrial networks, enabling tasks such as communication relay, spectrum monitoring, cooperative sensing, and navigation. Yet, their heterogeneity and multifunctionality bring severe challenges in task allocation and resource scheduling, where traditional multi-agent reinforcement learning methods often suffer from high algorithmic complexity, lengthy training times, and deployment difficulties on resource-constrained nodes. To address these issues, this paper proposes a low-complexity multi-agent soft actor–critic (MASAC) framework that combines parameter sharing (shared actor with device embeddings and shared-backbone twin critics), lightweight network design (fixed-width residual MLP with normalization), and robust training mechanisms (minimum-bias twin-critic updates and entropy scheduling) within the CTDE paradigm. Simulation results show that the proposed framework achieves more than 14-fold parameter compression and over a 93% reduction in training time, while maintaining or improving performance in terms of the delay–energy utility function. These advances substantially reduce computational overhead and accelerate convergence, providing a practical pathway for deploying multi-agent reinforcement learning in large-scale heterogeneous UAV clusters and supporting diverse mission scenarios under stringent resource and latency constraints. Full article

(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)

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19 pages, 13859 KB

Open AccessEditor’s ChoiceArticle

Hybrid CFD-Deep Learning Approach for Urban Wind Flow Predictions and Risk-Aware UAV Path Planning

by Gonzalo Veiga-Piñeiro, Enrique Aldao-Pensado and Elena Martín-Ortega

Drones 2025, 9(11), 791; https://doi.org/10.3390/drones9110791 - 12 Nov 2025

Cited by 7 | Viewed by 2566

Abstract

We present a CFD-driven surrogate modeling framework that integrates a Convolutional Autoencoder (CAE) with a Deep Neural Network (DNN) for the rapid prediction of urban wind environments and their subsequent use in UAV trajectory planning. A Reynolds-Averaged Navier–Stokes (RANS) CFD database is generated, [...] Read more.

We present a CFD-driven surrogate modeling framework that integrates a Convolutional Autoencoder (CAE) with a Deep Neural Network (DNN) for the rapid prediction of urban wind environments and their subsequent use in UAV trajectory planning. A Reynolds-Averaged Navier–Stokes (RANS) CFD database is generated, parameterized by boundary-condition descriptors, to train the surrogate for velocity magnitude and turbulent kinetic energy (TKE). The CAE compresses horizontal flow fields into a low-dimensional latent space, providing an efficient representation of complex flow structures. The DNN establishes a mapping from input descriptors to the latent space, and flow reconstructions are obtained through the frozen decoder. Validation against CFD demonstrates that the surrogate captures velocity gradients and TKE distributions with mean absolute errors below 1% in most of the domain, while residual discrepancies remain confined to near-wall regions. The approach yields a computational speed-up of approximately 4000× relative to CFD, enabling deployment on embedded or edge hardware. For path planning, the domain is discretized as a k-Non-Aligned Nearest Neighbors (k-NANN) graph, and an A* search algorithm incorporates heading constraints and surrogate-based TKE thresholds. The integrated pipeline produces turbulence-aware, dynamically feasible trajectories, advancing the integration of high-fidelity flow predictions into urban air mobility decision frameworks. Full article

(This article belongs to the Special Issue Computational Fluid Dynamics for Next-Generation Unmanned Aerial Vehicles)

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49 pages, 3395 KB

Open AccessEditor’s ChoiceReview

Underwater Drone-Enabled Wireless Communication Systems for Smart Marine Communications: A Study of Enabling Technologies, Opportunities, and Challenges

by Sarun Duangsuwan and Katanyoo Klubsuwan

Drones 2025, 9(11), 784; https://doi.org/10.3390/drones9110784 - 11 Nov 2025

Cited by 8 | Viewed by 5604

Abstract

Underwater drones such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are revolutionizing underwater operations and are essential for advanced marine applications like environmental monitoring, deep-sea exploration, and marine surveillance. In this paper, we concentrate on the enabling technologies and wireless [...] Read more.

Underwater drones such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are revolutionizing underwater operations and are essential for advanced marine applications like environmental monitoring, deep-sea exploration, and marine surveillance. In this paper, we concentrate on the enabling technologies and wireless communication strategies for underwater drones. Specifically, we analyze acoustic, optical, and radio frequency (RF) approaches, along with their respective advantages and disadvantages. We investigate the potential of integrating underwater drone-enabled wireless communication systems for smart marine communications. The study highlights the benefits of combining acoustic, optical, and RF methods to improve connectivity and data reliability. A hybrid underwater communication system is ideal for underwater drones because it can reduce latency, increase data throughput, and improve adaptability under various underwater conditions, supporting smart marine communications. The future direction involves developing hybrid communication frameworks that incorporate the Internet of Underwater Things (IoUT), AI-driven data, virtual reality (VR), and digital twin (DT) technologies, enabling a next-generation smart marine ecosystem. Full article

(This article belongs to the Special Issue Advancing Space-Air-Ground-Sea Integrated Networks in Drones: Techniques, Applications, and Future Directions)

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31 pages, 2633 KB

Open AccessEditor’s ChoiceArticle

Thinking Like an Expert: Aligning LLM Thought Processes for Automated Safety Modeling of High-Altitude Solar Drones

by Qingran Su, Xingze Li, Yuming Ren, Bing Fu, Chunming Hu and Yongfeng Yin

Drones 2025, 9(11), 780; https://doi.org/10.3390/drones9110780 - 9 Nov 2025

Viewed by 1984

Abstract

As the application of high-altitude solar drones expands, ensuring their safety is paramount. Traditional safety modeling, which relies on manual expert analysis, struggles to keep pace with rapid development cycles. While Large Language Models (LLMs) offer a path to automation, state-of-the-art reasoning frameworks [...] Read more.

As the application of high-altitude solar drones expands, ensuring their safety is paramount. Traditional safety modeling, which relies on manual expert analysis, struggles to keep pace with rapid development cycles. While Large Language Models (LLMs) offer a path to automation, state-of-the-art reasoning frameworks like Graph of Thoughts (GoT) are too generic, lacking the domain-specific knowledge required for effective application. To address this gap, we introduce K-EGoT, a framework that grounds LLM reasoning in a verifiable, domain-specific knowledge base. Our method introduces a “Safety Rationale”—a mandatory, auditable link between LLM-generated model extensions and expert-curated safety principles. We then train a specialized model using a novel “thought process alignment” strategy, applying Direct Preference Optimization (DPO) to the quality of these rationales to ensure the model’sreasoning aligns with expert logic. On a high-fidelity dataset for the flight control–energy coupling problem, our 7B K-EGoT model achieved a Safety Extension Score (SES) of 92.7, significantly outperforming the 84.7 score from standard GoT prompting. Our work delivers a reliable and auditable solution for automated safety modeling for this critical class of drones. Full article

(This article belongs to the Special Issue Design and Flight Control of Low-Speed Near-Space Unmanned Systems)

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30 pages, 14021 KB

Open AccessEditor’s ChoiceArticle

LLM-LCSA: LLM for Collaborative Control and Decision Optimization in UAV Cluster Security

by Hua Song, Zheng Yang, Haitao Du, Yuting Zhang, Jie Zeng and Xinxin He

Drones 2025, 9(11), 779; https://doi.org/10.3390/drones9110779 - 9 Nov 2025

Cited by 4 | Viewed by 4521

Abstract

With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent [...] Read more.

With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent architectures have limitations when addressing new threats, such as insufficient real-time response capabilities. To address these issues, this paper presnts an LLM-layered collaborative security architecture (LLM-LCSA) for multimachine collaborative security. This architecture optimizes the spatiotemporal fusion efficiency of multisource asynchronous data through cloud–edge–end collaborative deployment, combining an end lightweight LLM, an edge medium LLM, and a cloud-based foundation LLM. Additionally, a Mixture of Experts (MoEs) intelligent algorithm that dynamically activates the most relevant expert models by leveraging a threat–expert association matrix is introduced, thereby increasing the accuracy of complex threat identification and dynamic adaptability. Moreover, a resource-aware multi-objective optimization model is constructed to generate optimal decisions under resource constraints. Simulation results indicate that compared with traditional methods, LLM-LCSA achieves an average 7.92% improvement in the threat detection accuracy, reduces the system’s total response time by 44.52%, and enables resource scheduling during off-peak periods. This architecture provides an efficient, intelligent, and scalable solution for secure collaboration among UAV swarms. Future research should further explore its application potential in 6G network integration and large-scale swarm environments. Full article

(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)

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22 pages, 5996 KB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Machine Learning Algorithms for Object-Based Crop Classification Using Multispectral Imagery

by Madjebi Collela Be, Antsa Sarobidy Randrianantenaina, James E. Kanneh, Yingchun Han, Yaping Lei, Xiaoyu Zhi, Shiwu Xiong, Yahui Jiao, Shilong Shang, Yunzhen Ma, Beifang Yang, Lin Tao and Yabing Li

Drones 2025, 9(11), 763; https://doi.org/10.3390/drones9110763 - 5 Nov 2025

Cited by 4 | Viewed by 2564

Abstract

Unmanned Aerial Vehicles (UAVs) offer enhanced spatial and temporal resolution for agricultural remote sensing, surpassing traditional satellite-based methods. Given the abundance of evolving machine-learning methods for crop recognition, this study evaluates and compares five machine learning algorithms (ML) and tests an Ensemble Learning [...] Read more.

Unmanned Aerial Vehicles (UAVs) offer enhanced spatial and temporal resolution for agricultural remote sensing, surpassing traditional satellite-based methods. Given the abundance of evolving machine-learning methods for crop recognition, this study evaluates and compares five machine learning algorithms (ML) and tests an Ensemble Learning method as a sixth approach, integrated with object-based image analysis (OBIA) for crop-type classification using UAV multispectral imagery, aiming to identify the most effective model and produce a classification map based on the best-performing method. Image segmentation was built using eCognition software, and spectral, index, and gray level co-occurrence matrix (GLCM) features were extracted from the segmented object. A machine learning model integrating multiple classification algorithms (SVM, ANN, RF, XGBoost, KNN, Ensemble Learning) with automated hyperparameter optimization was developed and executed in Google Colab using Python 3.10. All classifiers achieved accuracies exceeding 80% and Area Under the Curve (AUC) values above 0.9. SVM and ANN are the best classifiers, with the same value of accuracy (94%), followed by XGBoost (93%), RF (92%), and KNN (89%). The Ensemble Learning method (SVM + ANN) as a sixth approach outperformed all single models, with an accuracy value of 95%. Cotton, maize, peanut, and soybean were classified with the highest accuracy, with index and GLCM features contributing most significantly, followed by spectral features. The integration of high-resolution UAV imagery with ML and OBIA demonstrates strong potential for automated crop-type classification, offering valuable support for precision agriculture applications. Full article

(This article belongs to the Special Issue UAS in Smart Agriculture: 2nd Edition)

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25 pages, 5362 KB

Open AccessEditor’s ChoiceArticle

Task Planning and Optimization for Multi-Region Multi-UAV Cooperative Inspection

by Yangyilei Xiong, Haoyu Tian, Jianing Tang, Jie Jin and Xiaoning Shen

Drones 2025, 9(11), 762; https://doi.org/10.3390/drones9110762 - 4 Nov 2025

Cited by 7 | Viewed by 1689

Abstract

To improve the efficiency of multi-region multi-unmanned aerial vehicle (UAV) inspection, this paper proposes a composite task planning strategy integrating the K-Means++ genetic algorithm (KMGA) and the multi-neighborhood iterative dynamic programming (MNIDP) method. Firstly, the multi-region multi-UAV inspection problem is modeled as a [...] Read more.

To improve the efficiency of multi-region multi-unmanned aerial vehicle (UAV) inspection, this paper proposes a composite task planning strategy integrating the K-Means++ genetic algorithm (KMGA) and the multi-neighborhood iterative dynamic programming (MNIDP) method. Firstly, the multi-region multi-UAV inspection problem is modeled as a multiple traveling salesmen problem with neighborhoods (MTSPN). Then, this problem is decomposed into two interrelated subproblems to mitigate the complexity inherent in the solution process: that is, the multiple traveling salesmen problem (MTSP) and multi-neighborhoods path planning (MNPP) problem. Based on this decomposition, the MTSP is solved by the KMGA by converting it into m spatially non-overlapping traveling salesmen problems (TSPs) and then these TSPs are solved to obtain the approximate optimal visiting sequences for the nodes in each TSP in a short time. Subsequently, the MNPP can be efficiently solved by an MNIDP which plans the paths between the corresponding neighborhood of each node based on the node visiting sequences, thus obtaining the approximate optimal path length of the MTSPN. The simulation results demonstrate that the proposed composite strategy exhibits advantages in computational efficiency and optimal path length. Specifically, compared to the baseline algorithm, the average tour length obtained by the KMGA decreased by 23.24%. Meanwhile, the average path lengths computed by MNIDP in three instances were reduced from 8.00% to 11.41% and from 6.46% to 10.08% compared to two baseline algorithms, respectively. It provides an efficient task and path planning solution for multi-region multi-UAV operations in power transmission line inspections, thereby enhancing inspection efficiency. Full article

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

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Open AccessEditor’s ChoiceArticle

Modelling, Design, and Control of a Central Motor Driving Reconfigurable Quadcopter

by Zhuhuan Wu, Ke Huang and Jiaying Zhang

Drones 2025, 9(11), 736; https://doi.org/10.3390/drones9110736 - 23 Oct 2025

Cited by 3 | Viewed by 1989

Abstract

Constrained by fixed frame dimensions, conventional drones usually demonstrate insufficient capabilities to accommodate complex environments. However, the reconfigurable drone can address this limitation through its deformable frame equipped with actuators or passive interaction mechanisms. Nevertheless, these additional components may introduce an excessive weight [...] Read more.

Constrained by fixed frame dimensions, conventional drones usually demonstrate insufficient capabilities to accommodate complex environments. However, the reconfigurable drone can address this limitation through its deformable frame equipped with actuators or passive interaction mechanisms. Nevertheless, these additional components may introduce an excessive weight burden, which conflicts with the lightweight objective in aircraft design. In this work, we propose a novel reconfigurable quadrotor inspired by the swimming morphology of jellyfish, with only one actuator placed at the centre of the frame to achieve significant morphological reconfiguration. In the design of the morphing mechanism, three telescopic sleeves are driven by the actuator, enabling arms’ rotation to achieve a maximum projected area reduction of 55%. The nested design of sleeves ensures a sufficient morphing range while maintaining structural compactness in the fully deployed mode. Furthermore, key structural dimensions are optimized, reducing the central motor load by up to 65% across configurations. After deriving parameter variations during morphing, Proportion–Integration–Differentiation (PID) controllers are implemented and flight simulations are conducted in MATLAB. Results confirm the drone’s sustained controllability during and after reconfiguration, with an “8”-shaped trajectory tracking root mean square error (RMSE) of 0.109 m and successful traversal through long narrow slits, reducing mission duration under certain conditions. Full article

(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)

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