Drones

21 pages, 5074 KB

Open AccessArticle

Asynchronous Tilt Transition Control of Quad Tilt Rotor UAV

by Xuebing Li, Zikang Su, Xin Chen, Changhui Jiang and Mi Hou

Drones 2026, 10(1), 76; https://doi.org/10.3390/drones10010076 - 22 Jan 2026

Viewed by 118

To address the challenges inherent in the transition flight control of QTR UAVs, this paper proposes an asynchronous tilt transition control framework that integrates NDIC with an ESO. First, a heterogeneous control allocation strategy is introduced to coordinate the rotors and aerodynamic surfaces, [...] Read more.

To address the challenges inherent in the transition flight control of QTR UAVs, this paper proposes an asynchronous tilt transition control framework that integrates NDIC with an ESO. First, a heterogeneous control allocation strategy is introduced to coordinate the rotors and aerodynamic surfaces, thereby maintaining consistent matching between control demands and actuator capabilities. Furthermore, compared with the synchronous tilt strategy, the proposed asynchronous tilt strategy improves pitch moment balance and forward acceleration capability, thereby enhancing robustness against CG variations and extending the achievable forward acceleration range. Finally, based on the asynchronous tilt transition strategy, a transition flight control method combining NDIC with ESO is presented to achieve precise transition control performance under the lumped disturbances. The simulation results demonstrate that the proposed tilt method achieves a safe and smooth transition, satisfies dynamic performance requirements, and exhibits strong robustness and high control accuracy. Full article

► Show Figures

Figure 1

30 pages, 2666 KB

Open AccessSystematic Review

Harnessing Regenerative Agriculture, Unmanned Aerial Systems, and AI for Sustainable Cocoa Farming in West Africa

by Andrew Manu, Jeff Dacosta Osei, Vincent Kodjo Avornyo, Thomas Lawler and Kwame Agyei Frimpong

Drones 2026, 10(1), 75; https://doi.org/10.3390/drones10010075 - 22 Jan 2026

Viewed by 194

Abstract

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can [...] Read more.

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article

(This article belongs to the Special Issue Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI) and Drone Technology)

► Show Figures

Graphical abstract

22 pages, 38941 KB

Open AccessArticle

Fusion Framework of Remote Sensing and Electromagnetic Scattering Features of Drones for Monitoring Freighters

by Zeyang Zhou and Jun Huang

Drones 2026, 10(1), 74; https://doi.org/10.3390/drones10010074 - 22 Jan 2026

Viewed by 177

Abstract

Certain types of unmanned aerial vehicles (UAVs) represent convenient platforms for remote sensing observation as well as low-altitude targets that are themselves monitored by other devices. In order to study remote sensing grayscale and radar cross-section (RCS) in an example drone, we present [...] Read more.

Certain types of unmanned aerial vehicles (UAVs) represent convenient platforms for remote sensing observation as well as low-altitude targets that are themselves monitored by other devices. In order to study remote sensing grayscale and radar cross-section (RCS) in an example drone, we present a fusion framework based on remote sensing imaging and electromagnetic scattering calculations. The results indicate that the quadcopter drone shows weak visual effects in remote sensing grayscale images while exhibiting strong dynamic electromagnetic scattering features that can exceed 29.6815 dBm² fluctuations. The average and peak RCS of the example UAV are higher than those of the quadcopter in the given cases. The example freighter exhibits the most intuitive grayscale features and the largest RCS mean under the given observation conditions, with a peak of 51.6186 dBm². Compared to the UAV, the small boat with a sharp bow design has similar dimensions while exhibiting lower RCS features and intuitive remote sensing grayscale. Under cross-scale conditions, grayscale imaging is beneficial for monitoring UAVs, freighters, and other nearby boats. Dynamic RCS features and grayscale local magnification are suitable for locating and recognizing drones. The established approach is effective in learning remote sensing grayscale and electromagnetic scattering features of drones used for observing freighters. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) Applications in Critical Industrial Sectors)

► Show Figures

Figure 1

35 pages, 10558 KB

Open AccessArticle

Cave of Altamira (Spain): UAV-Based SLAM Mapping, Digital Twin and Segmentation-Driven Crack Detection for Preventive Conservation in Paleolithic Rock-Art Environments

by Jorge Angás, Manuel Bea, Carlos Valladares, Cristian Iranzo, Gonzalo Ruiz, Pilar Fatás, Carmen de las Heras, Miguel Ángel Sánchez-Carro, Viola Bruschi, Alfredo Prada and Lucía M. Díaz-González

Drones 2026, 10(1), 73; https://doi.org/10.3390/drones10010073 - 22 Jan 2026

Viewed by 140

Abstract

The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on [...] Read more.

The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained on manually segmented images to explore automated crack detection under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts. Full article

(This article belongs to the Topic 3D Documentation of Natural and Cultural Heritage)

► Show Figures

Figure 1

22 pages, 9985 KB

Open AccessArticle

A Comparative Analysis of Multi-Spectral and RGB-Acquired UAV Data for Cropland Mapping in Smallholder Farms

by Evania Chetty, Maqsooda Mahomed and Shaeden Gokool

Drones 2026, 10(1), 72; https://doi.org/10.3390/drones10010072 - 21 Jan 2026

Viewed by 163

Abstract

Accurate cropland classification within smallholder farming systems is essential for effective land management, efficient resource allocation, and informed agricultural decision-making. This study evaluates cropland classification performance using Red, Green, Blue (RGB) and multi-spectral (blue, green, red, red-edge, near-infrared) unmanned aerial vehicle (UAV) imagery. [...] Read more.

Accurate cropland classification within smallholder farming systems is essential for effective land management, efficient resource allocation, and informed agricultural decision-making. This study evaluates cropland classification performance using Red, Green, Blue (RGB) and multi-spectral (blue, green, red, red-edge, near-infrared) unmanned aerial vehicle (UAV) imagery. Both datasets were derived from imagery acquired using a MicaSense Altum sensor mounted on a DJI Matrice 300 UAV. Cropland classification was performed using machine learning algorithms implemented within the Google Earth Engine (GEE) platform, applying both a non-binary classification of five land cover classes and a binary classification within a probabilistic framework to distinguishing cropland from non-cropland areas. The results indicate that multi-spectral imagery achieved higher classification accuracy than RGB imagery for non-binary classification, with overall accuracies of 75% and 68%, respectively. For binary cropland classification, RGB imagery achieved an area under the receiver operating characteristic curve (AUC–ROC) of 0.75, compared to 0.77 for multi-spectral imagery. These findings suggest that, while multi-spectral data provides improved classification performance, RGB imagery can achieve comparable accuracy for fundamental cropland delineation. This study contributes baseline evidence on the relative performance of RGB and multi-spectral UAV imagery for cropland mapping in heterogeneous smallholder farming landscapes and supports further investigation of RGB-based approaches in resource-constrained agricultural contexts. Full article

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

► Show Figures

Figure 1

21 pages, 2142 KB

Open AccessArticle

Real-Life ISO 15189 Qualification of Long-Range Drone Transportation of Medical Biological Samples: Results from a Clinical Trial

by Baptiste Demey, Olivier Bury, Morgane Choquet, Julie Fontaine, Myriam Dollerschell, Hugo Thorel, Charlotte Durand-Maugard, Olivier Leroy, Mathieu Pecquet, Annelise Voyer, Gautier Dhaussy and Sandrine Castelain

Drones 2026, 10(1), 71; https://doi.org/10.3390/drones10010071 - 21 Jan 2026

Viewed by 167

Abstract

Controlling pre-analytical conditions for medical biology tests, particularly during transport, is crucial for complying with the ISO 15189 standard and ensuring high-quality medical services. The use of drones, also known as unmanned aerial vehicles, to transport clinical samples is growing in scale, but [...] Read more.

Controlling pre-analytical conditions for medical biology tests, particularly during transport, is crucial for complying with the ISO 15189 standard and ensuring high-quality medical services. The use of drones, also known as unmanned aerial vehicles, to transport clinical samples is growing in scale, but requires prior validation to verify that there is no negative impact on the test results provided to doctors. This study aimed to establish a secure, high-quality solution for transporting biological samples by drone in a coastal region of France. The 80 km routes passed over several densely populated urban areas, with take-off and landing points within hospital grounds. The analytical and clinical impact of this mode of transport was compared according to two protocols: an interventional clinical trial on 30 volunteers compared to the reference transport by car, and an observational study on samples from 126 hospitalized patients compared to no transport. The system enabled samples to be transported without damage by maintaining freezing, refrigerated, and room temperatures throughout the flight, without any significant gain in travel time. Analytical variations were observed for sodium, folate, GGT, and platelet levels, with no clinical impact on the interpretation of the results. There is a risk of time-dependent alterations of blood glucose measurements in heparin tubes, which can be corrected by using fluoride tubes. This demonstrated the feasibility and security of transporting biological samples over long distances in line with the ISO 15189 standard. Controlling transport times remains crucial to assessing the quality of analyses. It is imperative to devise contingency plans for backup solutions to ensure the continuity of transportation in the event of inclement weather. Full article

(This article belongs to the Special Issue Recent Advances in Healthcare Applications of Drones)

► Show Figures

Figure 1

33 pages, 32306 KB

Open AccessArticle

A Reward-and-Punishment-Aware Incentive Mechanism for Directed Acyclic Graph Blockchain-Based Federated Learning in Unmanned Aerial Vehicle Networks

by Xiaofeng Xue, Qiong Li and Haokun Mao

Drones 2026, 10(1), 70; https://doi.org/10.3390/drones10010070 - 21 Jan 2026

Viewed by 137

Abstract

The integration of unmanned aerial vehicles (UAVs) and Federated Learning (FL) enables distributed model training while preserving data privacy. To overcome the challenges caused by centralized and synchronous model updates, we integrate Directed Acyclic Graph (DAG) blockchain-based FL into UAV networks. In this [...] Read more.

The integration of unmanned aerial vehicles (UAVs) and Federated Learning (FL) enables distributed model training while preserving data privacy. To overcome the challenges caused by centralized and synchronous model updates, we integrate Directed Acyclic Graph (DAG) blockchain-based FL into UAV networks. In this decentralized and asynchronous framework, UAVs can independently and autonomously participate in the FL process according to their own requirement. To achieve the high FL performance, it is essential for UAVs to actively contribute their computational and data resources to the FL process. However, it is challenging to ensure that UAVs consistently contribute their resources, as they may have a propensity to prioritize their own self-interest. Therefore, it is crucial to design effective incentive mechanisms that encourage UAVs to actively participate in the FL process and contribute their computational and data resources. Currently, research on effective incentive mechanisms for DAG blockchain-based FL framework in UAV networks remains limited. To address these challenges, this paper proposes a novel incentive mechanism that integrates both rewards and punishments to encourage UAVs to actively contribute to FL and to deter free riding under incomplete information. We formulate the interactions among UAVs as an evolutionary game, and the aspiration-driven rule is employed to imitate the UAV’s decision-making processes. We evaluate the proposed mechanism for UAVs within a DAG blockchain-based FL framework. Experimental results show that the proposed incentive mechanism substantially increases the average UAV contribution rate from

77 . 04_{\pm 0.84} %

(without incentive mechanism) to

97 . 48_{\pm 1.29} %

. Furthermore, the higher contribution rate results in an approximate 2.23% improvement in FL performance. Additionally, we evaluate the impact of different parameter configurations to analyze how they affect the performance and efficiency of the FL system. Full article

(This article belongs to the Section Drone Communications)

► Show Figures

Figure 1

38 pages, 6647 KB

Open AccessArticle

ST-DCL: A Spatio-Temporally Decoupled Cooperative Localization Method for Dynamic Drone Swarms

by Hao Wu, Zhangsong Shi, Zhonghong Wu, Huihui Xu and Zhiyong Tu

Drones 2026, 10(1), 69; https://doi.org/10.3390/drones10010069 - 20 Jan 2026

Viewed by 179

Abstract

In GPS-denied environments, the spatio-temporal coupling of errors caused by dynamic network topologies poses a fundamental challenge to cooperative localization, presenting existing methods with a dilemma: approaches pursuing global optimization lack dynamic adaptability, while those focusing on local adaptation struggle to guarantee global [...] Read more.

In GPS-denied environments, the spatio-temporal coupling of errors caused by dynamic network topologies poses a fundamental challenge to cooperative localization, presenting existing methods with a dilemma: approaches pursuing global optimization lack dynamic adaptability, while those focusing on local adaptation struggle to guarantee global convergence. To address this challenge, this paper proposes ST-DCL, a cooperative localization framework based on a novel principle of closed-loop spatio-temporal decoupling. The core of ST-DCL comprises two modules: a Dynamic Weighted Multidimensional Scaling (DW-MDS) optimizer, responsible for providing a globally consistent coarse estimate with provable convergence, and a specially designed Spatio-Temporal Graph Neural Network (ST-GNN) corrector, tasked with compensating for local nonlinear errors. The DW-MDS effectively suppresses interference from historical errors via an adaptive sliding window and confidence weights derived from our error propagation model. The key innovation of the ST-GNN lies in its two newly designed components: a Dynamic Topological Attention Module for actively modulating neighbor aggregation to inhibit spatial error diffusion, and a Dilated Causal Convolution Module for modeling long-term temporal dependencies to curb error accumulation. These two modules form a closed loop via a confidence feedback mechanism, working in synergy to achieve continuous error suppression. Theoretical analysis indicates that the framework exhibits bounded-error convergence under dynamic topologies. In simulations involving 200 nodes, velocities up to 50 m/s, and 15% NLOS links, the ST-DCL achieves a normalized root mean square error (NRMSE) of 0.0068, representing a 21% performance improvement over state-of-the-art methods. The practical efficacy and real-time capability are further validated through real-world flight experiments with a 10-UAV swarm in complex, GPS-denied scenarios. Full article

(This article belongs to the Section Drone Communications)

► Show Figures

Figure 1

28 pages, 17346 KB

Open AccessArticle

Cascaded ADRC Framework for Robust Control of Coaxial UAVs with Uncertainties and Disturbances

by Can Cui, Zi’an Wang, Miao Wang and Chao Xu

Drones 2026, 10(1), 68; https://doi.org/10.3390/drones10010068 - 20 Jan 2026

Viewed by 152

Abstract

Coaxial contra-rotor unmanned aerial vehicles (UAVs) are attractive for their compact structure and aerodynamic efficiency, making them suitable for long-endurance and heavy-payload operations. However, the coaxial configuration introduces strong rotor coupling, phase lag, and additional disturbances, which pose significant challenges for stable control. [...] Read more.

Coaxial contra-rotor unmanned aerial vehicles (UAVs) are attractive for their compact structure and aerodynamic efficiency, making them suitable for long-endurance and heavy-payload operations. However, the coaxial configuration introduces strong rotor coupling, phase lag, and additional disturbances, which pose significant challenges for stable control. To overcome these issues, we propose a cascaded Active Disturbance Rejection Control (ADRC-C) framework, in which a two-level control structure is adopted. The outer loop employs a classical ADRC controller to estimate and compensate for the lumped external forces, providing the compensated attitude command to the inner loop. The inner loop, in turn, adopts an

S O (3)

-based Extended State Observer (ESO) to handle high-frequency torque disturbances through real-time estimation and compensation. The proposed approach is validated through numerical simulations. Results confirm that the cascaded ADRC consistently outperforms conventional PID control in tracking accuracy, transient response, and disturbance rejection, demonstrating strong robustness for demanding coaxial UAV missions. Full article

► Show Figures

Figure 1

23 pages, 54360 KB

Open AccessArticle

ATM-Net: A Lightweight Multimodal Fusion Network for Real-Time UAV-Based Object Detection

by Jiawei Chen, Junyu Huang, Zuye Zhang, Jinxin Yang, Zhifeng Wu and Renbo Luo

Drones 2026, 10(1), 67; https://doi.org/10.3390/drones10010067 - 20 Jan 2026

Viewed by 170

Abstract

UAV-based object detection faces critical challenges including extreme scale variations (targets occupy 0.1–2% image area), bird’s-eye view complexities, and all-weather operational demands. Single RGB sensors degrade under poor illumination while infrared sensors lack spatial details. We propose ATM-Net, a lightweight multimodal RGB–infrared fusion [...] Read more.

UAV-based object detection faces critical challenges including extreme scale variations (targets occupy 0.1–2% image area), bird’s-eye view complexities, and all-weather operational demands. Single RGB sensors degrade under poor illumination while infrared sensors lack spatial details. We propose ATM-Net, a lightweight multimodal RGB–infrared fusion network for robust UAV vehicle detection. ATM-Net integrates three innovations: (1) Asymmetric Recurrent Fusion Module (ARFM) performs “extraction→fusion→separation” cycles across pyramid levels, balancing cross-modal collaboration and modality independence. (2) Tri-Dimensional Attention (TDA) recalibrates features through orthogonal Channel-Width, Height-Channel, and Height-Width branches, enabling comprehensive multi-dimensional feature enhancement. (3) Multi-scale Adaptive Feature Pyramid Network (MAFPN) constructs enhanced representations via bidirectional flow and multi-path aggregation. Experiments on VEDAI and DroneVehicle datasets demonstrate superior performance—92.4% mAP50 and 64.7% mAP50-95 on VEDAI, 83.7% mAP on DroneVehicle—with only 4.83M parameters. ATM-Net achieves optimal accuracy–efficiency balance for resource-constrained UAV edge platforms. Full article

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

► Show Figures

Figure 1

26 pages, 2427 KB

Open AccessArticle

Alternating Optimization-Based Joint Power and Phase Design for RIS-Empowered FANETs

by Muhammad Shoaib Ayub, Renata Lopes Rosa and Insoo Koo

Drones 2026, 10(1), 66; https://doi.org/10.3390/drones10010066 - 19 Jan 2026

Viewed by 176

Abstract

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS [...] Read more.

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications. Full article

(This article belongs to the Special Issue Drone-Centric Optical Wireless Networks: Innovations in 6G, AI, and Hybrid Communication Systems)

► Show Figures

Figure 1

22 pages, 2025 KB

Open AccessArticle

Vision-Based Unmanned Aerial Vehicle Swarm Cooperation and Online Point-Cloud Registration for Global Localization in Global Navigation Satellite System-Intermittent Environments

by Gonzalo Garcia and Azim Eskandarian

Drones 2026, 10(1), 65; https://doi.org/10.3390/drones10010065 - 19 Jan 2026

Viewed by 242

Abstract

Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAVs use biologically inspired swarm behaviors together with online monocular point-cloud [...] Read more.

Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAVs use biologically inspired swarm behaviors together with online monocular point-cloud registration to achieve real-time global localization. First, we apply a passive-perception strategy, bird-inspired drone swarm-keeping, enabling each UAV to estimate the relative motion and proximity of its neighbors using only monocular visual cues. This decentralized mechanism provides cohesive and collision-free group motion without GNSS, active ranging, or explicit communication. Second, we integrate this capability with a cooperative mapping pipeline in which one or more drones acting as global anchors generate a globally referenced monocular SLAM map. Vehicles lacking global positioning progressively align their locally generated point clouds to this shared global reference using an iterative registration strategy, allowing them to infer consistent global poses online. Other autonomous vehicles optionally contribute complementary viewpoints, but UAVs remain the core autonomous agents driving both mapping and coordination due to their privileged visual perspective. Experimental validation in simulation and indoor testbeds with drones demonstrates that the integrated system maintains swarm cohesion, improves spatial alignment by more than a factor of four over baseline monocular SLAM, and preserves reliable global localization throughout extended GNSS outages. The results highlight a scalable, lightweight, and vision-based approach to resilient UAV autonomy in tunnels, industrial environments, and other GNSS-challenged settings. Full article

► Show Figures

Figure 1

23 pages, 698 KB

Open AccessArticle

A Hamiltonian Neural Differential Dynamics Model and Control Framework for Autonomous Obstacle Avoidance in a Quadrotor Subject to Model Uncertainty

by Xu Wang, Yanfang Liu, Desong Du, Huarui Xu and Naiming Qi

Drones 2026, 10(1), 64; https://doi.org/10.3390/drones10010064 - 19 Jan 2026

Viewed by 178

Abstract

Establishing precise and reliable quadrotor dynamics model is crucial for safe and stable tracking control in obstacle environments. However, obtaining such models is challenging, as it requires precise inertia identification and accounting for complex aerodynamic effects, which handcrafted models struggle to do. To [...] Read more.

Establishing precise and reliable quadrotor dynamics model is crucial for safe and stable tracking control in obstacle environments. However, obtaining such models is challenging, as it requires precise inertia identification and accounting for complex aerodynamic effects, which handcrafted models struggle to do. To address this, this paper proposes a safety-critical control framework built on a Hamiltonian neural differential model (HDM). The HDM formulates the quadrotor dynamics under a Hamiltonian structure over the

SE (3)

manifold, with explicitly optimizable inertia parameters and a neural network-approximated control input matrix. This yields a neural ordinary differential equation (ODE) that is solved numerically for state prediction, while all parameters are trained jointly from data via gradient descent. Unlike black-box models, the HDM incorporates physical priors—such as

SE (3)

constraints and energy conservation—ensuring a physically plausible and interpretable dynamics representation. Furthermore, the HDM is reformulated into a control-affine form, enabling controller synthesis via control Lyapunov functions (CLFs) for stability and exponential control barrier functions (ECBFs) for rigorous safety guarantees. Simulations validate the framework’s effectiveness in achieving safe and stable tracking control. Full article

(This article belongs to the Special Issue Control, Planning, and Embodied Intelligence of Agile UAVs in Indoor Environments)

► Show Figures

Figure 1

23 pages, 4468 KB

Open AccessArticle

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

Viewed by 230

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))

► Show Figures

Figure 1

27 pages, 7771 KB

Open AccessReview

Advances in Folding-Wing Flying Underwater Drone (FUD) Technology

by Jianqiu Tu, Junjie Zhuang, Haixin Chen, Changjian Zhao, Hairui Zhang and Wenbiao Gan

Drones 2026, 10(1), 62; https://doi.org/10.3390/drones10010062 - 15 Jan 2026

Viewed by 373

Abstract

The evolution of modern warfare and civil exploration requires platforms that can operate seamlessly across the air–water interface. The folding-wing Hybrid Air and Underwater Vehicle (FUD) has emerged as a transformative solution, combining the high-speed cruising capabilities of fixed-wing aircraft with the stealth [...] Read more.

The evolution of modern warfare and civil exploration requires platforms that can operate seamlessly across the air–water interface. The folding-wing Hybrid Air and Underwater Vehicle (FUD) has emerged as a transformative solution, combining the high-speed cruising capabilities of fixed-wing aircraft with the stealth characteristics of underwater navigation. This review thoroughly analyzes the advancements and challenges in folding-wing FUD technology. The discussion is framed around four interconnected pillars: the overall design driven by morphing technology, adaptation of the propulsion system, multi-phase dynamic modeling and control, and experimental verification. The paper systematically compares existing technical pathways, including lateral and longitudinal folding mechanisms, as well as dual-use and hybrid propulsion strategies. The analysis indicates that, although significant progress has been made with prototypes demonstrating the ability to transition between air and water, core challenges persist. These challenges include underwater endurance, structural reliability under impact loads, and effective integration of the power system. Additionally, this paper explores promising application scenarios in both military and civilian domains, discussing future development trends that focus on intelligence, integration, and clustering. This review not only consolidates the current state of technology but also emphasizes the necessity for interdisciplinary approaches. By combining advanced materials, computational intelligence, and robust control systems, we can overcome existing barriers to progress. In conclusion, FUD technology is moving from conceptual validation to practical engineering applications, positioning itself to become a crucial asset in future cross-domain operations. Full article

(This article belongs to the Special Issue Advances in Autonomous Underwater Drones: 2nd Edition)

► Show Figures

Figure 1

26 pages, 9482 KB

Open AccessArticle

Can Environmental Analysis Algorithms Be Improved by Data Fusion and Soil Removal for UAV-Based Buffel Grass Biomass Prediction?

by Wagner Martins dos Santos, Alexandre Maniçoba da Rosa Ferraz Jardim, Lady Daiane Costa de Sousa Martins, Márcia Bruna Marim de Moura, Elania Freire da Silva, Luciana Sandra Bastos de Souza, Alan Cezar Bezerra, José Raliuson Inácio Silva, Ênio Farias de França e Silva, João L. M. P. de Lima, Leonor Patricia Cerdeira Morellato and Thieres George Freire da Silva

Drones 2026, 10(1), 61; https://doi.org/10.3390/drones10010061 - 15 Jan 2026

Viewed by 239

Abstract

The growing demand for sustainable livestock systems requires efficient methods for monitoring forage biomass. This study evaluated spectral (RGB and multispectral), textural (GLCM), and area attributes derived from unmanned aerial vehicle (UAV) imagery to predict buffelgrass (Cenchrus ciliaris L.) biomass, also testing [...] Read more.

The growing demand for sustainable livestock systems requires efficient methods for monitoring forage biomass. This study evaluated spectral (RGB and multispectral), textural (GLCM), and area attributes derived from unmanned aerial vehicle (UAV) imagery to predict buffelgrass (Cenchrus ciliaris L.) biomass, also testing the effect of soil pixel removal. A comprehensive machine learning pipeline (12 algorithms and 6 feature selection methods) was applied to 14 data combinations. Our results demonstrated that soil removal consistently improved the performance of the applied models. Multispectral (MSI) sensors were the most robust individually, whereas textural (GLCM) attributes did not contribute significantly. Although the MSI and RGB data combination proved complementary, the model with the highest accuracy was obtained with CatBoost using only RGB information after Boruta feature selection, achieving a CCC of 0.83, RMSE of 0.214 kg, and R² of 0.81 in the test set. The most important variable was vegetation cover area (19.94%), surpassing spectral indices. We conclude that integrating RGB UAVs with robust processing can generate accessible and effective tools for forage monitoring. This approach can support pasture management by optimizing stocking rates, enhancing natural resource efficiency, and supporting data-driven decisions in precision silvopastoral systems. Full article

(This article belongs to the Special Issue Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications)

► Show Figures

Figure 1

32 pages, 13734 KB

Open AccessArticle

Objective Programming Partitions and Rule-Based Spanning Tree for UAV Swarm Regional Coverage Path Planning

by Bangrong Ruan, Tian Jing, Meigen Huang, Xi Ning, Jiarui Wang, Boquan Zhang and Fengyao Zhi

Drones 2026, 10(1), 60; https://doi.org/10.3390/drones10010060 - 14 Jan 2026

Viewed by 234

Abstract

To address the problem of regional coverage path planning for unmanned aerial vehicle swarms (UAVs), this study proposes an algorithm based on objective programming partitions (OPP) and rule-based spanning tree coverage (RSTC). Aiming at the shortcomings of the traditional Divide Areas based on [...] Read more.

To address the problem of regional coverage path planning for unmanned aerial vehicle swarms (UAVs), this study proposes an algorithm based on objective programming partitions (OPP) and rule-based spanning tree coverage (RSTC). Aiming at the shortcomings of the traditional Divide Areas based on Robots Initial Positions combined with Spanning Tree Coverage (DARP-STC) algorithm in two core stages, that is, region partitions and spanning tree generation, the proposed algorithm conducts a targeted design and optimization, respectively. In the region partition stage, an objective programming and 0–1 integer programming model are adopted to realize the balanced allocation of UAVs’ task regions. In the spanning tree generation stage, a rule is designed to construct a spanning tree of coverage paths and is proven to achieve the minimum number of turns for the UAV under certain conditions. Both simulations and physical experiments demonstrate that the proposed algorithm can not only significantly reduce the number of turns of UAVs but also enhance the efficiency and coverage degree of tasks for UAV swarms. Full article

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

► Show Figures

Figure 1

23 pages, 8315 KB

Open AccessArticle

Dubins-Aware NCO: Learning SE(2)-Equivariant Representations for Heading-Constrained UAV Routing

by Jiazhan Gao, Yutian Wu, Liruizhi Jia, Heng Shi and Jihong Zhu

Drones 2026, 10(1), 59; https://doi.org/10.3390/drones10010059 - 14 Jan 2026

Viewed by 241

Abstract

The nonholonomic constraints of fixed-wing UAVs, characterized by coupled heading-curvature feasibility and asymmetric costs, fundamentally deviate from classical Euclidean routing assumptions. While standard neural combinatorial optimization (NCO) architectures could theoretically incorporate Dubins costs via reward signals, such naive adaptations lack the capacity to [...] Read more.

The nonholonomic constraints of fixed-wing UAVs, characterized by coupled heading-curvature feasibility and asymmetric costs, fundamentally deviate from classical Euclidean routing assumptions. While standard neural combinatorial optimization (NCO) architectures could theoretically incorporate Dubins costs via reward signals, such naive adaptations lack the capacity to explicitly model the intrinsic SE(2) geometric invariance and directional asymmetry of fixed-wing motion, leading to suboptimal generalization. To bridge this gap, we propose a Dubins-Aware NCO framework. We design a dual-channel embedding to decouple asymmetric physical distances from rotation-stable geometric features. Furthermore, we introduce a Rotary Phase Encoding (RoPhE) mechanism that theoretically guarantees strict SO(2) equivariance within the attention layer. Extensive sensitivity, ablation, and cross-distribution generalization experiments are conducted on tasks spanning varying turning radii and problem variants with instance scales of 10, 20, 36, and 52 nodes. The results consistently validate the superior optimality and stability of our approach compared with state-of-the-art DRL and NCO baselines, while maintaining significant computational efficiency advantages over classical heuristics. Our results highlight the importance of explicitly embedding geometry-physics consistency, rather than relying on scalar reward signals, for real-world fixed-wing autonomous scheduling. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

► Show Figures

Figure 1

29 pages, 2810 KB

Open AccessArticle

PAIR: A Hybrid A^* with PPO Path Planner for Multi-UAV Navigation in 2-D Dynamic Urban MEC Environments

by Bahaa Hussein Taher, Juan Luo, Ying Qiao and Hussein Ridha Sayegh

Drones 2026, 10(1), 58; https://doi.org/10.3390/drones10010058 - 13 Jan 2026

Viewed by 237

Abstract

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm [...] Read more.

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible

100 \times 100

grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

► Show Figures

Figure 1

24 pages, 3434 KB

Open AccessArticle

Hierarchical Route Planning Framework and MMDQN Agent-Based Intelligent Obstacle Avoidance for UAVs

by Boyu Dong, Yuzhen Zhang, Peiyuan Yuan, Shuntong Lu, Tao Huang and Gong Zhang

Drones 2026, 10(1), 57; https://doi.org/10.3390/drones10010057 - 13 Jan 2026

Viewed by 314

Abstract

Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in complex mountainous environments is studied. To deal [...] Read more.

Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in complex mountainous environments is studied. To deal with this issue, a hierarchical route planning framework is designed, including global route planning and AI-based local route re-planning using deep reinforcement learning, exhibiting both flexible versatility and practical coordination and deployment efficiency. Throughout the entire flight, the local route re-planning task triggered by dynamic threats can be executed in real time. Meanwhile, a multi-model DQN (MMDQN) agent with a Monte Carlo traversal iterative learning (MCTIL) strategy is designed for local route re-planning. Compared to existing methods, this agent can be directly used to generate local obstacle avoidance routes in various scenarios at any time during the flight, which simplifies the complicated structure and training process of conventional deep reinforcement learning (DRL) agents in dynamic, complex environments. Using the framework structure and MMDQN agent for local route re-planning ensures the safety and efficiency of the mission, as well as local obstacle avoidance during global flights. These performances are verified through simulations based on actual terrain data. Full article

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

► Show Figures

Figure 1

28 pages, 31378 KB

Open AccessArticle

Real-Time UAV Flight Path Prediction Using GRU Networks for Autonomous Site Assessment

by Yared Bitew Kebede, Ming-Der Yang, Henok Desalegn Shikur and Hsin-Hung Tseng

Drones 2026, 10(1), 56; https://doi.org/10.3390/drones10010056 - 13 Jan 2026

Viewed by 503

Abstract

Unmanned Aerial Vehicles (UAVs) have become essential tools across critical domains, including infrastructure inspection, public safety monitoring, traffic surveillance, environmental sensing, and target tracking, owing to their ability to collect high-resolution spatial data rapidly. However, maintaining stable and accurate flight trajectories remains a [...] Read more.

Unmanned Aerial Vehicles (UAVs) have become essential tools across critical domains, including infrastructure inspection, public safety monitoring, traffic surveillance, environmental sensing, and target tracking, owing to their ability to collect high-resolution spatial data rapidly. However, maintaining stable and accurate flight trajectories remains a significant challenge, particularly during autonomous missions in dynamic or uncertain environments. This study presents a novel flight path prediction framework based on Gated Recurrent Units (GRUs), designed for both single-step and multi-step-ahead forecasting of four-dimensional UAV coordinates, Easting (X), Northing (Y), Altitude (Z), and Time (T), using historical sensor flight data. Model performance was systematically validated against traditional Recurrent Neural Network architectures. On unseen test data, the GRU model demonstrated enhanced predictive accuracy in single-step prediction, achieving a MAE of 0.0036, Root Mean Square Error (RMSE) of 0.0054, and a (R²) of 0.9923. Crucially, in multi-step-ahead forecasting designed to simulate real-world challenges such as GPS outages, the GRU model maintained exceptional stability and low error, confirming its resilience to error accumulation. The findings establish that the GRU-based model is a highly accurate, computationally efficient, and reliable solution for UAV trajectory forecasting. This framework enhances autonomous navigation and directly supports the data integrity required for high-fidelity photogrammetric mapping, ensuring reliable site assessment in complex and dynamic environments. Full article

► Show Figures

Figure 1

55 pages, 1599 KB

Open AccessReview

The Survey of Evolutionary Deep Learning-Based UAV Intelligent Power Inspection

by Shanshan Fan and Bin Cao

Drones 2026, 10(1), 55; https://doi.org/10.3390/drones10010055 - 12 Jan 2026

Viewed by 402

Abstract

With the rapid development of the power Internet of Things (IoT), the traditional manual inspection mode can no longer meet the growing demand for power equipment inspection. Unmanned aerial vehicle (UAV) intelligent inspection technology, with its efficient and flexible features, has become the [...] Read more.

With the rapid development of the power Internet of Things (IoT), the traditional manual inspection mode can no longer meet the growing demand for power equipment inspection. Unmanned aerial vehicle (UAV) intelligent inspection technology, with its efficient and flexible features, has become the mainstream solution. The rapid development of computer vision and deep learning (DL) has significantly improved the accuracy and efficiency of UAV intelligent inspection systems for power equipment. However, mainstream deep learning models have complex structures, and manual design is time-consuming and labor-intensive. In addition, the images collected during the power inspection process by UAVs have problems such as complex backgrounds, uneven lighting, and significant differences in object sizes, which require expert DL domain knowledge and many trial-and-error experiments to design models suitable for application scenarios involving power inspection with UAVs. In response to these difficult problems, evolutionary computation (EC) technology has demonstrated unique advantages in simulating the natural evolutionary process. This technology can independently design lightweight and high-precision deep learning models by automatically optimizing the network structure and hyperparameters. Therefore, this review summarizes the development of evolutionary deep learning (EDL) technology and provides a reference for applying EDL in object detection models used in UAV intelligent power inspection systems. First, the application status of DL-based object detection models in power inspection is reviewed. Then, how EDL technology improves the performance of the models in challenging scenarios such as complex terrain and extreme weather is analyzed by optimizing the network architecture. Finally, the challenges and future research directions of EDL technology in the field of UAV power inspection are discussed, including key issues such as improving the environmental adaptability of the model and reducing computing energy consumption, providing theoretical references for promoting the development of UAV power inspection technology to a higher level. Full article

► Show Figures

Figure 1

33 pages, 3113 KB

Open AccessArticle

Hierarchical Role-Based Multi-Agent Reinforcement Learning for UHF Radiation Source Localization with Heterogeneous UAV Swarms

by Yuanqiang Sun, Xueqing Zhang, Menglin Wang, Yangqiang Yang, Tao Xia, Xuan Zhu and Tonghe Cui

Drones 2026, 10(1), 54; https://doi.org/10.3390/drones10010054 - 12 Jan 2026

Viewed by 244

Abstract

With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, [...] Read more.

With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, rapid advances in UAV technology have spurred exploration of UAV-based electromagnetic spectrum monitoring as a novel approach. However, the limited payload capacity and endurance of UAVs constrain their monitoring capabilities. To address these challenges, we propose HMUDRL, a distributed heterogeneous multi-agent deep reinforcement learning algorithm. By leveraging cooperative operation between cluster-head UAVs (CH) and cluster-monitoring UAVs (CM) within a heterogeneous UAV swarm, HMUDRL enables high-precision detection and wide-area localization of UHF radiation source. Furthermore, we integrate a minimum-gap localization algorithm that exploits the spatial distribution of multiple CM to accurately pinpoint anomalous radiation sources. Simulation results validate the effectiveness of HMUDRL: in the later stages of training, the success rate of localizing target radiation sources converges to 96.1%, representing an average improvement of 1.8% over baseline algorithms; localization accuracy, measured by root mean square error (RMSE), is enhanced by approximately 87.3% compared to baselines; and communication overhead is reduced by more than 80% relative to homogeneous architectures. These results demonstrate that HMUDRL effectively addresses the challenges of data transmission control and sensing-localization performance faced by UAVs in UHF spectrum monitoring. Full article

(This article belongs to the Special Issue Cooperative Perception, Planning, and Control of Heterogeneous UAVs)

► Show Figures

Figure 1

33 pages, 70283 KB

Open AccessArticle

Satellite-Aided Multi-UAV Secure Collaborative Localization via Spatio-Temporal Anomaly Detection and Diagnosis

by Jianxiong Pan, Qiaolin Ouyang, Zhenmin Lin, Tucheng Hao, Wenyue Li, Xiangming Li and Neng Ye

Drones 2026, 10(1), 53; https://doi.org/10.3390/drones10010053 - 12 Jan 2026

Viewed by 260

Abstract

Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity [...] Read more.

Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity threats make these systems vulnerable to false data injection attacks. Most existing detection approaches focus only on temporal dependencies in time-frequency features and lack diagnostic mechanisms for identifying malicious UAVs, which limits their ability to effectively detect and mitigate such attacks. To address this issue, this paper proposes an intelligent collaborative localization framework that safeguards localization integrity by identifying and correcting false ranging information from malicious UAVs. The framework captures spatio-temporal correlations in multidimensional ranging sequences through a graph attention network (GAT) coupled with a time-attention-based variational autoencoder (VAE) to detect anomalies through anomalous distribution patterns. Malicious UAVs are further diagnosed through an anomaly scoring mechanism based on statistical analysis and reconstruction errors, while detected anomalies are corrected via a K-nearest neighbor-based (KNN) algorithm to enhance localization performance. Simulation results show that the proposed model improves localization accuracy by 25.9%, demonstrating the effectiveness of spatial–temporal feature extraction in securing collaborative localization. Full article

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

► Show Figures

Figure 1

16 pages, 5921 KB

Open AccessArticle

Shipborne Stabilization Grasping Low-Altitude Drones Method for UAV-Assisted Landing Dock Stations

by Chuande Liu, Le Zhang, Chenghao Zhang, Jing Lian, Huan Wang and Bingtuan Gao

Drones 2026, 10(1), 52; https://doi.org/10.3390/drones10010052 - 12 Jan 2026

Viewed by 235

Abstract

Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for [...] Read more.

Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for single UAV guidance and flight attitude control systems to meet the growing demand for landing assistance. In this work, we present a shipborne manipulator arm designed to grasp drones that use low-altitude visual servo technology for landing on the water surface. The shipborne manipulator arm is fabricated as a key component of a seaplane drone dock comprising a ship-type embedded drone storage, a packaged helistop for power transfer and UAV recovery, and a multi-degree-of-freedom arm integrated with multi-source information sensors for the treatment of air-to-water-related airplane crashes. Dynamic model tests have demonstrated that the end-effector of the shipborne manipulator arm stabilizes and performs optimally for water surface disturbances. A down-to-top grasp docking paradigm for a UAV-assisted perching on a shipborne helistop that enables the charging components of the station system to be equipped automatically to ensure that the drone performs its mission in the best condition is also presented. The surface grasp experiments have verified the efficacy of this grasp paradigm when compared to the traditional autonomous landing method. Full article

(This article belongs to the Special Issue Cross-Modal Autonomous Cooperation for Intelligent Unmanned Systems)

► Show Figures

Figure 1

30 pages, 4879 KB

Open AccessArticle

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

Viewed by 296

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)

► Show Figures

Figure 1

18 pages, 10127 KB

Open AccessArticle

A Monitoring Method for Steep Slopes in Mountainous Canyon Regions Using Multi-Temporal UAV POT Technology Assisted by TLS

by Qing-Wen Wen, Zhi-Yu Li, Zhong-Hua Jiang, Hao Wu, Jia-Wen Zhou, Nan Jiang, Yu-Xiang Hu and Hai-Bo Li

Drones 2026, 10(1), 50; https://doi.org/10.3390/drones10010050 - 10 Jan 2026

Viewed by 178

Abstract

Monitoring steep slopes in mountainous canyon areas has always been a challenging problem, especially during the construction of large hydropower projects. Effective monitoring is crucial for construction safety and operational security. However, under complex terrain conditions, existing monitoring methods have significant limitations and [...] Read more.

Monitoring steep slopes in mountainous canyon areas has always been a challenging problem, especially during the construction of large hydropower projects. Effective monitoring is crucial for construction safety and operational security. However, under complex terrain conditions, existing monitoring methods have significant limitations and cannot comprehensively and accurately cover steep slopes. To address the above challenges, this study proposes a multi-temporal UAV-based photogrammetric offset tracking (POT) monitoring method assisted by terrestrial laser scanning (TLS), which is primarily applicable to rocky and texture-rich steep slopes. This method utilizes TLS point cloud data to provide supplementary ground control points (TLS-GCPs) for UAV image modeling, effectively overcoming the difficulty of deploying conventional RTK ground control points (RTK-GCPs) on high and steep slopes, thereby significantly improving the accuracy of UAV-based Structure-from-Motion (SfM) models. In a case study at a hydropower station, we employed TLS-assisted UAV modeling to produce high-precision UAV images. Using POT technology, we successfully identified signs of slope deformation between January 2024 and December 2024. Comparative experiments with traditional algorithms demonstrated that in areas where RTK-GCPs cannot be deployed, this method greatly enhances UAV modeling accuracy, fully meeting the monitoring requirements for steep slopes in complex terrains. Full article

► Show Figures

Figure 1

30 pages, 5328 KB

Open AccessArticle

DTVIRM-Swarm: A Distributed and Tightly Integrated Visual-Inertial-UWB-Magnetic System for Anchor Free Swarm Cooperative Localization

by Xincan Luo, Xueyu Du, Shuai Yue, Yunxiao Lv, Lilian Zhang, Xiaofeng He, Wenqi Wu and Jun Mao

Drones 2026, 10(1), 49; https://doi.org/10.3390/drones10010049 - 9 Jan 2026

Viewed by 346

Abstract

Accurate Unmanned Aerial Vehicle (UAV) positioning is vital for swarm cooperation. However, this remains challenging in situations where Global Navigation Satellite System (GNSS) and other external infrastructures are unavailable. To address this challenge, we propose to use only the onboard Microelectromechanical System Inertial [...] Read more.

Accurate Unmanned Aerial Vehicle (UAV) positioning is vital for swarm cooperation. However, this remains challenging in situations where Global Navigation Satellite System (GNSS) and other external infrastructures are unavailable. To address this challenge, we propose to use only the onboard Microelectromechanical System Inertial Measurement Unit (MIMU), Magnetic sensor, Monocular camera and Ultra-Wideband (UWB) device to construct a distributed and anchor-free cooperative localization system by tightly fusing the measurements. As the onboard UWB measurements under dynamic motion conditions are noisy and discontinuous, we propose an adaptive adjustment method based on chi-squared detection to effectively filter out inconsistent and false ranging information. Moreover, we introduce the pose-only theory to model the visual measurement, which improves the efficiency and accuracy for visual-inertial processing. A sliding window Extended Kalman Filter (EKF) is constructed to tightly fuse all the measurements, which is capable of working under UWB or visual deprived conditions. Additionally, a novel Multidimensional Scaling-MAP (MDS-MAP) initialization method fuses ranging, MIMU, and geomagnetic data to solve the non-convex optimization problem in ranging-aided Simultaneous Localization and Mapping (SLAM), ensuring fast and accurate swarm absolute pose initialization. To overcome the state consistency challenge inherent in the distributed cooperative structure, we model not only the UWB noisy uncertainty but also the neighbor agent’s position uncertainty in the measurement model. Furthermore, we incorporate the Covariance Intersection (CI) method into our UWB measurement fusion process to address the challenge of unknown correlations between state estimates from different UAVs, ensuring consistent and robust state estimation. To validate the effectiveness of the proposed methods, we have established both simulation and hardware test platforms. The proposed method is compared with state-of-the-art (SOTA) UAV localization approaches designed for GNSS-challenged environments. Extensive experiments demonstrate that our algorithm achieves superior positioning accuracy, higher computing efficiency and better robustness. Moreover, even when vision loss causes other methods to fail, our proposed method continues to operate effectively. Full article

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

► Show Figures

Figure 1

22 pages, 899 KB

Open AccessArticle

Rapid MRTA in Large UAV Swarms Based on Topological Graph Construction in Obstacle Environments

by Jinlong Liu, Zexu Zhang, Shan Wen, Jingzong Liu and Kai Zhang

Drones 2026, 10(1), 48; https://doi.org/10.3390/drones10010048 - 9 Jan 2026

Viewed by 231

Abstract

In large-scale Unmanned Aerial Vehicle (UAV) and task environments—particularly those involving obstacles—dimensional explosion remains a significant challenge in Multi-Robot Task Allocation (MRTA). To this end, a novel heuristic MRTA framework based on Topological Graph Construction (TGC) is proposed. First, the physical map is [...] Read more.

In large-scale Unmanned Aerial Vehicle (UAV) and task environments—particularly those involving obstacles—dimensional explosion remains a significant challenge in Multi-Robot Task Allocation (MRTA). To this end, a novel heuristic MRTA framework based on Topological Graph Construction (TGC) is proposed. First, the physical map is transformed into a pixel map, from which a Generalized Voronoi Graph (GVG) is generated by extracting clearance points, which is then used to construct the topological graph of the obstacle environment. Next, the affiliations of UAVs and tasks within the topological graph are determined to partition different topological regions, and the task value of each topological node is calculated, followed by the first-phase Task Allocation (TA) on these topological nodes. Finally, UAVs within the same topological region with their allocated tasks perform a local second-phase TA and generate the final TA result. The simulation experiments analyze the influence of different pixel resolutions on the performance of the proposed method. Subsequently, robustness experiments under localization noise, path cost noise, and communication delays demonstrate that the total benefit achieved by the proposed method remains relatively stable, while the computational time is moderately affected. Moreover, comparative experiments and statistical analyses were conducted against k-means clustering-based MRTA methods in different UAV, task, and obstacle scale environments. The results show that the proposed method improves computational speed while maintaining solution quality, with the PI-based method achieving speedups of over 60 times and the CBBA-based method over 10 times compared with the baseline method. Full article

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

► Show Figures

Figure 1

34 pages, 1599 KB

Open AccessArticle

Disturbance-Resilient Path-Following for Unmanned Airships via Curvature-Aware LOS Guidance and Super-Twisting Terminal Sliding-Mode Control

by Rongwei Liang, Duc Thien An Nguyen and Mostafa Hassanalian

Drones 2026, 10(1), 47; https://doi.org/10.3390/drones10010047 - 9 Jan 2026

Viewed by 243

Abstract

Unmanned airships are highly sensitive to parametric uncertainty, persistent wind disturbances, and sensor noise, all of which compromise reliable path-following. Classical control schemes often suffer from chattering and fail to handle index discontinuities on closed-loop paths due to the lack of mechanisms and [...] Read more.

Unmanned airships are highly sensitive to parametric uncertainty, persistent wind disturbances, and sensor noise, all of which compromise reliable path-following. Classical control schemes often suffer from chattering and fail to handle index discontinuities on closed-loop paths due to the lack of mechanisms and cannot simultaneously provide formal guarantees on state constraint satisfaction. We address these challenges by developing a unified, constraint-aware guidance and control framework for path-following in uncertain environments. The architecture integrates an extended state observer (ESO) to estimate and compensate lumped disturbances, a barrier Lyapunov function (BLF) to enforce state constraints on tracking errors, and a super-twisting terminal sliding-mode (ST-TSMC) control law to achieve finite-time convergence with continuous, low-chatter control inputs. A constructive Lyapunov-based synthesis is presented to derive the control law and to prove that all tracking errors remain within prescribed error bounds. At the guidance level, a nonlinear curvature-aware line-of-sight (CALOS) strategy with an index-increment mechanism mitigates jump phenomena at loop-closure and segment-transition points on closed yet discontinuous paths. The overall framework is evaluated against representative baseline methods under combined wind and parametric perturbations. Numerical results indicate improved path-following accuracy, smoother control signals, and strict enforcement of state constraints, yielding a disturbance-resilient path-following solution for the cruise of an unmanned airship. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Drones, Volume 10, Issue 1 (January 2026) – 76 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI