Drones, Volume 9, Issue 6 (June 2025) – 65 articles

This paper proposes a distance estimation error reduction framework to improve ground node localization accuracy in urban environments using an unmanned aerial vehicle (UAV) and path loss measurements. The primary goal of the framework is to bound distance estimation errors arising from inherent inaccuracies in path loss measurements. A k-means clustering algorithm is first applied to identify the region in which the ground node is located. Then, an analytical approach is used to select UAV waypoints. Moreover, a mean-based exponential smoothing approach is employed to refine the path loss measurements of the selected waypoints to mitigate the effects of multipath components that introduce significant errors in distance estimation. Finally, two estimators, maximum likelihood (ML)-based and semidefinite programming (SDP)-based relaxation, are employed to estimate the ground node’s location, validating the effectiveness of the proposed framework. Evaluations using ray tracing simulation data demonstrate a notable improvement in localization accuracy. The proposed framework effectively bounds the distance estimation errors and significantly reduces overall localization errors compared to conventional unbounded methods. Moreover, both estimators with the proposed framework achieve comparable localization accuracy, highlighting the framework’s capability to address key challenges in ML-based localization. Full article

Street crime is a critical public concern, attracting wide social and research attention. Conventional solutions to reduce street crimes are dispatching more police force in patrol and installing more cameras for street surveillance, which, however, suffer from huge manpower and financial consumption and limited performance. Inspired by the wide application of Unmanned Aerial Vehicles (UAVs) in policing and other related missions such as street surveillance, we investigate the use of UAVs in patrolling along high-risk streets to deter street crimes. UAVs significantly outperform police officers and street cameras in terms of cost reduction and deterring performance improvement. Technically, this paper proposes a data-driven framework to schedule the patrol UAVs, including an online patrol path planning module and an offline UAV station siting module. In the first module, the street-level deterring effect of the UAVs is estimated using a prediction-enhanced method, which guides the UAVs to patrol the high-risk streets more efficiently. Evolved from the path planning algorithm, the second module utilizes a data-driven method to estimate the deterring effect of the candidate UAV stations with different numbers of UAVs. Then both the location of the UAV stations and the UAVs at each station are determined. The proposed framework is comprehensively evaluated using a 6-year crime dataset of the Denver city. The results show that the proposed framework improves the deterring effect by 58.49% on average, and up to 157.32% in extreme cases compared to baselines. Full article

As urban populations expand and congestion intensifies, traditional ground transportation struggles to satisfy escalating mobility demands. Unmanned Electric Vertical Take-Off and Landing (eVTOL) aircraft, as a key enabler of Urban Air Mobility (UAM), leverage low-altitude airspace to alleviate ground traffic while offering environmentally sustainable solutions. However, supporting high bandwidth, real-time video applications, such as Virtual Reality (VR), Augmented Reality (AR), and 360° streaming, remains a major challenge, particularly within bandwidth-constrained metropolitan regions. This study proposes a novel Unmanned Aerial Vehicle (UAV)-enabled video streaming architecture that integrates 6G wireless technologies with intelligent routing strategies across cooperative airborne nodes, including unmanned eVTOLs and High-Altitude Platform Systems (HAPS). By relaying video data from low-congestion ground base stations to high-demand urban zones via autonomous aerial relays, the proposed system enhances spectrum utilization and improves streaming stability. Simulation results validate the framework’s capability to support immersive media applications in next-generation autonomous air mobility systems, aligning with the vision of scalable, resilient 3D transportation infrastructure. Full article

SmallUnmanned Aerial Systems (sUAS) have seen rapid adoption thanks to advances in endurance, communications, autonomy, and manufacturing costs, yet most testing remains focused on GPS-supported, above-ground operations. This study introduces new test methodologies and presents comprehensive experimental evaluations of collision tolerance, navigation, and trajectory following for commercial sUAS platforms in GPS-denied indoor environments. We also propose numerical and categorical metrics—based on established vehicle collision protocols such as the Modified Acceleration Severity Index (MASI) and Maximum Delta V (MDV)—to quantify collision resilience; for example, the tested platforms achieved an average MASI of 0.1 g, while demonstrating clear separation between the highest- and lowest-performing systems. The experimental results revealed that performance varied significantly with mission complexity, obstacle proximity, and trajectory requirements, identifying platforms best suited for subterranean or crowded indoor applications. By aggregating these metrics, users can select the optimal drone for their specific mission requirements in challenging enclosed spaces. Full article

This paper addresses the challenge of ineffective coordination between terminal maneuvering and precision strike capabilities in hypersonic unmanned aerial vehicles (UAVs). To resolve this issue, an intelligent spiral maneuver trajectory generation method utilizing a virtual trajectory framework is proposed. Initially, a relative motion model between the UAV and the virtual center of mass (VCM) is established based on the geometric principles of the Archimedean spiral. Subsequently, the interaction dynamics between the VCM and the target are formulated as a Markov decision process (MDP). A deep reinforcement learning (DRL) approach, employing the proximal policy optimization (PPO) algorithm, is implemented to train a policy network capable of end-to-end virtual trajectory generation. Ultimately, the relative spiral motion is superimposed onto the generated virtual trajectory to synthesize a composite spiral maneuvering trajectory. The simulation results demonstrate that the proposed method achieves expansive spiral maneuvering ranges while ensuring precise target strikes. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for collaborative search missions in complex environments. However, in the presence of interference, communication disruptions between UAVs and ground control stations can severely degrade coordination efficiency, leading to prolonged search times and reduced mission success rates. To address these challenges, this paper proposes a novel multi-agent deep reinforcement learning (MADRL) framework for joint spectrum and search collaboration in multi-UAV systems. The core problem is formulated as a combinatorial optimization task that simultaneously optimizes channel selection and heading angles to minimize the total search time under dynamic interference conditions. Due to the NP-hard nature of this problem, we decompose it into two interconnected Markov decision processes (MDPs): a spectrum collaboration subproblem solved using a received signal strength indicator (RSSI)-aware multi-agent proximal policy optimization (MAPPO) algorithm and a search collaboration subproblem addressed through a target probability map (TPM)-guided MAPPO approach with an innovative action-masking mechanism. Extensive simulations demonstrate superior performance compared to baseline methods (IPPO, QMIX, and IQL). Extensive experimental results demonstrate significant performance advantages, including 68.7% and 146.2% higher throughput compared to QMIX and IQL, respectively, along with 16.7–48.3% reduction in search completion steps versus baseline methods, while maintaining robust operations under dynamic interference conditions. The framework exhibits strong resilience to communication disruptions while maintaining stable search performance, validating its practical applicability in real-world interference scenarios. Full article

The increasing complexity of Internet of Drones (IoD) platforms demands more accessible ways for users to interact with unmanned aerial vehicle (UAV) data systems. Traditional methods requiring technical API knowledge create barriers for non-specialist users in dynamic operational environments. To address this challenge, we propose a retrieval-augmented generation (RAG) architecture that enables natural language querying over UAV telemetry, mission, and detection data. Our approach builds a semantic retrieval index from structured application programming interface (API) documentation and uses lightweight large language models to map user queries into executable API calls validated against platform schemas. This design minimizes fine-tuning needs, adapts to evolving APIs, and ensures schema conformity for operational safety. Evaluations conducted on a curated IoD dataset show 91.3% endpoint accuracy, 87.6% parameter match rate, and 95.2% schema conformity, confirming the system’s robustness and scalability. The results demonstrate that combining retrieval-augmented semantic grounding with structured validation bridges the gap between human intent and complex UAV data access, improving usability while maintaining a practical level of operational reliability. Full article

The channel wing offers unique advantages in the short take-off and landing (STOL) application of Unmanned Aerial Vehicles (UAVs). To investigate its aerodynamic performance, an individual propeller was simulated using the actuator disk model. The computed values were in close agreement with the experimental data. To conduct an initial assessment of the aerodynamic advantages offered by the channel wing, this study compared three configurations: a clean wing, a wing with a forward propeller, and a channel wing. The analysis revealed that the channel wing exhibits a better lift-to-drag ratio than the wing with a forward propeller. Further analysis investigated how propeller-to-wing clearance, axial placement relative to the wing’s leading edge, and changes in propeller diameter influence the channel wing aerodynamic characteristics. To validate the simulation results, a test platform was designed, and the calculated results were qualitatively verified. The findings indicated that reducing the propeller-to-wing clearance enhances the channel wing’s lift force and contributes to a higher lift-to-drag ratio. Altering the propeller’s installation position along the chordwise direction of the channel wing significantly influences its aerodynamic performance. Finally, the channel wing configuration was applied to a lifting-fuselage tandem-wing drone. A comparison with the conventional forward propeller configuration demonstrated that the drone with the channel wing achieves a higher lift-to-drag ratio, with a maximum value of 18.6. Compared with “forward propeller” configuration, the lift-to-drag ratio exhibits an improvement of 97.8% under the optimal configuration. Full article

This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, a mobile anchor trilateration and environmental modeling method is developed using a multi-UAV system by integrating the visual sensing capabilities of aerial surveillance UAVs with ultra-wideband technology. It constructs a real-time global 3D environmental model and provides precise positioning information, supporting autonomous planning and target guidance for near-ground UAV navigation. Secondly, based on real-time environmental perception, an improved D* Lite algorithm is employed to plan rapid and collision-free flight trajectories for near-ground navigation. This allows the UAV to autonomously execute collision-free movement from the initial position to the target position in complex environments. The results of real-world flight experiments demonstrate that the system can efficiently construct a global 3D environmental model in real time. It also provides accurate flight trajectories for the near-ground navigation of UAVs while delivering real-time positional updates during flight. The system enables UAVs to autonomously navigate in GPS-denied and unknown environments, and this work verifies the practicality and effectiveness of the proposed air–ground cooperative perception navigation system, as well as the mobile anchor trilateration and environmental modeling method. Full article

The use of UAVs for analyzing soil degradation processes, particularly erosion, has become a crucial tool in environmental monitoring. However, the use of LiDAR (Light Detection and Ranging) or TLS (Terrestrial Lasser Scanner) may not be affordable for many researchers because of the elevated costs and difficulties for cloud processing to present a valuable option for rapid landscape assessment following extreme events like Mediterranean storms. This study focuses on the application of drone-based remote sensing with only an RGB camera in geomorphological mapping. A key objective is the removal of vegetation from imagery to enhance the analysis of erosion and sediment transport dynamics. The research was carried out over a cereal cultivation plot in Málaga Province, an area recently affected by high-intensity rainfalls exceeding 100 mm in a single day in the past year, which triggered significant soil displacement. By processing UAV-derived data, a Digital Elevation Model (DEM) was generated through geostatistical techniques, refining the Digital Surface Model (DSM) to improve topographical change detection. The ability to accurately remove vegetation from aerial imagery allows for a more precise assessment of erosion patterns and sediment redistribution in geomorphological features with rapid spatiotemporal changes. Full article

Unmanned aerial vehicles (UAVs), owing to their flexible coverage expansion and dynamic adjustment capabilities, hold significant application potential across various fields. With the emergence of urban low-altitude air traffic dominated by UAVs, the integrated aviation information network combining UAVs and manned aircraft has evolved into a complex space–air–ground integrated Internet of Things (IoT) system. The application of 5G/6G network technologies, such as cloud computing, network function virtualization (NFV), and edge computing, has enhanced the flexibility of air traffic services based on service function chains (SFCs), while simultaneously expanding the network attack surface. Compared to traditional networks, the aviation information network integrating UAVs exhibits greater heterogeneity and demands higher service reliability. To address the failure issues of SFCs under attack, this study proposes an efficient SFC recovery method for recovery rate optimization (ERRRO) based on virtual network functions (VNFs) migration technology. The method first determines the recovery order of failed SFCs according to their recovery costs, prioritizing the restoration of SFCs with the lowest costs. Next, the migration priorities of the failed VNFs are ranked based on their neighborhood certainty, with the VNFs exhibiting the highest neighborhood certainty being migrated first. Finally, the destination nodes for migrating the failed VNFs are determined by comprehensively considering attributes such as the instantiated SFC paths, delay of physical platforms, and residual resources. Experiments demonstrate that the ERRRO performs well under networks with varying resource redundancy and different types of attacks. Compared to methods reported in the literature, the ERRRO achieves superior performance in terms of the SFC recovery rate and delay. Full article

For the formation reconfiguration of fixed-wing unmanned aerial vehicles (UAVs), a hierarchical control decision-making method considering both convergence and optimality is studied. To begin with, the dynamic model of the fixed-wing UAVs is established, and the formation reconfiguration control problem formally constructed. Subsequently, based on information such as the initial positions of the UAVs and the expected geometric configuration, an integer programming issue is formulated to determine the destinations of the UAVs. After completing the aforementioned preparations, by incorporating the concept of hierarchical games, the formation guidance and control problem is consequently reformulated as a multiplayer Stackelberg–Nash game (SNG). Through rigorous analysis, the optimality of using the Stackelberg–Nash equilibrium solution as the UAV control commands was demonstrated. Furthermore, a novel policy iteration (PI) algorithm for solving this equilibrium based on fixed-point iteration is proposed. To guarantee the accurate execution of the control commands, an auxiliary control system is designed, thereby forming a closed-loop real-time control decision-making mechanism. The numerical simulation results illustrate that the UAVs can rapidly switch to the desired formation configuration, thus validating the effectiveness of the proposed method. Full article

Path planning in multi-agent UAV swarms is a crucial issue that involves avoiding collisions in dynamic, obstacle-filled environments while consuming the least amount of time and energy possible. This work comprehensively evaluates reinforcement learning (RL) algorithms for multi-agent UAV path planning in 2D and 3D simulated environments. First, we develop a 2D simulation setup using Python in which UAVs (quadcopters), represented as points in space, navigate toward their respective targets while avoiding static obstacles and inter-agent collisions. In the second phase, we transition this comparison to a physics-based 3D simulation, incorporating realistic UAV (fixed wing) dynamics and checkpoint-based navigation. We compared five algorithms, namely, Proximal Policy Optimization (PPO), Soft Actor–Critic (SAC), Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), and Multi–Agent DDPG (MADDPG), in various scenarios. Our findings reveal significant performance differences between the algorithms across multiple dimensions. DDPG consistently demonstrated superior reward optimization and collision avoidance performance, while PPO and MADDPG excelled in the execution time required to reach the goal. Furthermore, our findings reveal how algorithms perform while transitioning from a simplistic 2D setup to a realistic 3D physics-based environment, which is essential for performing sim-to-real transfer. This work provides valuable insights into the suitability of several reinforcement learning (RL) algorithms for developing autonomous systems and UAV swarm navigation. Full article

The development of beyond 5G (B5G) future wireless communication networks (FWCN) needs novel solutions to support high-speed, reliable, and low-latency communication. Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are promising techniques that can enhance wireless connectivity in urban environments where tall buildings block line-of-sight (LoS) links. However, existing UAV-assisted communication strategies do not fully address key challenges like mobility management, handover failures (HOFs), and path disorders in dense urban environments. This paper introduces a deep deterministic policy gradient (DDPG)-based UAV-RIS framework to overcome these limitations. The proposed framework jointly optimizes UAV trajectories and RIS phase shifts to improve throughput, energy efficiency (EE), and LoS probability while reducing outage probability (OP) and HOF. A modified K-means clustering algorithm is used to efficiently partition the ground users (GUs) considering the newly added GUs as well. The DDPG algorithm, based on reinforcement learning (RL), adapts UAV positioning and RIS configurations in a continuous action space. Simulation results show that the proposed approach significantly reduces HOF and OP, increases EE, enhances network throughput, and improves LoS probability compared to UAV-only, RIS-only, and without UAV-RIS deployments. Additionally, by dynamically adjusting UAV locations and RIS phase shifts based on GU mobility patterns, the framework further enhances connectivity and reliability. The findings highlight its potential to transform urban wireless communication by mitigating LoS blockages and ensuring uninterrupted connectivity in dense environments. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has enabled the coordinated operation of multi-UAV systems, offering significant applications in agriculture, logistics, environmental monitoring, and disaster relief. In agriculture, UAVs are widely utilized for tasks such as ecological restoration, crop monitoring, and fertilization, providing efficient and cost-effective solutions for improved productivity and sustainability. This study addresses the collaborative task allocation problem for multi-UAV systems, using ecological grassland restoration as a case study. A multi-objective, multi-constraint collaborative task allocation problem (MOMCCTAP) model was developed, incorporating constraints such as UAV collaboration, task completion priorities, and maximum range restrictions. The optimization objectives include minimizing the maximum task completion time for any UAV and minimizing the total time for all UAVs. To solve this model, a deep reinforcement learning-based seagull optimization algorithm (DRL-SOA) is proposed, which integrates deep reinforcement learning with the seagull optimization algorithm (SOA) for adaptive optimization. The algorithm improves both global and local search capabilities by optimizing key phases of seagull migration, attack, and post-attack refinement. Evaluation against five advanced swarm intelligence algorithms demonstrates that the DRL-SOA outperforms the alternatives in convergence speed and solution diversity, validating its efficacy for solving the MOMCCTAP. Full article

The presented study demonstrates that UAVs can be flown with a morphing wing to develop essential aerodynamic efficiency without a tail structure, which decides the operational cost and flight safety. The mechanical control for morphing is discussed, where the system design, simulation, and experimental realization of ±15° SDOF sweep motion for a 7 kg eVTOL wing are detailed. The methodology, developed through a mathematical modeling of the mechanism’s kinematics and dynamics, is explained using Denavit–Hartenberg (D-H) convention, Lagrangian mechanics, and Euler–Lagrangian equations. The simulation and MBD analyses were performed in MATLAB R2021 and by Altair Motion Solve, respectively. The experiment was conducted on a dedicated test rig with two wing variants fitted with IMUs and an autopilot. The results from various methods were analyzed and experimentally compared to provide an accurate insight into the system’s design, modeling, and performance of the sweep morphing wing. The theoretical calculations by the mathematical model were compared with the test results. The sweep requirement is essential for eVTOL to have long endurance and multi-mission capabilities. Therefore, the developed sweep morphing mechanism is very useful, meeting such a demand. However, the results for three-dimensional morphing, operating sweep, pitch, and roll together are also presented, for the sake of completeness. Full article

This paper addresses inherent limitations in unmanned aerial vehicle (UAV) undercarriages hindering vertical takeoff and landing (VTOL) capabilities on uneven slopes and obstacles. Robotic landing gear (RLG) designs have been proposed to address these limitations; however, existing designs are typically limited to ground slopes of 6–15°, beyond which rollover would happen. Moreover, articulated RLG concepts come with added complexity and weight penalties due to multiple drivetrain components. Previous research has highlighted that even a minor 3-degree slope change can increase the dynamic rollover risks by 40%. Therefore, the design optimization of robotic landing gear for enhanced VTOL capabilities requires a multidisciplinary framework that integrates static analysis, dynamic simulation, and control strategies for operations on complex terrain. This paper presents a novel, hybrid, compliant, belt-driven, three-legged RLG system, supported by a multidisciplinary design optimization (MDO) methodology, aimed at achieving enhanced VTOL capabilities on uneven surfaces and moving platforms like ship decks. The proposed system design utilizes compliant mechanisms featuring a series of three-flexure hinges (3SFH), to reduce the number of articulated drivetrain components and actuators. This results in a lower system weight, improved energy efficiency, and enhanced durability, compared to earlier fully actuated, articulated, four-legged, two-jointed designs. Additionally, the compliant belt-driven actuation mitigates issues such as backlash, wear, and high maintenance, while enabling smoother torque transfer and improved vibration damping relative to earlier three-legged cable-driven four-bar link RLG systems. The use of lightweight yet strong materials—aluminum and titanium—enables the legs to bend 19 and 26.57°, respectively, without failure. An animated simulation of full-contact landing tests, performed using a proportional-derivative (PD) controller and ship deck motion input, validate the performance of the design. Simulations are performed for a VTOL UAV, with two flexible legs made of aluminum, incorporating circular flexure hinges, and a passive third one positioned at the tail. The simulation results confirm stable landings with a 2 s settling time and only 2.29° of overshoot, well within the FAA-recommended maximum roll angle of 2.9°. Compared to the single-revolute (1R) model, the implementation of the optimal 3R Pseudo-Rigid-Body Model (PRBM) further improves accuracy by achieving a maximum tip deflection error of only 1.2%. It is anticipated that the proposed hybrid design would also offer improved durability and ease of maintenance, thereby enhancing functionality and safety in comparison with existing robotic landing gear systems. Full article

Integrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility. This affects their reliability and real-time processing. While existing reviews have discussed object detection techniques and sensor-based systems, providing valuable insights into their applications, only a few have addressed the unique underground challenges that affect 3D detection models. This review synthesizes the current advancements in 3D object detection models for underground autonomous truck navigation. It assesses deep learning algorithms, fusion techniques, multi-modal sensor suites, and limited datasets in an underground detection system. This study uses systematic database searches with selection criteria for relevance to underground perception. The findings of this work show that the mid-level fusion method for combining different sensor suites enhances robust detection. Though YOLO (You Only Look Once)-based detection models provide superior real-time performance, challenges persist in small object detection, computational trade-offs, and data scarcity. This paper concludes by identifying research gaps and proposing future directions for a more scalable and resilient underground perception system. The main novelty is its review of underground 3D detection systems in autonomous trucks. Full article

The continuous configuration changes and velocity variations of tilt-rotor UAVs during the transition phase pose significant challenges to flight safety. Hence, the transition phase trajectory must be specially designed. The transition corridor is an effective means of characterizing the controllable flight state and safe flight boundary of the tilt-rotor UAV transition phase. However, the conventional transition corridor is established based on the trim criterion, which cannot fully characterize the dynamic characteristics of the transition phase, resulting in deviations in the delineation of the flight boundary. This paper proposes a method that characterizes the dynamic transition corridor of a tilt-rotor UAV during the transition phase. A three-dimensional transition corridor considering the nacelle angle, velocity, and angle of attack is established by relaxing the force constraints and introducing angle of attack variables, allowing the dynamic characteristics of acceleration and deceleration in the transition phase to be characterized. On this basis, a transition trajectory optimization method based on the three-dimensional dynamic transition corridor is established using pigeon-inspired optimization with an objective that considers the smooth transition of tilt-rotor UAVs. Numerical simulations show that, compared with the transition trajectory obtained using a two-dimensional transition corridor, the proposed method ensures smoother changes in the velocity, nacelle angle, and expected angle of attack during the transition phase, resulting in stronger engineering practicality. Full article

As the low-altitude economy undergoes rapid growth, unmanned aerial vehicles (UAVs) have served as mobile sink nodes in wireless sensor networks (WSNs), significantly enhancing data collection efficiency. However, the open nature of wireless channels and spectrum scarcity pose severe challenges to data security, particularly when legitimate UAVs (UAV-L) receive confidential information from ground sensor nodes (SNs), which is vulnerable to interception by eavesdropping UAVs (UAV-E). In response to this challenge, this study presents a cooperative jamming (CJ) scheme for Reconfigurable Intelligent Surfaces (RIS)-assisted UAV-WSN to combat aerial malicious eavesdropping. The multi-dimensional optimization problem (MDOP) of system security under quality of service (QoS) constraints is addressed by collaboratively optimizing the transmit power (TP) of SNs, the flight trajectories (FT) of the UAV-L, the frame length (FL) of time slots, and the phase shift matrix (PSM) of the RIS. To address the challenge, we put forward a Cooperative Jamming Joint Optimization Algorithm (CJJOA) scheme. Specifically, we first apply the block coordinate descent (BCD) to decompose the original MDOP into several subproblems. Then, each subproblem is convexified by successive convex approximation (SCA). The numerical results demonstrate that the designed algorithm demonstrates extremely strong stability and reliability during the convergence process. At the same time, it shows remarkable advantages compared with traditional benchmark testing methods, effectively and practically enhancing security. Full article

Nighttime terrain mapping has remained a significant challenge in photogrammetry due to the absence of visible light required by conventional imaging systems. This study evaluates the feasibility of generating Digital Surface Models (DSMs) from nighttime aerial thermal imagery using structure-from-motion photogrammetry. A DJI Mavic 3 Enterprise Thermal Unmanned Aerial Vehicle (UAV) captured 1746 images at 35 m altitude over a 9.4-hectare campus environment. Reflective aluminum sheets served as ground control points, ensuring visibility in thermal imagery under nocturnal conditions. The resulting thermal DSM achieved a point density of 0.117 points/cm². Statistical analysis of four independent checkpoints yielded a root mean square error (RMSE) of 0.0522 m, a mean error (ME) of −0.052 m, and a standard deviation (SD) of 0.0054 m, indicating high vertical accuracy with minimal scatter around the systematic bias. Comparison with a reference RGB-based DSM revealed a correlation coefficient of 0.975, demonstrating strong spatial agreement. These results establish that high-quality DSMs can be generated solely from nighttime thermal imagery, providing a viable alternative for applications requiring 24-h operational capability, including emergency response, post-disaster assessment, and nocturnal environmental monitoring where traditional photogrammetry is impractical. Full article

Due to the objects in UAV aerial images often presenting characteristics of multiple scales, small objects, complex backgrounds, etc., the performance of object detection using current models is not satisfactory. To address the above issues, this paper designs a multiscale small object detection model for UAV aerial images, namely MSUD-YOLO, based on YOLOv10s. First, the model uses an attention scale sequence fusion mode to achieve more efficient multiscale feature fusion. Meanwhile, a tiny prediction head is incorporated to make the model focus on the low-level features, thus improving its ability to detect small objects. Secondly, a novel feature extraction module named CFormerCGLU has been designed, which improves feature extraction capability in a lighter way. In addition, the model uses lightweight convolution instead of standard convolution to reduce the model’s computation. Finally, the WIoU v3 loss function is used to make the model more focused on low-quality examples, thereby improving the model’s object localization ability. Experimental results on the VisDrone2019 dataset show that MSUD-YOLO improves mAP50 by 8.5% compared with YOLOv10s. Concurrently, the overall model reduces parameters by 6.3%, verifying the model’s effectiveness for object detection in UAV aerial images in complex environments. Furthermore, compared with multiple latest UAV object detection algorithms, our designed MSUD-YOLO offers higher detection accuracy and lower computational cost; e.g., mAP50 reaches 43.4%, but parameters are only 6.766 M. Full article

Practical large-scale multiple unmanned aerial vehicle (multi-UAV) networks are susceptible to multiple potential points of vulnerability, such as hardware failures or adversarial attacks. Existing resilient multi-dimensional coordination control algorithms in multi-UAV networks are rather costly in the computation of a safe point and rely on an assumption of the maximum number of adversarial nodes in the multi-UAV network or neighborhood. In this paper, a dynamic trusted convex hull method is proposed to filter received states in multi-dimensional space without requiring assumptions about the maximum adversaries. Based on the proposed method, a distributed local control protocol is designed with lower computational complexity and higher tolerance of adversarial nodes. Sufficient and necessary graph-theoretic conditions are obtained to achieve resilient multi-dimensional consensus and containment control despite adversarial nodes’ behaviors. The theoretical results are validated through simulations. Full article

The geolocalization of unmanned aerial vehicles (UAVs) in satellite-denied environments has emerged as a key research focus. Recent advancements in this area have been largely driven by learning-based frameworks that utilize convolutional neural networks (CNNs) and Transformers. However, both CNNs and Transformers face challenges in capturing global feature dependencies due to their restricted receptive fields. Inspired by state-space models (SSMs), which have demonstrated efficacy in modeling long sequences, we propose a pure Mamba-based method called the Cross-Mamba Interaction Network (CMIN) for UAV geolocalization. CMIN consists of three key components: feature extraction, information interaction, and feature fusion. It leverages Mamba’s strengths in global information modeling to effectively capture feature correlations between UAV and satellite images over a larger receptive field. For feature extraction, we design a Siamese Feature Extraction Module (SFEM) based on two basic vision Mamba blocks, enabling the model to capture the correlation between UAV and satellite image features. In terms of information interaction, we introduce a Local Cross-Attention Module (LCAM) to fuse cross-Mamba features, providing a solution for feature matching via deep learning. By aggregating features from various layers of SFEMs, we generate heatmaps for the satellite image that help determine the UAV’s geographical coordinates. Additionally, we propose a Center Masking strategy for data augmentation, which promotes the model’s ability to learn richer contextual information from UAV images. Experimental results on benchmark datasets show that our method achieves state-of-the-art performance. Ablation studies further validate the effectiveness of each component of CMIN. Full article

In recent years, UAV formations have garnered significant attention in fields such as reconnaissance, communications, and transportation. This paper aims to design an efficient passive localization method for UAV formations that satisfies system electromagnetic compatibility (EMC) requirements. A self-adjustment model characterized by internally active communication and externally silent operation for UAV formations is proposed, which optimizes the positions of the UAVs under test based on their current locations and standard reference positions while adhering to formation geometry constraints. By comprehensively considering constraints including the number of UAVs, formation geometry, and system EMC, this study evaluates electromagnetic radiation interference within the UAV formation system and derives an iterative adjustment scheme for formation positions. Finally, simulation experiments through specific case studies calculate polar radius deviations and polar angle deviations during the adjustment process. The results validate that the proposed method meets the requirements for both formation adjustment and EMC, thereby providing a more scientific basis for passive localization and position adjustment in UAV formations. Full article

Tethered drones—defined in this work as multirotor aerial platforms physically connected to a ground station via a cable—have emerged as a transformative subclass of Tethered Unmanned Aerial Vehicles (TUAVs), offering enhanced power autonomy, communication robustness, and safety through a physical ground connection. This review provides a comprehensive analysis of the current state of tethered drone systems technology, focusing on critical system components such as power delivery, data transmission, tether management, and modeling frameworks. Emphasis is placed on the tether multifunctional role—not only as a physical link but also as a sensor, actuator, and communication channel—impacting both hardware design and control strategies. By consolidating fragmented research across disciplines, this work offers a unified reference for the design, implementation, and advancement of TUAV systems, with tethered drones as their principal application. Full article

In this study, we introduce an Unmanned Aerial Vehicle (UAV) centric visual semantic simultaneous localization and mapping (SLAM) framework that integrates RGB–D cameras, inertial measurement units (IMUs), and a 5G–enabled remote interaction module. Our system addresses three critical limitations in existing approaches: (1) Distance constraints in remote operations; (2) Static map assumptions in dynamic environments; and (3) High–dimensional perception requirements for UAV–based applications. By combining YOLO–based object detection with epipolar–constraint-based dynamic feature removal, our method achieves real-time semantic mapping while rejecting motion artifacts. The framework further incorporates a dual–channel communication architecture to enable seamless human–in–the–loop control over UAV–Unmanned Ground Vehicle (UGV) teams in large–scale scenarios. Experimental validation across indoor and outdoor environments indicates that the system can achieve a detection rate of up to 75 frames per second (FPS) on an NVIDIA Jetson AGX Xavier using YOLO–FASTEST, ensuring the rapid identification of dynamic objects. In dynamic scenarios, the localization accuracy attains an average absolute pose error (APE) of 0.1275 m. This outperforms state–of–the–art methods like Dynamic–VINS (0.211 m) and ORB–SLAM3 (0.148 m) on the EuRoC MAV Dataset. The dual-channel communication architecture (Web Real–Time Communication (WebRTC) for video and Message Queuing Telemetry Transport (MQTT) for telemetry) reduces bandwidth consumption by 65% compared to traditional TCP–based protocols. Moreover, our hybrid dynamic feature filtering can reject 89% of dynamic features in occluded scenarios, guaranteeing accurate mapping in complex environments. Our framework represents a significant advancement in enabling intelligent UAVs/UGVs to navigate and interact in complex, dynamic environments, offering real-time semantic understanding and accurate localization. Full article

Autonomous exploration is a fundamental challenge for various applications of unmanned aerial vehicles (UAVs). To enhance exploration efficiency in large-scale unknown environments, we propose a Fast Autonomous Exploration Framework (FAEM) designed to enable efficient autonomous exploration and real-time mapping by UAV quadrotors in unknown environments. By employing a hierarchical exploration strategy that integrates geometry-constrained, occlusion-free ellipsoidal viewpoint generation with a global-guided kinodynamic topological path searching method, the framework identifies a global path that accesses high-gain viewpoints and generates a corresponding highly maneuverable, energy-efficient flight trajectory. This integrated approach within the hierarchical framework achieves an effective balance between exploration efficiency and computational cost. Furthermore, to ensure trajectory continuity and stability during real-world execution, we propose an adaptive dynamic replanning strategy incorporating dynamic starting point selection and real-time replanning. Experimental results demonstrate FAEM’s superior performance compared to typical and state-of-the-art methods in existence. The proposed method was successfully validated on an autonomous quadrotor platform equipped with LiDAR navigation. The UAV achieves coverage of 8957–13,042 m³ and increases exploration speed by 23.4% compared to the state-of-the-art FUEL method, demonstrating its effectiveness in large-scale, complex real-world environments. Full article

In order to solve the problems of identity solidification, key duration, and lack of anonymity in communications between unmanned aerial vehicles (UAVs) and ground control stations (GCSs), a mutual secure communication scheme named Dynamic Identity and Hybrid Encryption is proposed in this paper. By constructing an identity update mechanism and a lightweight hybrid encryption system, the anonymity and untraceability of the communicating parties can be realized within a resource-limited environment, and threats such as man-in-the-middle (MITM) attacks, identity forgery, and message tampering can be effectively resisted. Dynamic Identity and Hybrid Encryption (DIHE) uses a flexible encryption strategy to balance security and computing cost and satisfies security attributes such as mutual authentication and forward security through formal verification. Our experimental comparison shows that, compared with the traditional scheme, the calculation and communication costs of DIHE are lower, making it especially suitable for the communication environment between UAVs and GCSs with limited computing power, thus providing a feasible solution for secure low-altitude Internet of Things (IoT) communication. Full article

UAV formation navigation in complex environments such as narrow tunnels faces multiple challenges, including obstacle avoidance, formation maintenance, and communication constraints. This paper proposes a cooperative obstacle avoidance strategy for UAV formation based on adaptive event-triggered impulse control, achieving efficient navigation under limited resources. The strategy comprises four key modules: an adaptive event-triggering mechanism, optical flow-based obstacle detection, leader–follower formation structure, and dynamic communication topology management. The adaptive event-triggering mechanism dynamically adjusts triggering thresholds, ensuring control accuracy while reducing control update frequency; the enhanced optical flow perception model improves obstacle recognition ability through a sector-based approach, incorporating tunnel-specific avoidance strategies; the leader–follower formation structure employs dynamic weight allocation to balance obstacle avoidance needs with formation maintenance; and communication topology optimization enhances system robustness under limited communication conditions. Simulation experiments were conducted in an arc-shaped tunnel environment with 15 randomly distributed obstacles, and the results demonstrate that the proposed method significantly improves collision rates, formation errors, and communication overhead compared to traditional methods. Lyapunov stability analysis proves the convergence of the proposed control strategy. This research provides new theoretical and practical references for multi-UAV cooperative control in complex narrow environments. Full article