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

This study presents the design and flight testing of a small unmanned aerial vehicle (UAV) in a flying wing configuration. The flying wing concept provides a low-drag platform suitable for observation, surveillance, and search-and-rescue missions. The UAV is designed to achieve inherent stability without the use of vertical stabilizers or artificial stabilization systems, which may reduce aerodynamic efficiency. The design process includes aerodynamic analyses aimed at balancing static and dynamic stability. Flight tests are conducted to validate the proposed configuration and to assess its ability to maintain stable flight under various operating conditions. The results confirm that the developed flying wing UAV achieves stable flight without artificial stabilization, demonstrating the potential of flying wing configurations as efficient platforms for small unmanned aerial vehicles. In particular, the concept is well suited for applications requiring long-endurance flights, low energy consumption, and reduced radar reflectivity.

The emergence of quantum computing poses a significant threat to the security of traditional encryption methods employed in satellite communication. To mitigate this vulnerability and enhance cybersecurity in the next generation of communication systems, a novel physical-layer solution is presented. This approach centers on enhancing satellite link security through the analysis of stochastic atmospheric scintillation, facilitated by machine learning (ML). The proposed method safeguards ground stations against Machine-in-the-Middle (MITM) attacks perpetrated from aerial platforms (AP) such as drones or Unmanned Aerial Vehicles (UAVs). The underlying principle leverages the distinct statistical parameters inherent to received signals. These parameters are contingent upon the specific propagation channel, which is influenced by rapid tropospheric scintillation. As signals from legitimate satellites and malicious drones traverse separate spatial paths within the dynamic atmosphere, they exhibit demonstrably divergent scintillation statistics. Wavelet filtering is employed to extract these statistics from the incoming signal. The extracted data is subsequently processed through an ML algorithm, enabling the differentiation between satellite signals and potential spoofing signals emanating from drones. Extensive simulations have been conducted, illustrating the efficacy and robustness of the proposed architecture, consistently achieving an authentication rate exceeding 98% across diverse scenarios. Additionally, experimental results obtained from measurement data collected from Nilesat and Eutelsat satellites at a ground station in Israel provide empirical validation for this innovative approach.

We propose a unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and jamming (U-ISJC) framework, in which a multifunctional UAV first detects the sensing target to obtain sensing information, and subsequently transmits the information to communication users via a unified beam in the presence of multiple eavesdroppers. To avoid functional conflicts, a time slot frame structure is designed for the UAV’s multifunctional capabilities, enabling communication, sensing, and jamming tasks within each timeslot. The time slot allocation factor dynamically adjusts based on the UAV’s flight trajectory for efficient UAV resource utilization. Additionally, to prevent security rate leakage caused by eavesdroppers, a jamming beam is added to serve both jamming and sensing functions. Our objective is to maximize the the worst-case total secure data transmission rate by jointly optimizing sub-time slot allocation, beamforming, and UAV trajectory. To address this problem, we propose a joint optimization algorithm that adopts the concave–convex procedure (CCCP) technique and semi-definite relaxation (SDR), under the block coordinate descent (BCD) framework. The simulation results show that compared with the baseline scheme, the proposed algorithm substantially improves the communication security rate while ensuring the quality of communication and sensing.

Abstract Accurate fault detection and diagnosis are critical components of any fault-tolerant control system, especially for unmanned aerial vehicles (UAVs) where reliability is paramount. Traditionally, both model-based and data-driven approaches have been applied for fault diagnosis. However, the increasing complexity of high-dimensional UAV systems has shifted focus toward data-driven methods, which leverage advanced classification algorithms to enhance fault identification and isolation. This study builds on this evolution by developing a sophisticated condition-based monitoring (CBM) system specifically designed for multirotor UAVs. In contrast to earlier studies that primarily relied on raw data for classifier training, this work introduces advanced preprocessing techniques and multidomain feature extraction, significantly improving the robustness and accuracy of fault detection. A comparative analysis is performed between feature-selection methods, including recursive feature elimination with cross-validation (RFECV) and variational autoencoder (VAE), to extract critical insights into UAV operational behavior. Through testing and evaluating various classification models on data from a hexarotor UAV under diverse actuator fault conditions, this research identifies optimal approaches for real-time fault detection and diagnosis. Results demonstrate notable improvements across all evaluation metrics, establishing this approach as a substantial advancement in UAV fault tolerance.

Unmanned aerial vehicle (UAV) forest fire detection is vital for forest safety. However, early-stage UAV fire scenarios often involve small targets, weak smoke signals, and strict onboard resource constraints, which pose significant challenges to existing detectors. To improve the speed and accuracy of UAV forest fire detection, this paper proposes a lightweight fire detection algorithm, AHE-YOLO, specifically designed for UAVs. The proposed method adopts a coordinated lightweight design to improve feature preservation and cross-scale representation under limited computational budgets. Specifically, the Adaptive Downsampling (ADown) module preserves shallow fire-related cues during spatial reduction, improving sensitivity to small flame and smoke targets. The high-level screening-feature fusion pyramid network (HS-FPN) introduces cross-scale attention to promote more discriminative multi-level feature interaction while reducing redundant computation. Furthermore, the Efficient Mobile Inverted Bottleneck Convolution (EMBC) module is employed to improve receptive-field efficiency and feature selectivity under lightweight constraints, further enhancing detection accuracy and inference speed. Finally, the performance of AHE-YOLO is comprehensively evaluated through ablation and comparative experiments on the same dataset. The final experimental results show that YOLO-AHE achieves a mean average precision (mAP) of 94.8% while reducing model parameters by 39.7%, decreasing FLOPs by 27.0%, and shrinking the model size by 36.4%. In addition, its inference speed improves by 16.5%. Beyond detection performance, the proposed framework supports sustainable forest monitoring by enabling early fire warning with reduced computational and energy demands, showing strong potential for real-time deployment on resource-constrained UAV and edge platforms.

The rapid development and wide application of unmanned aerial vehicles (UAVs) have made illegal and unauthorized flights a serious threat to public safety, making timely detection essential. However, existing UAV detection methods still struggle to accurately detect small UAVs because of low feature resolution, background clutter, and dense spatial distribution. Transformer-based detectors, such as Detection Transformer (DETR), have shown promising improvements in detection accuracy; however, they demand significant computation and exhibit high inference latency, limiting deployment on resource-constrained platforms. To address these challenges, we propose UDRT-DETR, a small UAV detection method based on infrared imaging and the Real-Time Detection Transformer (RT-DETR). To reduce the computational cost of conventional transformer backbones, we design a cascaded inverted residual backbone (CIRB) that combines cascaded inverted residual mobile blocks (CI-RMBs) with depthwise separable convolutions and structured reparameterization, thereby enhancing feature representation and reducing computation. To reduce the cost of multi-head self-attention, we propose a super token attention-based intra-scale feature interaction (STA-IFI) module that projects tokens into a compact super-token space, eliminating redundant interactions while preserving global context for detecting densely distributed small UAVs. For effective cross-scale integration, we design a slim-neck-ASF, which combines lightweight convolutional units with adaptive upsampling for a precise multi-scale fusion. We design the inner-MPDIoU loss to refine bounding-box regression using auxiliary constraints, thereby improving localization accuracy. Experiments on our self-built infrared UAV dataset demonstrate that UDRT-DETR achieves a precision of 90.71%, which is 4.66% higher than RT-DETR, while reducing GFLOPs by 17.84%, confirming state-of-the-art accuracy and enabling real-time UAVs surveillance.

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present.

To address severe measurement error fluctuations and heterogeneous information source uncertainties in master–slave unmanned aerial vehicle (UAV) formations, a high-precision cooperative navigation method is proposed. Integrating inertial navigation, satellite positioning, and inter-UAV relative distance, the method innovatively introduces three key components: a multi-source information fusion-based cooperative navigation framework for accurate formation state estimation, a cooperative geometric dilution of precision (CGDOP) model based on hybrid observation configurations for positioning accuracy evaluation, and a dynamic-weight Gaussian belief propagation (WGBP) algorithm for adaptive measurement weight adjustment to suppress low-quality observation interference. Experiments demonstrate that WGBP achieves the lowest mean error in 22 out of 24 cases and the smallest standard deviation in 21 cases compared with EKF, WGP, HRGBP, and WGBP. Empirical field experiments further demonstrate consistent superiority of WGBP in dynamic environments.

Background Border control authorities face increasing operational pressure due to growing traveller volumes, evolving security risks, and stricter regulatory requirements. Traditional manual procedures alone are no longer sufficient to ensure both security and smooth passenger flow. The ODYSSEUS project was developed to address these challenges by combining artificial intelligence, computer vision, and multi-sensor technologies into a unified, privacy-respecting solution for border management. Methods The project followed a step-by-step methodology starting with an analysis of user needs, operational workflows, and real scenarios provided by border guards. These inputs were used to define use cases and system requirements. Each ODYSSEUS component was then designed according to the principles of the EU Artificial Intelligence Act, with attention to transparency, robustness, and human oversight. The technologies were implemented, tested in controlled environments, and gradually integrated into a modular platform. Finally, the complete system was validated in three pilot locations representing land, sea, and rail border environments. Results The ODYSSEUS platform successfully combined several advanced tools, including digital travel credential verification, behavioural authentication, faceless people counting, unmanned aerial vehicle-based vehicle inspection, and artificial intelligence-supported X-ray screening. All components were integrated into a single decision support interface that provided real-time information and alerts. The pilots demonstrated that the system could support officers by improving situational awareness, reducing manual workload, and enabling faster processing, while maintaining strict compliance with data protection requirements. Conclusions The project shows that a modular and artificial intelligence-enabled approach can enhance border control operations without compromising privacy or regulatory obligations. The successful demonstrations in three different environments confirm that the ODYSSEUS technologies are mature, functional, and suitable for further deployment and scaling. The work provides a practical foundation for future integration of artificial intelligence systems in European border management.

The theoretical and applied aspects of computer modeling are described, taking into account modern methods of constructing information, measurement and control systems (IIAs) of aircraft during their operation. The LA and IIiUS facilities are high‑tech and complex technical systems (CTC) that require combined approaches to their assessment. The ways of forming the main blocks of information models for obtaining the main indicators of aircraft reliability and safety are shown. Formulas are given for the use of adequate information technology processes and methods for assessing the technical level of created samples of both single‑level and multi‑level hierarchical systems in combination with known methods, operating algorithms and software, which are more complete in information content with probabilistic characteristics. The theoretical aspects of the work and the formulations are supported by a computational experiment, during which the aircraft cargo was delivered to a given area. The results of the work can be useful to developers of unmanned aircraft systems and specialists in the field of designing CTC facilities when predicting their technical condition with an assessment of functional safety and ensuring the desired efficiency.

In autonomous exploration tasks in unstructured terrain, exploration efficiency and map topology quality have been a difficult problem to balance. Among the current autonomous exploration methods, geometry-based exploration methods only focus on exploration efficiency but not map quality, which not only leads to frequent backtracking by the robot, but also tends to ignore non-geometric risks such as negative obstacles. To address this pain point, we propose the Structure-Aware Topology Exploration framework. Unlike pure geometric exploration, we utilize U-Net to semantically analyze the unmanned aerial vehicle aerial images, and force the robot’s path to be anchored to the geometric axis of the safe area through the Semantic Seeded Voronoi mechanism. To avoid map redundancy leading to backtracking, we directly introduce topological sparsity constraints in the decision function to realize online structural pruning during exploration. Simulation experiments based on real-world aerial imagery demonstrate that the proposed framework effectively overcomes the late-stage exploration plateau: compared with purely geometric baselines (Rapidly exploring Random Tree and Frontier), it reduces average path length to 278.4 m (45% reduction) and improves exploration efficiency by 80%; compared with the semantic frontier-based baseline, it achieves 28.6% higher efficiency and 13% shorter path length, maximizing information gain per unit travel distance.

Improved prediction of winter wheat yield at regional scale with limited ground samples by unmanned aerial vehicle and satellite synergy

Modeling interactive crash avoidance behaviors: A multi-agent state-space transformer-enhanced reinforcement learning framework.

A two-layer adaptive control framework for prescribed performance in unmanned aerial vehicles

This study aims to investigate the icing behavior of unmanned aerial vehicles (UAVs) propellers under different operating conditions. Unlike previous work, a systematic analysis of droplet impingement, heat transfer characteristics, and ice accretion distribution on the blade surface is conducted, revealing the spatial distribution characteristics of propeller icing from multiple perspectives. The propeller flow field is solved using the Reynolds-averaged Navier–Stokes (RANS) equations coupled with the Multiple Reference Frame (MRF) model, the droplet motion and collection are predicted by an Eulerian multiphase approach, and the icing process is modeled using the shear-stress transport-based shallow water film method (SWIM). The results show that higher rotational speeds and larger droplet diameters significantly increase ice accumulation, with more complex ice shapes forming particularly at the blade tip. In contrast, higher temperatures reduce the overall icing amount but alter the spatial distribution of ice. The blade tip region exhibits stronger transport capacity for unfrozen water, leading to faster ice accretion. These findings provide useful guidance for designing more effective anti-icing strategies for UAV propellers.

Cooperative decision-making of unmanned aerial vehicles: A multi-agent reinforcement learning approach

Yield forecasting in maize: Performance and limits of unmanned aerial vehicle and PlanetScope remote sensing across multiple growth cycles

Freq-DETR: Frequency-aware transformer for real-time small object detection in unmanned aerial vehicle imagery

Investigation of MIMO channel model for ultra supersonic unmanned aerial vehicles