Search Results (2,932)

This scientific article presents an innovative approach to optimizing routes of high-altitude unmanned aerial vehicles (UAVs) for effective monitoring of smart cities. The main proposed method is based on the ant colony optimization (ACO) algorithm with the implementation of an inverse pheromone mechanism—a repulsion-based exploration strategy. Unlike standard pheromones that encourage exploitation of frequently visited paths, this approach promotes exploration of unvisited areas by repelling UAVs from undesirable route sections, allowing UAVs to adapt more efficiently to dynamic changes in the urban environment. The authors developed a simulation system in the Webots environment, taking into account numerous factors: atmospheric conditions at high altitudes, potential for improved energy efficiency, urban development features, and priority of observation zones. Simulation results demonstrate that the proposed algorithm using inverse pheromones provides more effective area coverage compared to traditional route-planning methods, which may contribute to reduced UAV energy consumption and optimizing the monitoring process in real time. The research makes a significant contribution to the development of smart city technologies, offering a solution that can be integrated with existing urban monitoring systems to improve the efficiency of urban infrastructure observation, enhance security, and optimize urban resource management. Full article

The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of highway lane extraction from low-altitude UAV perspectives by applying a novel three-stage framework. This framework consists of (1) a deep learning-based semantic segmentation module that employs an enhanced STDC network with boundary-aware loss for precise detection of roads and lane markings; (2) an optimized polynomial fitting algorithm incorporating iterative classification and adaptive error thresholds to achieve robust lane marking consolidation; and (3) a global optimization module designed for context-aware lane generation. Our methodology demonstrates superior performance with 94.11% F1-score and 93.84% IoU, effectively bridging the technical gap in UAV-based lane extraction while establishing a reliable foundation for advanced traffic monitoring applications. Full article

Unmanned Aerial Vehicles (UAVs) play a crucial role in the continuous monitoring and coordination of disaster relief operations, providing real-time information that aids in decision-making and resource allocation. However, optimizing the deployment of multi-UAV systems in disaster-stricken areas presents a significant challenge. This challenge arises due to conflicting objectives, such as maximizing coverage while minimizing energy consumption, critical to ensuring prolonged operational capability in dynamic and unpredictable environments. To address these challenges, this paper proposes a novel successive deployment method specifically designed for optimizing UAV placements in complex disaster relief scenarios. The overall optimization problem is decomposed into two NP-hard subproblems: the coverage problem and the Energy Consumption (EC) problem. To achieve maximum coverage of the affected area, we employ the Strong Weiszfeld (SW) algorithm to determine optimal UAV placement. Simultaneously, to minimize energy consumption while maintaining optimal coverage performance, we utilize the Steepest Descent with Goldstein (SDG) algorithm. This dual-algorithmic approach is tailored to balance the trade-offs between wide-area coverage and energy efficiency. We validate the effectiveness of the proposed SW + SDG method by comparing its performance against traditional deployment strategies across multiple scenarios. Experimental results demonstrate that our approach significantly reduces energy consumption while maintaining extensive coverage, and outperforms conventional algorithms. This not only ensures a more sustainable and long-lasting operational network but also enhances deployment efficiency and stability. These findings suggest that the SW + SDG algorithm is a robust and versatile solution for optimizing multi-UAV deployments in dynamic, resource-constrained environments, providing a balanced approach to coverage and energy efficiency. Full article

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. Traditional UAV autonomy has relied mainly on visual perception or preprogrammed planning, offering limited adaptability and explainability. This study introduces a novel reference architecture, the multimodal AI–HMC system, based on which a dedicated UAV use case architecture was instantiated and experimentally validated in a controlled laboratory environment. The architecture integrates VLM-powered reasoning, real-time depth estimation, and natural-language interfaces, enabling UAVs to perform context-aware actions while providing transparent explanations. Unlike prior approaches, the system generates navigation commands while also communicating the underlying rationale and associated confidence levels, thereby enhancing situational awareness and fostering user trust. The architecture was implemented in a real-time UAV navigation platform and evaluated through laboratory trials. Quantitative results showed a 70% task success rate in single-obstacle navigation and 50% in a cluttered scenario, with safe obstacle avoidance at flight speeds of up to 0.6 m/s. Users approved 90% of the generated instructions and rated explanations as significantly clearer and more informative when confidence visualization was included. These findings demonstrate the novelty and feasibility of embedding VLMs into UAV systems, advancing explainable, human-centric autonomy and establishing a foundation for future multimodal AI applications in HMC, including robotics. Full article

Urban unmanned aerial vehicle (UAV) surveillance faces significant obstacles due to visual obstructions, inadequate lighting, small target dimensions, and acoustic signal interference caused by environmental noise and multipath propagation. To address these issues, this study proposes a multimodal detection framework that integrates an efficient YOLOv11-based visual detection module—trained on a comprehensive dataset containing over 50,000 UAV images—with a Capon beamforming-based acoustic imaging system using a 144-element spiral-arm microphone array. Adaptive compensation strategies are implemented to improve the robustness of each sensing modality, while detections results are validated through intersection-over-union and angular deviation metrics. The angular validation is accomplished by mapping acoustic direction-of-arrival estimations onto the camera image plane using established calibration parameters. Experimental evaluation reveals that the fusion system achieves outstanding performance under optimal conditions, exceeding 99% accuracy. However, its principal advantage becomes evident in challenging environments where individual modalities exhibit considerable limitations. The fusion approach demonstrates substantial performance improvements across three critical scenarios. In low-light conditions, the fusion system achieves 78% accuracy, significantly outperforming vision-only methods which attain only 25% accuracy. Under occlusion scenarios, the fusion system maintains 99% accuracy while vision-only performance drops dramatically to 9.75%, though acoustic-only detection remains highly effective at 99%. In multi-target detection scenarios, the fusion system reaches 96.8% accuracy, bridging the performance gap between vision-only systems at 99% and acoustic-only systems at 54%, where acoustic intensity variations limit detection capability. These experimental findings validate the effectiveness of the complementary fusion strategy and establish the system’s practical value for urban airspace monitoring applications. Full article

Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article

As wind turbines are affected by the harsh marine environment, inspection is crucial for the continuous operation of offshore wind farms. Nowadays, the main method of inspection is manual inspection, which has significant limitations in terms of safety, economy, and labor. With the advancement of technology, unmanned inspection systems have attracted more attention from researchers and the industry. This study proposes a novel framework to enable Unmanned Aerial Vehicles (UAVs) to improve their adaptability in autonomous inspection tasks on offshore wind farms, which includes multi-UAVs, inspection task nodes, and multiple charging stations. The main contributions of this paper are as follows: we propose an improved PSO algorithm to improve the location of charging stations; based on the multi-depot traveling salesman problem, we establish a multi-station UAV cooperative task allocation model with energy constraints, with the inspection time consumption of UAVs as the optimization objective; we also propose the Dynamic elite Double population Genetic Algorithm (DDGA) to aid in the cooperative task allocation of UAVs. The simulation results show that, compared with other algorithms, the proposed framework has higher universality and superiority. This paper provides a specific method for the application of unmanned inspection systems in the inspection of wind turbines in offshore wind farms. Full article

With the increasing deployment of unmanned aerial vehicle (UAV) swarms in scenarios such as disaster response, environmental monitoring, and military reconnaissance, the need for secure and scalable formation control has become critical. Traditional centralized architectures face challenges such as limited scalability, communication bottlenecks, and single points of failure in large-scale swarm coordination. To address these issues, this paper proposes a blockchain-based decentralized formation control framework that integrates smart contracts to manage UAV registration, identity authentication, formation assignment, and positional coordination. The system follows a leader–follower structure, where the leader broadcasts formation tasks via on-chain events, while followers respond in real-time through event-driven mechanisms. A parameterized control model based on dynamic angle and distance adjustments is employed to support various formations, including V-shape, line, and circular configurations. The transformation from relative to geographic positions is achieved using Haversine and Euclidean methods. Experimental validation in a simulated environment demonstrates that the proposed method achieves lower communication latency and better responsiveness compared to polling-based schemes, while offering enhanced scalability and robustness. This work provides a feasible and secure decentralized control solution for future UAV swarm systems. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

Polar environmental research requires advanced detection methods to understand rapid changes in these regions. Unmanned aerial vehicles (UAVs) bridge the gap between satellite remote sensing and in situ ice-based buoy measurements, offering improved spatiotemporal resolution and operational efficiency. However, their widespread use in polar regions remains limited due to insufficient endurance capabilities. To address this problem, this paper presents a new monitoring system, the so-called UAV and Ice-based buoy cross-domain observation system (UBCOS). Particularly, the ice-based buoy integrates a Real-Time Kinematic (RTK) base station, a contact-based charging system, and an Iridium communication system, providing UAVs with centimeter-level positioning correction, low-temperature charging support, and remote data transmission capabilities. UAVs equipped with pod-mounted cameras capture imagery of sea ice surface characteristics within a 4 km radius of the buoy. Field tests conducted in the Arctic in 2024 demonstrate that the system achieved expected performance in both monitoring task execution and data collection, validating its practicality and reliability for polar sea ice monitoring. Full article

With the booming development of the low-altitude economy and the widespread application of Unmanned Aerial Vehicles (UAVs), integrated sensing and communication (ISAC) technology plays an increasingly pivotal role in intelligent communication networks. However, low-altitude platforms supporting ISAC, such as UAV swarms, are highly vulnerable to deception jamming in complex electromagnetic environments. Existing multistatic radar systems face challenges in processing slowly fluctuating targets (like low-altitude UAVs) and adapting to complex electromagnetic environments when fusing multiple pulse echoes. To address this issue, targeting the protection needs of low-altitude targets like UAVs, this paper leverages the characteristic of rapid amplitude fluctuation in frequency-agile radar echoes to analyze the differences between true and false targets in multistatic frequency-agile radar systems, particularly for slowly fluctuating UAV targets, demonstrating the feasibility of discrimination. Building on this, we introduce a neural network approach to deeply extract discriminative features from true and false target echoes and propose a neural network-based variable-length processing method for deception jamming discrimination in multistatic frequency-agile radar. The simulation results show that the proposed method effectively exploits deep-level echo features, significantly improving the discrimination probability between true and false targets, especially for slowly fluctuating UAV targets. Crucially, even when trained on a fixed number of pulses, the model can process input data with varying pulse counts, greatly enhancing its practical deployment capability in dynamic UAV mission scenarios. Full article

Quantifying air emissions from livestock pastures remains challenging due to spatial variability and temporal fluctuations in emissions due to weather conditions. In this study we used a small unmanned aerial vehicle (sUAV) equipped with real-time sensors and an air sample collection system to directly measure carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), nitrous oxide (N₂O), nitrogen dioxide (NO₂), hydrogen sulfide (H₂S), total volatile organic compound (VOC), and particulate matter (PM₁, PM_2.5, PM₁₀) emissions across two dairy pastures, two beef pastures, and one sheep pasture in Wisconsin. Emission rates were calculated using the Lagrangian mass balance model and validated against ground-level dynamic flux chamber (DFC) measurements. UAV-based CO₂ concentrations showed a strong correlation with DFC measurements (R² = 0.86, RMSE = 21.5 ppm, MBE = +9.7 ppm). Dairy 1 yielded the highest emissions for most compounds, with average emission rates of 0.50 ± 0.28 g m⁻² day⁻¹ head⁻¹ for CO₂, 8.48 ± 2.75 mg m⁻² day⁻¹ head⁻¹ for CH₄, and 0.20 ± 0.60 mg m⁻² day⁻¹ head⁻¹ for NH₃. The sheep pasture, on the other hand, had the lowest CH₄ and NH₃ emission rates, averaging 0.35 ± 0.22 mg m⁻² day⁻¹ head⁻¹ and 0.02 ± 0.05 mg m⁻² day⁻¹ head⁻¹, respectively. Rainfall events (≥ 5 mm within five days of sampling) significantly elevated N₂O emissions (0.56 ± 0.40 vs. 0.13 ± 0.17 mg m⁻² day⁻¹ head⁻¹). Particulate matter emissions were significantly affected by forage density. PM_2.5 emission rates reached 1.25 × 10⁻⁴ g m⁻² day⁻¹ head⁻¹ under low vegetative cover. It was concluded that emissions were affected by both animal species and the environmental conditions. The findings of this study provide a foundation for further development of emission inventories for pasture-based livestock production systems. Full article

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in power system applications, such as transmission line inspection, fault diagnosis, and post-disaster emergency communication restoration. Nonetheless, limitations of range and battery capacity have rendered the assurance of uninterrupted task operation a critical concern. Efficient cooperation and energy replenishment solutions are crucial for effective UAV cluster scheduling to resolve this issue. This study proposes an innovative scheduling method that integrates UAV path planning with laser-based remote charging technology. Initially, a scheduling model incorporating both energy consumption and task completion time is established. Subsequently, an integrated laser-powered UAV model is proposed, unifying charging operations with mission execution processes. Furthermore, a distributed robust optimization (DRO) framework is proposed to handle spatiotemporal uncertainties, particularly those caused by weather conditions. Finally, the proposed scheduling method is applied to a disaster recovery scenario of a power system. Simulation results demonstrate that the proposed strategy significantly outperforms traditional scheduling methods without remote charging by achieving higher task completion rates and improved energy efficiency. These findings substantiate the effectiveness and engineering feasibility of the proposed method in enhancing UAV cluster operational capabilities under stringent energy constraints. Full article

A tethered Unmanned Aerial Vehicle (UAV) is a special type of UAV that is powered continuously through a cable, ensuring long-duration flight. However, the pulling interference of the cable significantly affects the UAV’s stability control, limiting its application and development. This paper addresses this issue by first analyzing the effect of cable tension on the UAV’s wind resistance capability and demonstrates the possibility of using cable tension to assist in wind resistance control. Building on this, a robust time-delay compensator is designed to address the problem of unknown external disturbance and unknown time delay in the cable control input. Sufficient conditions for system boundedness are provided using the Lyapunov–Krasovskii functional. Subsequently, to deal with the strong nonlinearity and strong coupling issues of the sufficient conditions, the Harris Hawks Optimization (HHO) algorithm is employed for intelligent optimization of the controller parameters. Simulation results indicate that the HHO-based robust time-delay compensator exhibits excellent robustness and fast response. Full article

Precise and robust localization for micro Unmanned Aerial Vehicles (UAVs) in GNSS-denied environments is hindered by the lack of diverse datasets and the limited real-world performance of existing visual matching methods. To address these gaps, we introduce two contributions: (1) the SatLoc dataset, a new benchmark featuring synchronized, multi-source data from varied real-world scenarios tailored for UAV-to-satellite image matching, and (2) SatLoc-Fusion, a hierarchical localization framework. Our proposed pipeline integrates three complementary layers: absolute geo-localization via satellite imagery using DinoV2, high-frequency relative motion tracking from visual odometry with XFeat, and velocity estimation using optical flow. An adaptive fusion strategy dynamically weights the output of each layer based on real-time confidence metrics, ensuring an accurate and self-consistent state estimate. Deployed on a 6 TFLOPS edge computer, our system achieves real-time operation at over 2 Hz, with an absolute localization error below 15 m and effective trajectory coverage exceeding $90\%$, demonstrating state-of-the-art performance. The SatLoc dataset and fusion pipeline provide a robust and comprehensive baseline for advancing UAV navigation in challenging environments. Full article