Search Results (1,056)

This paper presents HydroAir, a novel air-propelled Unmanned Surface Vehicle (USV) specifically designed for operation in shallow waters and obstacle-rich aquatic environments such as lakes, reservoirs, and large dams. Unlike conventional aquatic robots, HydroAir employs an aerial propulsion system that enables it to overcome partially submerged obstacles, vegetation, and extremely shallow regions where traditional propeller-based platforms fail. The vehicle features a system with a very reliable internal architecture, providing high maneuverability and robustness in both manual and autonomous navigation modes. The primary objective of HydroAir is to serve as a mobile sensing platform for three-dimensional reconstruction of aquatic environments, particularly the underwater terrain. The onboard sensing suite enables bathymetric data acquisition, while a dedicated monitoring and control software integrates these data with aerial reconstructions obtained from Unmanned Aerial Vehicles (UAVs), allowing for the fusion of above-water and underwater spatial information into a unified 3D model. Experimental validations were conducted in large-scale, real-world environments, including tests in a hydroelectric dam operated by Santo Antônio Energia on the Madeira River in Brazil, demonstrating the platform’s operational feasibility, stability, and reconstruction capabilities. The results indicate that HydroAir is a promising solution for environmental monitoring, inspection, and mapping in challenging aquatic environments where conventional autonomous surface vehicles are limited. Full article

Autonomous underwater vehicles (AUVs) are widely employed in missions conducted near the seabed, including underwater inspection, seabed mapping, and marine resource exploration. During such operating conditions, the interaction between the AUV and the bottom wall can significantly influence the surrounding flow field and the hydrodynamic characteristics of the vehicle. In this study, a numerical investigation is carried out to examine the influence of near-bottom effects on the hydrodynamic performance of an AUV using a commercial Computational Fluid Dynamics (CFD) solver. The seabed is assumed as a flat wall, and two operating conditions are considered, including an open-water case and a near-bottom case with a clearance ratio of h/L_A = 1.93. The flow field is investigated through analyses of hydrodynamic force, pressure distribution, and wake structures. The results indicate that the wall bottom noticeably alters the pressure field and wake development around the AUV, leading to changes in total resistance and flow separation. The findings provide useful insights into the hydrodynamic mechanisms associated with near-bottom operation and offer valuable guidance for the design, control, and operation of AUVs performing missions in shallow or seabed-related missions. Full article

In unmanned aerial vehicle (UAV)-based power line inspection, multi-scale defects and complex backgrounds challenge the balance between detection accuracy, speed, and model lightweighting, limiting automated grid inspection. This paper proposes a Multi-Scale Mamba Framework (MS-Mamba) for efficient and accurate defect perception. A drone inspection dataset containing 5137 images from 14 defect categories was constructed and divided into training and validation sets with an 8:2 split. To address the large scale variation among defects, the categories are decoupled into macroscopic, mesoscopic, and microscopic groups according to physical attributes and visual scales. As the core perception engine, a lightweight state-space mechanism is designed to balance accuracy and deployability. A spatial resolution-aware hierarchical reconstruction strategy and a dynamic feature selection mechanism are integrated to enhance feature extraction, reduce background redundancy, and improve small-target representation. Compared with the YOLOv5s baseline, MS-Mamba achieves an mAP@0.5 of 0.749, corresponding to a 15.6 percentage-point improvement, while reducing parameters by 0.13 M and computational cost by 1.7 GFLOPs. Ablation studies and visual analyses further confirm fewer missed and false detections in complex backgrounds. The developed end-to-end inspection system was validated through closed-loop engineering tests, demonstrating strong potential for industrial deployment. Full article

Unmanned aerial vehicles (UAVs) are widely used for inspection and monitoring tasks, where mission efficiency is strongly influenced by environmental conditions such as wind. In this work, we study a joint route–speed optimization problem for UAV inspection missions under uncertain wind conditions. The objective is to determine both the visiting sequence of inspection targets and the flight speeds along route segments in order to minimize worst-case energy consumption while satisfying mission duration constraints. We formulate the problem using a robust optimization framework that accounts for uncertainty in both wind speed and wind direction. The resulting model involves coupled discrete routing decisions and continuous speed control variables, which makes the problem computationally challenging. To address this difficulty, we propose a robust route–speed decomposition (RRSD) framework that alternates between route improvement and nonlinear speed optimization. Computational experiments on randomly generated instances, evaluated over eight random seeds per setting and compared against five baselines, including a simulated-annealing metaheuristic, demonstrate that RRSD consistently reduces worst-case energy consumption. A sensitivity analysis over the wind-uncertainty half-widths further shows that this advantage widens as the uncertainty set grows, and comparisons with exact enumeration on small instances confirm near-optimal solution quality at reasonable computational cost. These results highlight the importance of jointly optimizing routing decisions and speed control for energy-efficient UAV mission planning under uncertain environmental conditions. Full article

Ultra-large-scale offshore photovoltaic (PV) installations require efficient and reliable construction-phase inspection to ensure installation integrity and compliance with engineering specifications. As the deployment scale expands to thousands of platforms and millions of photovoltaic modules, conventional manual inspection becomes labor-intensive, time-consuming, and increasingly prone to omission errors. This study presents an autonomous inspection framework based on AI-driven computer vision for the detection and localization of missing photovoltaic modules in offshore PV systems. The proposed framework integrates high-resolution UAV-acquired RGB imagery, YOLOv8-based object detection, geographic coordinate transformation, spatial deduplication, and deterministic grid-based indexing to convert aerial observations into structured engineering inspection records. Each detected missing module is automatically assigned a unique platform identifier together with row–column coordinates, enabling engineering-level localization while eliminating redundant detections caused by overlapping UAV imagery. The proposed framework was validated using a dataset comprising 2800 annotated UAV images collected from a 1 GW offshore photovoltaic project. The experimental results revealed a recall of 96.15%, an F1-score of 98.04%, and a manual verification consistency of 96.83%. Geographic deduplication eliminated duplicate grid records, while the average processing time of 1.12 s per image demonstrates the computational feasibility of the framework for large-scale offshore deployment. The results confirm that integrating deep learning-based visual detection with geographic spatial mapping enables reliable, scalable, and engineering-oriented verification of missing photovoltaic modules during construction-phase inspection, thereby supporting standardized and data-driven acceptance workflows for large-scale renewable energy infrastructure. Full article

In the area of transmission line inspection, one of the prominent areas of research has been to unite Unmanned Aerial Vehicles (UAVs) with neural network object detection algorithms. This area of research is challenging because of high computational resource consumption and poor infrared detection capabilities. In this study we propose an infrared image detection algorithm, named IISD-YOLO, using a modified version of the YOLOv11 network, to detect infrared transmission line insulator strings. Firstly, the original object detection layer was removed and replaced with the ShuffleNetv2 network to achieve the goal of a lightweight model; subsequently, based on the original feature extraction module C3k2, the Manhattan Self-Attention (MaSA) mechanism was introduced to design a new feature extraction module, C3k2-MaSA, which enhances the feature extraction capability for infrared objects; finally, the bidirectional feature pyramid network (Bi-FPN) is used to replace the original feature fusion module, enhancing the network’s ability to process and fuse information at different scales. The comparative experiments show that compared with the mainstream YOLO models, IISD-YOLO has improved by 4.5, 6.1, and 4.8 percentage points respectively on mAP@50 over YOLOv5, YOLOv8, and YOLOv10; furthermore, this model outperforms advanced models including YOLO-CIR, FA-YOLO, YOFIR, and RT-DETR, with improvements of 2.9, 9.1, 5.0, and 1.1 percentage points respectively on mAP@50. The ablation study shows that each improvement effectively enhances the overall performance. Compared with the original YOLOv11, the IISD-YOLO has increased its mAP@50 by 3.5 percentage points, while reducing the number of Params by 1.1 million and the computational GFLOPs by 2 G. These results confirm the superior performance of IISD-YOLO in infrared insulator string detection. Full article

Unmanned aerial vehicle (UAV)-based inspection is essential for transmission line maintenance, where object detection enables reliable identification of component states and defects. However, fog-induced degradation reduces image contrast and suppresses fine structural cues, thereby significantly degrading detection performance. To address this issue, we propose a robust detection framework, termed FogTLD-YOLO, for defect detection under foggy conditions. The proposed model adopts a degradation-adaptive enhancement strategy to mitigate feature deterioration. A fog-aware gated compensation module leverages frequency-domain priors to selectively compensate degraded regions, while a structural-positional enhancement pyramid preserves geometric continuity and positional sensitivity during feature aggregation. Together, these designs improve the representation of slender structures and small rgb]1,0,0objects. Extensive experiments show that FogTLD-YOLO achieves 82.1% mAP50, outperforming the best competitive algorithm by 2.8% with comparable efficiency. Comprehensive analyses, including module insertion strategies, gating design variants, convolutional branch configurations, and cross-architecture evaluations, further validate the effectiveness and general applicability of the proposed design for robust defect detection in foggy inspection scenarios. Full article

To address the challenges of significant vehicle parking constraints, limited UAV endurance, and insufficient multi-task coordination efficiency in distribution network inspection, this paper proposes a vehicle–UAV synchronous cooperative inspection task scheduling method based on multi-objective twin delayed deep deterministic policy gradient and nondominated sorting genetic algorithm II (MOTD3-NSGA-II). First, a vehicle–UAV synchronous cooperative inspection model is established by considering staged vehicle repositioning, same-site UAV launch, landing, and retrieval, as well as state-of-charge constraints. On this basis, a multi-objective optimization model is formulated with task coverage, mission completion time, minimum residual state of charge, and load balance as objectives. Then, a bi-level closed-loop solution framework is developed, in which NSGA-II is employed to optimize cooperative parameters and objective preference weights, while the inner-layer MOTD3 learns UAV scheduling policies in a continuous action space. Finally, the proposed method is validated in four simulation scenarios with different task scales and spatial distribution characteristics. The results show that 100% task coverage is achieved in all four scenarios, with mission completion times of 11,109 s, 9693 s, 10,538 s, and 10,721 s, respectively, while the minimum residual state of charge is maintained within 0.28–0.36. The results demonstrate that the proposed method can balance inspection completeness, execution efficiency, energy safety, and cooperative stability, providing a useful reference for intelligent task scheduling in vehicle–UAV cooperative distribution network inspection. Full article

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot ($300\times 180$ mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at $2.5$ Hz, (iii) continuous platform translation at $0.3$–$0.8$ m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight $\lambda \left(t\right)$; and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3° attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm). Full article

In vision-based low-altitude unmanned aerial vehicle (UAV) remote sensing, detecting small targets accurately and maintaining stable tracking under fast-motion conditions remain significant challenges. Specifically, small-object detection suffers from low feature representation, while camera motion often induces tracking drift and identity switches. To address these issues, this paper proposes a novel small target detection and tracking algorithm named TCYOLO-SofByteTrack, which integrates an improved YOLOv11 with ByteTrack. The algorithm comprises two core innovative modules: First, the TCYOLO detector is designed by integrating the C3k2-TA feature enhancement module with triplet attention mechanism to achieve cross-dimensional interaction modeling, significantly improving small target feature representation capability and network contextual awareness. A Cross-Scale Feature Fusion Module for UAVs (CCFM-UAV) is constructed to provide precise detection support for small targets at different scales. Second, building upon the ByteTrack framework, the SofByteTrack tracker is designed, which introduces a sparse optical flow-based motion compensation strategy. This strategy estimates and compensates for image displacement caused by UAV motion in real time, ensuring the stability of target bounding boxes under fast-motion conditions, thereby effectively mitigating tracking drift and identity switches. Experimental results demonstrate that the TCYOLO detector achieves a 7.4% improvement in mAP for small target detection compared to the baseline YOLOv11 model. The complete TCYOLO-SofByteTrack tracking algorithm achieves a HOTA score of 45.3%, MOTA of 42.7%, and IDF1 of 57.8%, representing improvements of 4.5%, 5.9%, and 8.0%, respectively, over the baseline methods. Furthermore, the number of successfully tracked targets increased by 37.3%, while identity switches decreased by 23.4%. These results demonstrate the notable advantages of the proposed method in small target detection accuracy, tracking precision, and identity consistency. Its generalization capability is further validated on a custom highway inspection dataset. Moreover, deployment tests on an NVIDIA Jetson Orin NX platform show that, compared to YOLOv11n, the proposed algorithm achieves higher detection accuracy while still meeting real-time processing requirements, highlighting its practical applicability in resource-constrained scenarios. Full article

Regular inspection of defects like sprayed concrete cracking and water seepage is crucial for the long-term safety of reservoir slopes in hydraulic engineering. Traditional manual inspections suffer from low efficiency and high cost. This paper presents a weighted Traveling Salesman Problem (TSP) model established by a Genetic Algorithm (GA) to optimize Unmanned Aerial Vehicle (UAV) inspection paths for these slopes. The model integrates UAV acceleration and deceleration physics. It weights the flight distance, converting it into flight time, and uses 3D-coordinate data to form the objective function. We calibrated key parameters, including acceleration and speed thresholds, by fitting displacement-time quadratic functions to field data from a DJI Matrice 350 RTK UAV. Tests on multiple slope models show the weighted GA optimizes the planned path by 46.2%, improves average inspection efficiency by 7.90% over an algorithm simulating human decision-making, and by 7.66% over a standard (non-weighted) GA. This work provides a reference for intelligent path planning on reservoir slopes and is applicable to similar scenarios like highway and railway slopes. Full article

The rapid proliferation of unmanned aerial vehicles (UAVs) in civilian sectors has generated diverse research spanning platform engineering, application deployment, and regulatory governance. This scoping review systematically maps the current knowledge landscape of civilian UAVs, their applications, and their regulatory frameworks, and aims to serve as initial practical guidance for researchers and practitioners initiating drone-based projects. Following PRISMA-ScR guidelines, a structured three-stream literature search was conducted using Google Scholar, yielding 109 sources published between 2015 and 2025. This review synthesises findings across three domains: (1) technical specifications, including UAV platform configurations, their common applications, their advantages and limitations, electromechanical systems, flight control architectures, and communication technologies, while also providing key guidance on how to choose the appropriate components for a given application; (2) civil applications across eight sectors—delivery logistics, infrastructure inspection, precision agriculture, environmental monitoring, emergency response, waste management, and commercial uses—to provide inspiration as well as to capture important details on drone projects; and (3) regulatory frameworks and ethical considerations governing UAV operations. Analysis reveals concentrated research attention on autonomy and AI-driven control systems and emerging focus on communication infrastructure. Geographic representation is dominated by US, European, and Chinese contexts, with limited coverage of developing regions. Key knowledge gaps include economic feasibility analyses, standardisation frameworks, developing-world deployment contexts, and environmental lifecycle assessments. Contradictions emerge between optimistic application scalability claims and fundamental constraints in energy storage, swarm communication reliability, and privacy–efficiency trade-offs. This review provides researchers and practitioners with a comprehensive map of current UAV knowledge, identifies critical research gaps, and establishes a foundation for future research in civilian drone technologies. This study aims to systematically consolidate and synthesise fragmented research on civilian UAV technologies, applications, and regulatory frameworks into a unified reference for research and practice. Full article

The rapid expansion of the new energy vehicle (NEV) market underscores a critical gap in the absence of a scientific health evaluation method for official inspections and annual checks. To address this, our study develops a comprehensive and quantitative health calibration system tailored for four specific application scenarios: annual inspection, battery health assessment, maintenance, and used car evaluation. Utilizing the Delphi method and Fuzzy Analytic Hierarchy Process (FAHP), we propose a construction method for a hierarchical and quantitative evaluation system. For each scenario, an independent quantitative evaluation table is established, identifying key indicators through a combination of specific operational contexts and expert opinions. The FAHP is then applied to determine the precise weights of these selected indicators, yielding a clear weighting structure for health metrics across different scenarios. This work culminates in a quantitative evaluation methodology for the health degree of in-use NEVs. By extending vehicle service life, reducing premature battery degradation, and enhancing safety, the proposed system directly supports the sustainable development of the NEV industry. It contributes to resource conservation, lower environmental impact, and greater consumer trust in green transportation. The proposed system is significant for fostering the healthy development of the NEV industry, enhancing vehicle safety and reliability, promoting technological progress, and strengthening consumer purchase confidence. Full article

Unmanned Aerial Vehicles (UAVs) have become a key technology in precision agriculture, enabling efficient monitoring, inspection, and targeted interventions. However, effective UAV path planning in such environments requires the generation of safe, energy-efficient, and smooth trajectories in complex three-dimensional spaces. This paper proposes an Adaptive Q-Learning Guided Gorilla Troops Optimizer (AQGTO) for 3D UAV path planning. The proposed method integrates a state-aware Q-learning mechanism into the Gorilla Troops Optimizer (GTO), enabling the optimizer to adaptively select exploration, exploitation, and diversification strategies according to the current optimization state. A multi-objective cost function is formulated to simultaneously minimize path length, an energy-related surrogate cost, obstacle proximity, path smoothness, and altitude variation. In addition, a feasibility repair mechanism is introduced to ensure collision-free trajectories in environments with cylindrical obstacles. The proposed approach is evaluated in three representative agricultural scenarios: row-crop fields, orchard environments, and hilly terrains. Experimental results show that AQGTO achieves competitive and improved performance compared with classical A*, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the original GTO in terms of trajectory cost, path efficiency, and stability. Furthermore, an ablation study confirms that the integration of Q-learning significantly enhances optimization performance. These results suggest that AQGTO provides an effective and robust solution for UAV path planning in complex agricultural environments. Full article

Insulator defect instance segmentation in unmanned aerial vehicle (UAV)-based power inspection scenarios remains challenging because of large target-scale variation, complex backgrounds, weak defect textures, and limited annotated samples. To examine whether common structural enhancement strategies can improve performance in this small-sample setting, this study investigates the applicability of two modifications to YOLOv11-seg: introducing a Large Selective Kernel (LSK) module into deep backbone stages and incorporating a P2 high-resolution feature map into the feature fusion network. Experiments were conducted on an expanded insulator defect instance segmentation dataset containing 836 images, including 138 images with defect instances. To reduce the influence of a single random partition, three independent stratified data splits were constructed, and all results were reported as mean ± standard deviation across the three splits. The results show that, within the YOLOv11-seg framework, none of the LSK-based, P2-based, or LSK+P2 variants provides a clear and consistent improvement over the baseline. Although some variants achieve slightly higher mean values in individual box-level metrics, the differences remain within the range of split-to-split variation and do not support a robust performance advantage. In addition, external comparisons with Mask R-CNN, pretrained YOLOv8s-seg, and pretrained YOLOv11s-seg provide a broader reference for the performance level of different instance segmentation frameworks under the current setting. The results show that YOLOv11s-seg remains competitive among YOLO-family models, while YOLOv8s-seg achieves slightly higher average performance. These findings suggest that increasing structural complexity does not necessarily lead to robust performance gains in small-sample and class-imbalanced insulator defect instance segmentation and that the practical value of structural modifications should be evaluated cautiously under repeated data splits. Full article