Search Results (2,403)

In this research, two inflatable-winged Unmanned Aerial Vehicles (UAVs) in distinct configurations, a single-fuselage layout with external trailing-edge control surfaces and a twin-fuselage layout with fully movable control surfaces were designed, developed, and flight tested to investigate the flight characteristics of inflatable-winged aircraft. Initially, inflatable wings were designed and fabricated from various materials, followed by rigorous ground testing, including structural characteristics tests, pressure retention and resistance tests, and low-speed wind-tunnel evaluations. Following this, two methods for controlling the inflatable wings were proposed, and their roll control effectiveness was thoroughly investigated. Subsequently, two inflatable-winged UAV prototypes, each employing a different configuration and manipulation method, were designed, assembled, and subjected to basic low-altitude flight tests to assess the feasibility of their aerodynamic layouts and control characteristics. The results demonstrated that a segmented wing design with a multi-boom configuration is particularly well-suited for inflatable wings. Additionally, both proposed control methods were tested and shown to be effective in flight. The findings provide valuable insights into the properties of inflatable wings and offer substantial guidance for the development of inflatable-winged aircraft. Full article

Camellia oleifera is an important economic tree species in China. Accurate estimation of canopy structural parameters of C. oleifera is essential for yield prediction and plantation management. However, this remains challenging in mountainous plantations due to canopy occlusion and background interference. This study developed a multi-level object-oriented segmentation method integrating UAV-based LiDAR and visible-light data to address this issue. The proposed approach progressively eliminates background objects (bare soil, weeds, and forest gaps) through hierarchical segmentation and classification in eCognition, ultimately enabling precise canopy delineation. The method was validated in a high-canopy-closure plantation characterized by a mountainous area. The results demonstrated exceptional performance; canopy area extraction and individual plant extraction achieved average F-scores of 97.54% and 91.69%, respectively. The estimated tree height and mean crown diameter were strongly correlated with field measurements (both R² = 0.75). This study provides a method for extracting the parameters of C. oleifera canopies that is suitable for mountainous regions with high canopy closure, demonstrating significant potential for supporting digital management and precision forestry optimization in such wooded areas. Full article

This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture. Full article

Lift–cruise-type unmanned aerial vehicles (UAVs) powered by hydrogen fuel cells often integrate secondary energy storage devices to improve responsiveness to load fluctuations during different flight phases, which necessitates an efficient energy management strategy that optimizes power allocation among multiple power sources. This paper presents an innovative fuel cell DC–DC converter (FDC) design for the hybrid power system of a lift–cruise-type UAV comprising a multi-stack fuel cell system and a battery. The novelty of this work lies in the development of an FDC suitable for a multi-stack fuel cell system through a dual-input single-output converter structure and a control algorithm. To integrate inputs supplied from two hydrogen fuel cell stacks into a single output, a controller with a single voltage controller–dual current controller structure was applied, and its performance was verified through simulations and experiments. Load balancing was maintained even under input asymmetry, and fault-tolerant performance was evaluated by analyzing the FDC output waveform under a simulated single-stack input failure. Furthermore, under the assumed flight scenarios, the results demonstrate that stable and efficient power supply is achieved through power-supply mode switching and application of a power distribution algorithm. Full article

In recent years, the use of Unmanned Aerial Vehicles (UAVs) has been widely investigated, with particular attention to their potential applications within smart city initiatives. In urban areas, UAV-based delivery services are expected to help address the shortage of truck drivers while also contributing to the promotion of carbon neutrality. Furthermore, the use of multiple UAVs as a communication platform through multi-hop UAV relaying has been studied. UAV-based communication platforms are gaining attention as cost-effective solutions in regions where deploying terrestrial base stations is challenging, such as mountainous areas and remote islands, as well as in emergency situations like natural disasters. Among UAV-based communication platforms, multi-hop UAV relaying is attracting attention as an effective means. However, when employing multi-hop UAV relaying, challenges arise in scenarios where the distance between the source and destination is large, including increased costs due to the need for a larger number of UAVs and reduced throughput caused by the increase in hop count. To address these issues, this paper proposes a flight path design for UAVs in a multi-hop communication system utilizing delivery UAVs, aiming to improve throughput between destinations. The proposed method targets communication between a source and multiple destinations by strategically placing relay points (Way Points: WPs) along the flight paths. By routing UAVs through WPs, new communication links are established, enabling the direct construction of networks between destinations. This approach reduces the number of hops and ensures stable communication at a constant speed. For WP placement algorithms, we propose two methods: a centroid-based method and a shortest-communication-distance-based method. Simulation results demonstrate that the proposed approach enhances throughput. Full article

This paper investigates the cooperative path-following control problem for multiple unmanned aerial vehicles (UAVs) under Global Navigation Satellite System (GNSS) denial conditions. To achieve equidistant distribution and uniform velocity motion within the swarm, a distributed control strategy based on Linear Matrix Inequalities (LMI) is proposed. Additionally, a novel virtual arc-length cooperation strategy is introduced, decomposing the formation maintenance problem into two subtasks: path following and velocity synchronization. This approach reduces control complexity and significantly minimizes frequent velocity cooperation issues caused by angular separation errors. To enable online estimation and compensation for model uncertainties and external disturbances, a USDE is incorporated, offering enhanced adaptability to time-varying disturbances. Simultaneously, a dynamic event-triggered mechanism (ETM) is designed to exchange neighbor information only when necessary, substantially reducing communication load. Global consistent ultimately bounded stability of the closed-loop system is rigorously proven using Lyapunov theory. Finally, validation results from the simulation platform demonstrate the proposed method’s certain feasibility and effectiveness in practical applications. Full article

Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person reports occur in parallel. The method extends iso-probability curve-based trajectory planning to the multi-target case and introduces a dynamic task allocation scheme that distributes search agents (e.g., UAVs) across tasks according to evolving probabilities of success. Overlapping search regions are explicitly resolved to eliminate duplicate coverage and to ensure balanced effort among tasks. The framework also extends the behavior-based motion prediction model for missing persons and the non-parametric estimator for iso-probability curves to capture more realistic search conditions. Extensive simulated experiments, with multiple concurrent tasks, demonstrate that the proposed method tangibly improves mean detection times compared with equal-allocation and individual static assignment strategies. Full article

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article

As a pioneering solution for precision agriculture, unmanned, robotics-centred greenhouse farms have become a key technological pathway for intelligent upgrades. The robotic arm is the core unit responsible for achieving full automation, and the level of technological development of this unit directly affects the productivity and intelligence of these farms. This review aims to systematically analyze the current applications, challenges, and future trends of robotic arms and their key technologies within unmanned greenhouse. The paper systematically classifies and compares the common types of robotic arms and their mobile platforms used in greenhouses. It provides an in-depth exploration of the core technologies that support efficient manipulator operation, focusing on the design evolution of end-effectors and the perception algorithms for plants and fruit. Furthermore, it elaborates on the framework for integrating individual robots into collaborative systems analyzing typical application cases in areas such as plant protection and fruit and vegetable harvesting. The review concludes that greenhouse robotic arm technology is undergoing a profound transformation evolving from single-function automation towards system-level intelligent integration. Finally, it discusses the future development directions highlighting the importance of multi-robot systems, swarm intelligence, and air-ground collaborative frameworks incorporating unmanned aerial vehicles (UAVs) in overcoming current limitations and achieving fully autonomous greenhouses. Full article

Accurate and comprehensive characterization of high-steep slopes is crucial for real-time risk prediction, disaster assessment, and damage evolution monitoring. The study focused on a high-steep rocky slope along the Yanjiang Expressway in Sichuan Province, China. A novel digital reconstruction method was introduced, which integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry through a Transformer-based method combining GeoTransformer with the Maximal Cliques (MAC) algorithm. The results indicated that TLS excels in capturing fine-scale features, whereas UAV demonstrates superior performance in large-scale terrain reconstruction. However, multi-sensor data exhibit heterogeneity in terms of partial overlap, large outliers, and density differences. To address these challenges, the GeoTransformer-MAC framework extracts geometrically invariant features from cross-source point cloud (CSPC) to establish initial correspondences, followed by rigorous screening of high-quality locally consistent correspondences to optimize transformation parameters. This method achieves accurate digital reconstruction of the high-steep rock slope. Global and local error analyses verify the model’s superiority in both overall slope characterization and fine-scale feature representation. Compared with the TLS-only model and the conventional method, the Transformer-based method improves the slope model integrity by 85.58%, increases the data density by 9.71%, and improves the accuracy by nearly threefold. This study provides a novel approach for the digital modeling of complex terrains, which serves the refined identification and modeling of geohazards for high-steep slopes in complex mountainous regions. Full article

Crop rotation is a cornerstone of sustainable agronomy, whereas continuous monoculture can degrade soil fertility and crop vigor. A three-year field experiment (2023–2025) in Lithuania compared winter rye grown in a long-term field experiment of continuous monoculture (with and without fertilizer/herbicide inputs) with five diversified rotation treatments that included manure, forage, or cover crop phases. Unmanned aerial vehicle (UAV) multispectral imaging was used to monitor crop health via the Normalized Difference Vegetation Index (NDVI, an indicator of plant vigor). NDVI measurements at three key developmental stages (flowering to ripening, BBCH 61–89) showed that diversified rotations consistently achieved higher NDVI than monoculture, indicating more robust crop growth. Notably, the most intensive and row-crop rotations had the highest canopy vigor, whereas continuous monocultures had the lowest. An anomalous weather year (2024) temporarily reduced NDVI differences, but rotation benefits re-emerged in 2025. Overall, UAV-based NDVI effectively captured rotation-induced differences in rye canopy vigor, highlighting the agronomic advantages of diversified cropping systems and the value of UAV remote sensing for crop monitoring. Full article

Urban green spaces have emerged as a key outdoor natural space supporting social interaction since the pandemic and lockdowns, and particularly benefit the health and well-being of the elderly and children amid global population aging. However, the challenges of socialization among multi-age groups and the influence of green space quality on these interactions remain insufficiently understood. To create healthy multi-age spaces, this study aimed to compare the associations between urban green space quality and social interaction among the elderly, children, and the general population. To achieve this, we adopted a UAV vision method, integrating the SOSIP protocol with a vision-based behavioral recognition model to capture outdoor social interactions across multiple age groups. Multilevel regression models were applied to analyze the hierarchical data structure and assess the contributions of different green space quality indicators, including green space size, facility, amenity, aesthetic features, maintenance and cleanliness, incivility, and overall quality. The findings indicated that overall green space quality is the most significant contributor in promoting social interaction, while maintenance and cleanliness appeared equally important to children and the general population. The presence of facilities and a larger green space size encourage informal encounters and facilitate collective activities only among the general population. However, aesthetic features and the number of amenities had limited effects on the social interactions of multi-age groups. These results suggest that how green space quality influences social interaction varies among different age groups. Accordingly, targeted green space quality enhancement strategies are proposed to support the planning of sustainable, healthy multi-age spaces that could balance the needs of both the elderly and younger populations. Full article

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference database. The challenge comes from the perspective inconsistency between matched objects. In this work, we propose a novel metric learning scheme for hard exemplar mining to improve the performance of cross-view geo-localization. Specifically, we introduce a Dynamic Online Cross-Batch (DOCB) hard exemplar mining scheme that solves the problem of the lack of hard exemplars in mini-batches in the middle and late stages of training, which leads to training stagnation. It mines cross-batch hard negative exemplars according to the current network state and reloads them into the network to make the gradient of negative exemplars participating in back-propagation. Since the feature representation of cross-batch negative examples adapts to the current network state, the triplet loss calculation becomes more accurate. Compared with methods only considering the gradient of anchors and positives, adding the gradient of negative exemplars helps us to obtain the correct gradient direction. Therefore, our DOCB scheme can better guide the network to learn valuable metric information. Moreover, we design a simple Siamese-like network called multi-scale feature aggregation (MSFA), which can generate multi-scale feature aggregation by learning and fusing multiple local spatial embeddings. The experimental results demonstrate that our DOCB scheme and MSFA network achieve an accuracy of 95.78% on the CVUSA dataset and 86.34% on the CVACT_val dataset, which outperforms those of other existing methods in the field. Full article

As a fundamental information carrier in Industrial Internet of Things (IIoT), electromagnetic spectrum data presents critical challenges for efficient spectrum sensing and situational awareness in smart industrial cognitive radio systems. Addressing sparse sampling limitations caused by energy-constrained transceiver nodes in Unmanned Aerial Vehicle (UAV) spectrum monitoring, this paper proposes a compressive sensing-based 3D spectrum tensor completion framework for extrapolative reconstruction in obstructed areas (e.g., building occlusions). First, a Sparse Coding Neural Gas (SCNG) algorithm constructs an overcomplete dictionary adaptive to wide-range spectral fluctuations. Subsequently, a Bag of Pursuits-optimized Orthogonal Matching Pursuit (BoP-OOMP) framework enables adaptive key-point sampling through multi-path tree search and temporary orthogonal matrix dimensionality reduction. Finally, a Neural Gas competitive learning strategy leverages intermediate BoP solutions for gradient-weighted dictionary updates, eliminating computational redundancy. Benchmark results demonstrate 43.2% reconstruction error reduction at sampling ratios r ≤ 20% across full-space measurements, while achieving decoupling of highly correlated overlapping subspaces—validating superior estimation accuracy and computational efficiency. Full article

Real-time object detection in Unmanned Aerial Vehicle (UAV) imagery is critical yet challenging, requiring high accuracy amidst complex scenes with multi-scale and small objects, under stringent onboard computational constraints. While existing methods struggle to balance accuracy and efficiency, we propose RTUAV-YOLO, a family of lightweight models based on YOLOv11 tailored for UAV real-time object detection. First, to mitigate the feature imbalance and progressive information degradation of small objects in current architectures multi-scale processing, we developed a Multi-Scale Feature Adaptive Modulation module (MSFAM) that enhances small-target feature extraction capabilities through adaptive weight generation mechanisms and dual-pathway heterogeneous feature aggregation. Second, to overcome the limitations in contextual information acquisition exhibited by current architectures in complex scene analysis, we propose a Progressive Dilated Separable Convolution Module (PDSCM) that achieves effective aggregation of multi-scale target contextual information through continuous receptive field expansion. Third, to preserve fine-grained spatial information of small objects during feature map downsampling operations, we engineered a Lightweight DownSampling Module (LDSM) to replace the traditional convolutional module. Finally, to rectify the insensitivity of current Intersection over Union (IoU) metrics toward small objects, we introduce the Minimum Point Distance Wise IoU (MPDWIoU) loss function, which enhances small-target localization precision through the integration of distance-aware penalty terms and adaptive weighting mechanisms. Comprehensive experiments on the VisDrone2019 dataset show that RTUAV-YOLO achieves an average improvement of 3.4% and 2.4% in mAP50 and mAP50-95, respectively, compared to the baseline model, while reducing the number of parameters by 65.3%. Its generalization capability for UAV object detection is further validated on the UAVDT and UAVVaste datasets. The proposed model is deployed on a typical airborne platform, Jetson Orin Nano, providing an effective solution for real-time object detection scenarios in actual UAVs. Full article