Drones, Volume 9, Issue 7 (July 2025) – 49 articles

The deployment of drones in civil airspaces has increased rapidly in recent years. However, their small radar cross-section hinders detectability by existing surveillance systems. This paper evaluates the feasibility of using additive manufacturing techniques with materials with enhanced electromagnetic properties to increase RADAR drone detectability. The examined methods include the production of propellers and landing gear with additive techniques and metal-doped plastics. Field experiments were performed measuring the radar detection range of an off-the-shelf drone with a 24 GHz RADAR module, and findings indicate that the use of specialized materials for drone manufacturing greatly improves drone detectability, increasing the detection range by a factor of 3.9 without changes to the drone design or inclusion of external electromagnetic reflectors. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

In this paper, unmanned aerial vehicle (UAV)-assisted vehicular communications are investigated to minimize latency and maximize the utilization of available UAV battery power. As communication and cooperation among UAV and vehicles is frequently required, a viable approach is to reduce the transmission of redundant messages. However, when the sensor data captured by the varying number of vehicles is not independent and identically distributed (non-i.i.d.), this becomes challenging. Hence, in order to group the vehicles with similar data distributions in a cluster, we utilize federated learning (FL) based on an attention mechanism. We jointly maximize the UAV’s available battery power in each transmission window and minimize communication latency. The simulation experiments reveal that the proposed personalized FL approach achieves performance improvement compared with baseline FL approaches. Our model, trained on the V2X-Sim dataset, outperforms existing methods on key performance indicators. The proposed FL approach with an attention mechanism offers a reduction in communication latency by up to 35% and a significant reduction in computational complexity without degradation in performance. Specifically, we achieve an improvement of approximately 40% in UAV energy efficiency, 20% reduction in the communication overhead, and 15% minimization in sojourn time. Full article

Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to capture the intricate spectral and structural heterogeneity of forest canopies, particularly at fine spatial resolutions. To address these limitations, we introduce ForestIQNet, a novel end-to-end multimodal deep learning framework designed to estimate AGB and associated carbon stocks from UAV-acquired imagery with high spatial fidelity. ForestIQNet combines dual-stream encoders for processing multispectral UAV imagery and a voxelized Canopy Height Model (CHM), fused via a Cross-Attentional Feature Fusion (CAFF) module, enabling fine-grained interaction between spectral reflectance and 3D structure. A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO₂e, capturing long-range spatial dependencies and enhancing generalization. Proposed method achieves an R² of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R² and 11.7 kg RMSE for XGBoost and 0.73 R² and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. These results establish a new benchmark for UAV-enabled biomass estimation and provide scalable, interpretable tools for climate monitoring and forest management. Full article

Object detection in drone-captured imagery has attracted significant attention due to its wide range of real-world applications, including surveillance, disaster response, and environmental monitoring. Although the majority of existing methods are developed under closed-set assumptions, and some recent studies have begun to explore open-vocabulary or open-world detection, their application to UAV imagery remains limited and underexplored. In this paper, we address this limitation by exploring the relationship between images and textual semantics to extend object detection in UAV imagery to an open-vocabulary setting. We propose a novel and efficient detector named Unmanned Aerial Vehicle Open-Vocabulary Detector (UAV-OVD), specifically designed for drone-captured scenes. To facilitate open-vocabulary object detection, we propose improvements from three complementary perspectives. First, at the training level, we design a region–text contrastive loss to replace conventional classification loss, allowing the model to align visual regions with textual descriptions beyond fixed category sets. Structurally, building on this, we introduce a multi-level text-guided fusion decoder that integrates visual features across multiple spatial scales under language guidance, thereby improving overall detection performance and enhancing the representation and perception of small objects. Finally, from the data perspective, we enrich the original dataset with synonym-augmented category labels, enabling more flexible and semantically expressive supervision. Experiments conducted on two widely used benchmark datasets demonstrate that our approach achieves significant improvements in both mean mAP and Recall. For instance, for Zero-Shot Detection on xView, UAV-OVD achieves 9.9 mAP and 67.3 Recall, 1.1 and 25.6 higher than that of YOLO-World. In terms of speed, UAV-OVD achieves 53.8 FPS, nearly twice as fast as YOLO-World and five times faster than DetrReg, demonstrating its strong potential for real-time open-vocabulary detection in UAV imagery. Full article

Predictive maintenance (PdM) is a proactive strategy that enhances safety, minimizes unplanned downtime, and optimizes operational costs by forecasting equipment failures before they occur. This study presents a novel Field Programmable Gate Array (FPGA)-accelerated predictive maintenance framework for UAV engines using a Singular Value Decomposition (SVD)-optimized Long Short-Term Memory (LSTM) model. The model performs binary classification to predict the likelihood of imminent engine failure by processing normalized multi-sensor data, including temperature, pressure, and vibration measurements. To enable real-time deployment on resource-constrained UAV platforms, the LSTM’s weight matrices are compressed using Singular Value Decomposition (SVD), significantly reducing computational complexity while preserving predictive accuracy. The compressed model is executed on a Xilinx ZCU-104 FPGA and uses a pipelined, AXI-based hardware accelerator with efficient memory mapping and parallelized gate calculations tailored for low-power onboard systems. Unlike prior works, this study uniquely integrates a tailored SVD compression strategy with a custom hardware accelerator co-designed for real-time, flight-safe inference in UAV systems. Experimental results demonstrate a 98% classification accuracy, a 24% reduction in latency, and substantial FPGA resource savings—specifically, a 26% decrease in BRAM usage and a 37% reduction in DSP consumption—compared to the 32-bit floating-point SVD-compressed FPGA implementation, not CPU or GPU. These findings confirm the proposed system as an efficient and scalable solution for real-time UAV engine health monitoring, thereby enhancing in-flight safety through timely fault prediction and enabling autonomous engine monitoring without reliance on ground communication. Full article

Despite decades of development, micro air vehicles (MAVs) still face challenges related to endurance. While solar power has been successfully implemented in larger aircraft as a clean and renewable source of energy, its adaptation to MAVs presents unique challenges due to payload constraints and complex surface geometries. To address this, this work proposes an automated algorithm for optimal solar panel arrangement on complex upper surfaces of the MAV. In addition to that, the aerodynamic performance is evaluated through computational fluid dynamics (CFD) simulations based on the Reynolds-Averaged Navier–Stokes (RANS) method. A multi-objective optimization approach simultaneously considers photovoltaic energy generation and aerodynamic efficiency. Wind tunnel validation and stability analysis of flight dynamics confirm the advantages of our optimized design. To our knowledge, this represents the first systematic framework for the energy–aerodynamic co-optimization of solar-powered MAVs (SMAVs). Flight tests of a $500\mathrm{mm}$-span tailless prototype demonstrate the practical feasibility of our approach with maximum solar cell deployment. Full article

In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for collaborative systems. However, current methods also overlook resource overload for heterogeneous units and limit planning to a single task chain in cross-platform rescue scenarios, resulting in low robustness and limited flexibility. To this end, this paper proposes Gemini, a cascaded dual-agent deep reinforcement learning (DRL) framework based on the Heterogeneous Service Network (HSN) for multiple task chains planning in UAV-UGV collaboration. Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. For each mission, a well-trained Gemini can not only allocate resources in load balancing but also plan multiple task chains simultaneously, which enhances the robustness in cross-platform rescue. Simulation results show that Gemini can increase rescue effectiveness by approximately 60% and improve load balancing by approximately 80%, compared to the baseline algorithm. Additionally, Gemini’s performance is stable and better than the baseline in various disaster scenarios, which verifies its generalization. Full article

The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach—FIM-DC (Fitting and Interpolation Model-based Data Correction)—specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions. Full article

Autonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exploration to obtain feasible and smooth paths for autonomous vehicles in 3D off-road environments. In our framework, we design a target selection strategy based on 3D terrain traversability analysis, and the traversability is evaluated by integrating vehicle dynamics with geometric indicators of the terrain. This strategy detects the frontiers within 3D environments and utilizes the traversability cost of frontiers as the pivotal weight within the clustering process, ensuring the accessibility of candidate points. Additionally, we introduced a more precise approach to evaluate navigation costs in off-road terrain. To obtain a smooth local path, we generate a cluster of local paths based on the global path and evaluate the optimal local path through the traversability and smoothness of the path. The method is validated in simulations and real-world environments based on representative off-road scenarios. The results demonstrate that our method reduces the exploration time by up to 36.52% and ensures the safety of the vehicle while exploring unknown 3D off-road terrain compared with state-of-the-art methods. Full article

Routing in flying ad hoc networks (FANETs) is hindered by high mobility, trajectory-induced topology dynamics, and energy constraints. Conventional topology-based or position-based protocols often fail due to stale link information and limited neighbor awareness. This paper proposes a trajectory-informed routing protocol enhanced by Q-learning: Traj-Q-GPSR, tailored for mission-oriented UAV swarm networks. By leveraging mission-planned flight trajectories, the protocol builds time-aware two-hop neighbor tables, enabling routing decisions based on both current connectivity and predicted link availability. This spatiotemporal information is integrated into a reinforcement learning framework that dynamically optimizes next-hop selection based on link stability, queue length, and node mobility patterns. To further enhance adaptability, the learning parameters are adjusted in real time according to network dynamics. Additionally, a delay-aware queuing model is introduced to forecast optimal transmission timing, thereby reducing buffering overhead and mitigating redundant retransmissions. Extensive ns-3 simulations across diverse mobility, density, and CBR connections demonstrate that the proposed protocol consistently outperforms GPSR, achieving up to 23% lower packet loss, over 80% reduction in average end-to-end delay, and improvements of up to 37% and 52% in throughput and routing efficiency, respectively. Full article

The construction of hydroelectric dams leads to substantial land-cover alterations, particularly through the removal of vegetation in wetland and valley areas. This results in exposed sediment that is susceptible to erosion, potentially leading to dust storms. While the reintroduction of vegetation plays a crucial role in restoring these landscapes and mitigating erosion, such efforts incur substantial costs and require detailed information to help optimize vegetation densities that effectively reduce dust storm risk. This study evaluates the performance of drones for measuring the growth of introduced low-lying grasses on reservoir beaches. A set of test flights was conducted to compare LiDAR and photogrammetry data, assessing factors such as flight altitude, speed, and image side overlap. The results indicate that, for this specific vegetation type, photogrammetry at lower altitudes significantly enhanced the accuracy of vegetation classification, permitting effective quantitative assessments of vegetation densities for dust storm risk reduction. Full article

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article

The integration of reconfigurable intelligent surfaces (RISs) with non-terrestrial networks (NTNs), particularly those enabled by unmanned aerial vehicles (UAVs) or drone-based platforms, has emerged as a transformative approach to enhance 6G connectivity in high-mobility scenarios. UAV-assisted NTNs offer flexible deployment, dynamic altitude control, and rapid network reconfiguration, making them ideal candidates for RIS-based signal optimization. However, the high mobility of UAVs and their three-dimensional trajectory dynamics introduce unique challenges in maintaining robust, low-latency links and seamless handovers. This paper presents a comprehensive performance analysis of RIS-assisted UAV-based NTNs, focusing on optimizing RIS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR), throughput, energy efficiency, and reliability under UAV mobility constraints. A joint optimization framework is proposed that accounts for UAV path loss, aerial shadowing, interference, and user mobility patterns, tailored specifically for aerial communication networks. Extensive simulations are conducted across various UAV operation scenarios, including urban air corridors, rural surveillance routes, drone swarms, emergency response, and aerial delivery systems. The results reveal that RIS deployment significantly enhances the SINR and throughput while navigating energy and latency trade-offs in real time. These findings offer vital insights for deploying RIS-enhanced aerial networks in 6G, supporting mission-critical drone applications and next-generation autonomous systems. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in the new generation of communication systems. They serve as access points, base stations, relays, and gateways to extend network coverage, enhance connectivity, or offer communications services in places lacking telecommunication infrastructure. However, optimizing UAV placement in three-dimensional (3D) environments with diverse user distributions and uneven terrain conditions is a crucial challenge. Therefore, this paper proposes a novel framework to minimize UAV transmission power while ensuring a guaranteed data rate in realistic and complex scenarios. To this end, using the particle swarm optimization evolution (PSO-E) algorithm, this paper analyzes the impact of user-truncated distribution models for suburban, urban and dense urban environments. Extensive simulations demonstrate that dense urban environments demand higher power than suburban and urban environments, with uniform user distributions requiring the most power in all scenarios. Conversely, Gaussian and exponential distributions exhibit lower power requirements, particularly in scenarios with concentrated user hotspots. The proposed model provides insight into achieving efficient network deployment and power optimization, offering practical solutions for future communication networks in complex 3D scenarios. Full article

Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in both governmental and civilian applications, offering significant reductions in operational costs by minimizing human involvement. There is a growing demand for autonomous, scalable, and intelligent coordination strategies in complex aerial missions involving multiple Unmanned Aerial Vehicles (UAVs). Traditional control techniques often fall short in dynamic, uncertain, or large-scale environments where decentralized decision-making and inter-agent cooperation are crucial. A potentially effective technique used for UAV fleet operation is Multi-Agent Reinforcement Learning (MARL). MARL offers a powerful framework for addressing these challenges by enabling UAVs to learn optimal behaviors through interaction with the environment and each other. Despite significant progress, the field remains fragmented, with a wide variety of algorithms, architectures, and evaluation metrics spread across domains. This survey aims to systematically review and categorize state-of-the-art MARL approaches applied to UAV control, identify prevailing trends and research gaps, and provide a structured foundation for future advancements in cooperative aerial robotics. The advantages and limitations of these techniques are discussed along with suggestions for further research to improve the effectiveness of MARL application to UAV fleet management. Full article

Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in Mentha arvensis using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. Among the tested models, Random Forest achieved the highest predictive accuracy (R² = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R² = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (15–30 DAP) but improved markedly from mid to late growth stages (45–90 DAP). The findings highlight the significance of UAV-acquired data coupled with machine learning approaches for non-destructive nitrogen flavanol estimation, which can immensely contribute to improving real-time crop growth monitoring. Full article

Fish kills are sudden mass mortalities that occur in freshwater and marine systems worldwide. Fish kill surveys are essential for assessing the ecological and economic impacts of fish kill events, but are often labor-intensive, time-consuming, and spatially limited. This study aims to address these challenges by exploring the application of unoccupied aerial systems (or drones) and deep learning techniques for coastal fish carcass detection. Seven flights were conducted using a DJI Phantom 4 RGB quadcopter to monitor three sites with different substrates (i.e., sand, rock, shored Sargassum). Orthomosaics generated from drone imagery were useful for detecting carcasses washed ashore, but not floating or submerged carcasses. Single shot multibox detection (SSD) with a ResNet50-based model demonstrated high detection accuracy, with a mean average precision (mAP) of 0.77 and a mean average recall (mAR) of 0.81. The model had slightly higher average precision (AP) when detecting large objects (>42.24 cm long, AP = 0.90) compared to small objects (≤14.08 cm long, AP = 0.77) because smaller objects are harder to recognize and require more contextual reasoning. The results suggest a strong potential future application of these tools for rapid fish kill response and automatic enumeration and characterization of fish carcasses. Full article

This paper presents the design, fabrication, simulation, and partial validation of a low-cost, fixed-wing unmanned glider equipped for temperature and image monitoring. Aerodynamic optimization was performed using XFLR5 and ANSYS Fluent 2023 R1, with spanwise variation between NACA 63(3)-618 and NACA 4415 to enhance performance. Wind tunnel tests of the selected airfoil showed good agreement with CFD predictions, with deviations within 5–10%. The airframe, fabricated using 3D-printed PLA with a cross-lattice structure, was integrated with an ESP32-CAM and temperature sensor. A reflective thermal coating was applied to mitigate the heat sensitivity of PLA. Propeller-induced flow was analyzed separately using the lattice Boltzmann method. Real-time flight behavior was simulated in a virtual environment via Simulink and FlightGear. While full in-flight testing is pending, the results demonstrate a scalable, open-source UAV platform for environmental monitoring and academic research. Full article

Unmanned aerial vehicles (UAVs) autonomous landing plays a key role in cooperative work with other heterogeneous agents. A neglected aspect of UAV autonomous landing on a moving platform is addressed in this study. The landing process is divided into three stages: positioning, tracking, and landing. In the tracking phase, MPCs are designed to implement tracking of the target landing platform. In the landing phase, we adopt a nested Apriltags collaboration identifier combined with the Aprilatags algorithm to design a PID speed controller, thereby improving the dynamic tracking accuracy of UAVs and completing the landing. The experimental data suggested that the method enables the UAV to perform dynamic tracking and autonomous landing on a moving platform. The experimental results show that the success rate of UAV autonomous landing is as high as 90%, providing a highly feasible solution for UAV autonomous landing. Full article

Within the field of remote sensing, Unmanned Aerial Vehicle (UAV) infrared object detection plays a pivotal role, especially in complex environments. However, existing methods face challenges such as insufficient accuracy or low computational efficiency, particularly in the detection of small objects. This paper proposes a lightweight and accurate UAV infrared object detection model, YOLO-UIR, for small object detection from a UAV perspective. The model is based on the YOLO architecture and mainly includes the Efficient C2f module, lightweight spatial perception (LSP) module, and bidirectional feature interaction fusion (BFIF) module. The Efficient C2f module significantly enhances feature extraction capabilities by combining local and global features through an Adaptive Dual-Stream Attention Mechanism. Compared with the existing C2f module, the introduction of Partial Convolution reduces the model’s parameter count while maintaining high detection accuracy. The BFIF module further enhances feature fusion effects through cross-level semantic interaction, thereby improving the model’s ability to fuse contextual features. Moreover, the LSP module efficiently combines features from different distances using Large Receptive Field Convolution Layers, significantly enhancing the model’s long-range information capture capability. Additionally, the use of Reparameterized Convolution and Depthwise Separable Convolution ensures the model’s lightweight nature, making it highly suitable for real-time applications. On the DroneVehicle and HIT-UAV datasets, YOLO-UIR achieves superior detection performance compared to existing methods, with an mAP of 71.1% and 90.7%, respectively. The model also demonstrates significant advantages in terms of computational efficiency and parameter count. Ablation experiments verify the effectiveness of each optimization module. Full article

To address the dynamic decision-making and control problem in unmanned aerial vehicle (UAV) swarms, this paper proposes a cumulative prospect theory-driven pigeon-inspired optimization (CPT-PIO) algorithm. Gray relational analysis and information entropy theory are integrated into cumulative prospect theory (CPT), constructing a prospect value model for Pareto solutions by setting reference points, defining value functions, and determining attribute weights. This prospect value is used to evaluate the quality of each Pareto solution and serves as the fitness function in the pigeon-inspired optimization (PIO) algorithm to guide its evolutionary process. Furthermore, incorporating individual and swarm situation assessment methods, the situation assessment model is constructed and the information entropy theory is employed to ascertain the weight of each assessment index. Finally, the reverse search mechanism and competitive learning mechanism are introduced into the standard PIO to prevent premature convergence and enhance the population’s exploration capability. Simulation results demonstrate that the proposed CPT-PIO algorithm significantly outperforms two novel multi-objective optimization algorithms in terms of search performance and solution quality, yielding higher-quality Pareto solutions for dynamic UAV swarm decision-making. Full article

This paper investigates the robust tracking control of unmanned aerial vehicles (UAVs) against external time-varying disturbances. First, by introducing a virtual position controller, we innovatively decouple the UAV dynamics into independent position and attitude error subsystems, transforming the robust tracking problem into two zero-sum differential games. This approach contrasts with conventional methods by treating disturbances as strategic “players”, enabling a systematic framework to address both external disturbances and model uncertainties. Second, we develop an integral reinforcement learning (IRL) framework that approximates the optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equations without relying on precise system models. This model-free strategy overcomes the limitation of traditional robust control methods that require known disturbance bounds or accurate dynamics, offering superior adaptability to complex environments. Third, the proposed recursive Ridge regression with a forgetting factor (${\mathrm{R}}^3{\mathrm{F}}^2$ ) algorithm updates actor-critic-disturbance neural network (NN) weights in real time, ensuring both computational efficiency and convergence stability. Theoretical analyses rigorously prove the closed-loop system stability and algorithm convergence, which fills a gap in existing data-driven control studies lacking rigorous stability guarantees. Finally, numerical results validate that the method outperforms state-of-the-art model-based and model-free approaches in tracking accuracy and disturbance rejection, demonstrating its practical utility for engineering applications. Full article

This study introduces an advanced automated guided vehicle (AGV) specifically designed for application in recirculating aquaculture systems (RASs). The proposed AGV seamlessly integrates automated feeding, real-time monitoring, and an intelligent path-planning system to enhance operational efficiency. To achieve optimal and adaptive navigation, a hybrid algorithm is developed, incorporating Newton–Raphson-based optimisation (NRBO) alongside ant colony optimisation (ACO). Additionally, dueling deep Q-networks (dueling DQNs) dynamically optimise critical parameters, thereby improving the algorithm’s adaptability to the complexities of RAS environments. Both simulation-based and real-world experiments substantiate the system’s effectiveness, demonstrating superior convergence speed, path quality, and overall operational efficiency compared to traditional methods. The findings of this study highlight the potential of AGV to enhance precision and sustainability in recirculating aquaculture management. Full article

This paper proposes a cluster-based flight path construction method for automated drone-assisted pear pollination systems in orchard environments. The approach uses RGB-D (Red-Green-Blue-Depth) sensing through an observation drone equipped with RGB and depth cameras to detect blooming pear flowers. Flower detection is performed using a YOLO (You Only Look Once)-based object detection algorithm, and three-dimensional flower positions are estimated by integrating depth information with the drone’s positional and orientation data in the east-north-up coordinate system. To enhance pollination efficiency, the method applies the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to group detected flowers based on spatial proximity that correspond to branch-level distributions. The cluster centroids then construct a collision-free flight path, with offset vectors ensuring safe navigation and appropriate nozzle orientation for effective pollen spraying. Field experiments conducted using RTK-GNSS-based flight control confirmed the accuracy and stability of generated flight trajectories. The drone hovered in front of each flower cluster and performed uniform spraying along the planned path. The method achieved a fruit set rate of 62.1%, exceeding natural pollination at 53.6% and compared to the 61.9% of manual pollination. These results demonstrate the effectiveness and practicability of the method for real-world deployment in pear orchards. Full article

This paper presents a three-dimensional robust spatiotemporal cooperative guidance law for unmanned aerial vehicles (UAVs) to track a dynamic target under jointly strongly connected topologies, even when some UAVs malfunction. To resolve the infinite gain challenge in existing prescribed-time cooperative guidance laws, a novel bounded-gain prescribed-time stability criterion was formulated. This criterion allows the convergence time of the guidance law to be prescribed arbitrarily without any convergence performance trade-off. Firstly, new prescribed-time disturbance observers are designed to achieve accurate estimations of the target acceleration within a prescribed time regardless of initial conditions. Then, by leveraging a distributed convex hull observer, a tangential acceleration command is proposed to drive arrival times toward a common convex combination within a prescribed time under jointly strongly connected topologies, remaining effective even when partial UAVs fail. Moreover, by utilizing a prescribed-time nonsingular sliding mode control method, normal acceleration commands are developed to guarantee that the line-of-sight angles constraints can be satisfied within a prescribed time. Finally, numerical simulations validate the effectiveness of the proposed guidance law. Full article

Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we experiment with autonomous UAV search for humans in Mediterranean karst environment. The UAVs are directed using the Heat equation-driven area coverage (HEDAC) ergodic control method based on known probability density and detection function. The sensing framework consists of a probabilistic search model, motion control system, and object detection enabling to calculate the target’s detection probability. This paper focuses on the experimental validation of the proposed sensing framework. The uniform probability density, achieved by assigning suitable tasks to 78 volunteers, ensures the even probability of finding targets. The detection model is based on the You Only Look Once (YOLO) model trained on a previously collected orthophoto image database. The experimental search is carefully planned and conducted, while recording as many parameters as possible. The thorough analysis includes the motion control system, object detection, and search validation. The assessment of the detection and search performance strongly indicates that the detection model in the UAV control algorithm is aligned with real-world results. Full article

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 and 2025, high-resolution images were combined with dense point clouds produced by Structure from Motion (SfM) methods. Iterative Closest Point (ICP) registration (RMSE = 2.09 cm) and Multiscale Model-to-Model Cloud Comparison (M3C2) analysis was used to quantify the surface changes. The study found a volumetric increase of 7744.04 m³ in the dump zones accompanied by an excavation loss of 8359.72 m³, so producing a net difference of almost 615.68 m³. Surface risk factors were evaluated holistically using a variety of morphometric criteria. These measures covered surface variation in several respects: their degree of homogeneity, presence of any unevenness or texture, verticality, planarity, and linearity. Surface variation > 0.20, roughness > 0.15, and verticality > 0.25 help one to identify zones of increased instability. Point cloud modeling derived from UAVs and GIS-based spatial analysis were integrated to show that morphological anomalies are spatially correlated with possible failure zones. Full article

With the rapid development of the low-altitude economy and smart logistics, unmanned aerial vehicles (UAVs), as core low-altitude platforms, have been widely applied in urban delivery, emergency rescue, and other fields. Although path planning in complex environments has become a research hotspot, optimization and scheduling of UAVs under time window constraints and task assignments remain insufficiently studied. To address this issue, this paper proposes an improved algorithmic framework based on a two-layer structure to enhance the intelligence and coordination efficiency of multi-UAV path planning. In the lower layer path planning stage, considering the limitations of the whale optimization algorithm (WOA), such as slow convergence, low precision, and susceptibility to local optima, this study integrates a backward learning mechanism, nonlinear convergence factor, random number generation strategy, and genetic algorithm principle to construct an improved IWOA. These enhancements significantly strengthen the global search capability and convergence performance of the algorithm. For upper layer task assignment, the improved ALNS (IALNS) addresses local optima issues in complex constraints. It integrates K-means clustering for initialization and a simulated annealing mechanism, improving scheduling rationality and solution efficiency. Through the coordination between the upper and lower layers, the overall solution flexibility is improved. Experimental results demonstrate that the proposed IALNS-IWOA two-layer method outperforms the conventional IALNS-WOA approach by 7.30% in solution quality and 7.36% in environmental adaptability, effectively improving the overall performance of UAV trajectory planning. Full article

Unmanned Aerial Vehicle (UAV) image semantic segmentation faces challenges in recognizing novel categories due to closed-set training paradigms and the high cost of annotation. While open-vocabulary semantic segmentation (OVSS) leverages vision-language models like CLIP to enable flexible class recognition, existing methods are limited to low-resolution images, hindering their applicability to high-resolution UAV data. Current adaptations—downsampling, cropping, or modifying CLIP—compromise either detail preservation, global context, or computational efficiency. To address these limitations, we propose HR-Seg, the first high-resolution OVSS framework for UAV imagery, which effectively integrates global context from downsampled images with local details from cropped sub-images through a novel cost-volume architecture. We introduce a detail-enhanced encoder with multi-scale embedding and a detail-aware decoder for progressive mask refinement, specifically designed to handle objects of varying sizes in aerial imagery. We evaluated existing OVSS methods alongside HR-Seg, training on the VDD dataset and testing across three benchmarks: VDD, UDD, and UAVid. HR-Seg achieved superior performance with mIoU scores of 89.38, 73.67, and 55.23, respectively, outperforming all compared state-of-the-art OVSS approaches. These results demonstrate HR-Seg’s exceptional capability in processing high-resolution UAV imagery. Full article