Search Results (2,218)

The increasing activity of unmanned aerial vehicles (UAVs) has intensified the demand for reliable and computationally efficient methods for passive radio-frequency (RF) signal detection. In practical RF monitoring scenarios, the environment is often non-stationary and affected by varying noise conditions. Under such circumstances, classical energy-based detectors are sensitive to noise uncertainty, while more robust approaches, such as cyclostationary analysis, require substantially higher computational resources. This work presents a burst-aware cascade method for UAV RF signal presence detection that explicitly addresses this trade-off. The proposed framework combines fast energy-based screening with temporal burst aggregation, applying spectral correlation function (SCF) analysis selectively and only when sustained signal activity is indicated. Detection is performed on fixed-length RF signal chunks, while additional segment-level duration constraints are used to characterize sustained transmissions. The method is evaluated using the publicly available DroneRF dataset and compared against six baseline detectors, including fixed-threshold energy, wavelet-based, blind cyclostationary, two adaptive energy detector variants, and a lightweight convolutional neural network. Experimental results confirm that chunk-level detection remains difficult for all considered methods. Temporal aggregation across longer intervals yields a substantial improvement: the cascade achieves $P_d$ = 1.000 and AUC = 1.000 at the segment level, matching exhaustive cyclostationary detection while reducing per-segment processing time by a factor of 2.46. An additional result is that burst-level concatenation prior to SCF estimation provides implicit coherent integration, preserving $P_d$ = 1.000 at signal amplitude reductions of up to −20 dB where standalone detectors degrade to $P_d$ = 0.995. Overall, burst-aware cascade architectures offer a practical and interpretable approach to RF-based UAV monitoring, providing a well-grounded compromise between detection reliability and computational efficiency under realistic operating conditions. Full article

Precise and continuous monitoring of thermal effects are critical for ensuring the structural safety of bridges and preventing potential failures. This study presents a methodology integrating unmanned aerial vehicle (UAV)-based thermal measurements with interferometric synthetic aperture radar (InSAR) satellite data to assess and monitor the thermomechanical response of bridges. A three-dimensional (3D) finite element model (FEM) of a prestressed concrete (PC) bridge was developed and validated using in situ displacement measurements. High-resolution, 3D temperature distributions of bridge elements were obtained daily and seasonally using UAV-based infrared thermography (UAV–IRT). Thermal maps were validated with point temperature measurements on the structure. Simultaneously, long-term wide-area deformation trends were investigated using satellite-based InSAR observations. The thermo-mechanical displacement behavior derived from UAV–IRT measurements was compared with historical InSAR-derived seasonal deformation patterns to develop an integrated multi-source structural monitoring framework. The behavior of the bridge in daily and seasonal temperature cycles was simulated and analyzed by integrating UAV–IRT thermal load data into FEM. Maximum stress levels occurring under the most adverse thermal loading conditions and over a one-year period were calculated, taking into account stress limits. The FEM revealed a maximum vertical displacement of 12.3 mm under extreme thermal loading, with tensile stresses in the deck mid-depth exceeding the 3.5 MPa limit, signaling a potential risk for thermally induced cracking. Integration of UAV–IRT thermal observations and historical InSAR deformation measurements revealed vertical temperature gradients of up to 24 °C during summer conditions and indicated that the observed structural response was predominantly governed by thermo-elastic deformation. UAV-satellite methodology offers a rapid, economical, and comprehensive solution for the structural health monitoring of bridges exposed to thermal effects. Full article

Currently, multiple unmanned aerial vehicles (UAVs) can cooperatively work as mobile edge computing (MEC) servers in the sky to provide computation services to ground terminals (GTs). Such an MEC-enabled multi-UAV system will greatly benefit the GTs, each of which can offload its tasks on demand to a nearby UAV. In particular, if a GT has to process computation-intensive deep learning tasks in a catastrophic environment, it can partially offload these tasks to UAVs using a scheme like Partial Program Offloading (PPO). This ensures the quick processing of the deep learning tasks while saving computing resources on both the GT and UAV sides. Nevertheless, UAV–GT offloading links are frequently blocked by ground obstacles in complicated environments, and individual UAVs may have limited computation capacity. Moreover, UAVs lack a constant propulsion energy supply to sustain a long mission time. All these factors lead to a degraded Quality of Service (QoS) for GTs in terms of task latency. To address this issue, we propose to jointly optimize the UAV trajectories, computing resource allocation, and the partial offloading of deep learning tasks. The formulated joint optimization problem is challenging to solve optimally, as it is non-convex and involves multiple coupled constraints. We propose utilizing the Successive Convex Approximation (SCA) method alongside a Block Coordinate Descent (BCD) approach to tackle this joint problem. Numerical results demonstrate that the proposed joint optimization scheme significantly outperforms the benchmark solutions. Full article

Classification of ground and low-altitude targets in radar remote sensing is challenging because environmental clutter and noise can significantly degrade the discriminability of target echoes, especially under complex outdoor observation conditions. To improve the classification performance for humans, vehicles, and unmanned aerial vehicles (UAVs), this paper proposes a velocity-aware multi-feature fusion method based on measured radar echo data. First, radar echoes are preprocessed using a wavelet-decomposition-based strategy to suppress clutter and noise while preserving useful target information. Then, multiple complementary features, including wavelet packet energy distribution, spectral entropy, spectral standard deviation, temporal standard deviation, amplitude dispersion coefficient, and relative radar cross-section (RCS), are extracted to characterize the target echoes from different perspectives. Considering the influence of target velocity on Doppler distribution and class separability, the measured data are further divided into different velocity intervals for stratified classification. Based on the fused feature vectors, a long short-term memory (LSTM) network is employed to model feature relationships and perform target classification. Experiments conducted on real measured radar echo data demonstrate that the proposed method achieves classification accuracies of 97.82% for UAVs, 96.00% for vehicles, and a mean interval-level accuracy of 96.94%, indicating its effectiveness for ground and low-altitude target classification in cluttered radar remote sensing environments. Full article

As the core technology for realizing autonomous navigation of unmanned ground vehicles, the path planning algorithm directly determines the reliability and stability of navigation tasks in complex dynamic environments. With the expanding range of application scenarios, traditional path planning approaches have become increasingly inadequate in terms of real-time performance, dynamic obstacle avoidance, and multi-objective optimization. The recent rise in AI-based methods has provided new opportunities for this field. This paper systematically analyzes the latest research progress in this area. By reviewing and analyzing the highly recognized literature in recent years, we classify mainstream path planning and related algorithms into six types: graph-search-based, sampling-based, local optimization-based, meta-heuristic optimization, AI-based, and optimal control methods. The core improvement trends, advantages, and inherent limitations of each algorithm type are deeply analyzed. Through bibliometric analysis, we identify major gaps in current research, including over-reliance on simulation methods, overly restrictive environmental assumptions, and insufficient handling of multiple objectives. Finally, we point out the critical gap between simulation environments and real-world deployment and advocate the use of hybrid algorithms to address the deficiencies of single algorithms, along with effective validation in real environments. This direction is crucial for promoting the broader practical application of unmanned ground vehicle technology. Full article

Remote sensing of crop diseases has traditionally focused on detecting visible symptoms, often limiting intervention to advanced stages of epidemic development. This study investigates whether high-resolution unmanned aerial vehicles (UAV)-based red–green–blue (RGB) imagery can reveal earlier physiological destabilization preceding visible symptoms of wheat stripe rust and wheat leaf rust. UAV imagery was acquired at four winter wheat-growing sites in Luxembourg during the 2018/2019 season. Temporal dynamics of green–red spectral slopes were analyzed and compared with ground-based disease severity observations to identify potential pre-symptomatic spectral signals. A consistent flattening of the green–red spectral slope was detected prior to a rapid increase in visually assessed severity for both diseases. However, the length of this pre-symptomatic window varied between the two diseases: it lasted 7 to 14 days for wheat stripe rust and 5 to 10 days for wheat leaf rust. Likewise, the reduction in spectral slope magnitude was slightly greater for wheat stripe rust (65–80%) than for wheat leaf rust (60–75%), indicating that the temporal lead time and intensity of the spectral response were disease-dependent. During the pre-symptomatic phase, the spectral dynamics reflected latent physiological changes rather than visible disease severity. Strong correlations emerged only after the epidemic transition. These findings demonstrate that UAV-based RGB imagery could capture a distinct pre-symptomatic phase of stripe rust and leaf rust epidemics in winter wheat. Interpreting RGB spectral dynamics as early-warning indicators rather than merely as static severity proxies can guide proactive disease monitoring and precision agriculture. Full article

With the rapid development of unmanned aerial vehicle (UAV) remote sensing and deep learning, road crack detection has become an important component of road condition assessment and intelligent road maintenance. However, accurately detecting cracks from UAV images remains challenging due to complex background environments, slender crack structures, blurred boundaries, and irregular crack shapes and orientations. Traditional methods that rely solely on visual information often struggle to achieve stable and accurate detection performance under these conditions. To address these challenges, this paper proposes a Multimodal Crack Geometry-Consistent Network (MCGC-Net) for high-precision road crack detection in complex road scenes. First, a UAV-based multimodal road crack dataset with image-text annotations is constructed. Specifically, crack-related textual descriptions are automatically generated from crack annotations using predefined semantic templates, which summarize crack morphology, spatial distribution characteristics, and structural properties. These semantic descriptions provide high-level semantic prior information for crack representation learning. Second, a Multimodal Contrastive Semantic Gating module (MCSG) is introduced to leverage automatically generated crack semantic descriptions and in-batch image-text semantic differences to guide visual feature learning, thereby improving the discrimination between crack and non-crack regions under complex background conditions. Furthermore, a Crack-Aware Slenderness Loss (CASL) is proposed to explicitly constrain slenderness consistency between predicted boxes and ground-truth boxes, improving localization stability for slender crack targets. In addition, a KAN-based Nonlinear Channel Attention mechanism (KAN-CA) is introduced to enhance feature representation capability for complex crack structures. Experimental results demonstrate that the proposed MCGC-Net effectively improves crack detection accuracy and structural representation capability under complex road environments. The proposed method provides a practical and reliable solution for UAV-based intelligent road crack detection. Full article

Accurate and timely monitoring of soil salinity is of great significance for the ecological restoration of saline-alkali land and precision agricultural management. In this study, a typical coastal saline-alkali farmland located in Huanghua City, Hebei Province, China, in the Bohai coastal region, was selected as the study area. High-resolution images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor, and ground soil salinity samples were collected synchronously. Based on the construction of a feature library comprising spectral reflectance, vegetation indices, and salinity indices, three algorithms, PSO-SFLA, MultiSURF, and VIP, were employed for feature selection. Subsequently, an ensemble model was established, utilizing Ridge Regression (Ridge), Random Forest (RF), and Extra Trees (ET) as primary base learners, and Extreme Gradient Boosting (XGBoost) as the secondary meta-learner. This ensemble model was applied for soil salinity inversion. Furthermore, the coefficient of determination (R²), standardized root mean square error (S_RMSE), and the ratio of performance to interquartile distance (RPIQ) were introduced to comprehensively evaluate the accuracy of the models. Finally, the intrinsic physical responses of the features were explored through SHAP. The results showed that the optimization by the PSO-SFLA effectively reduced the impact of spectral multicollinearity, and 11 core features highly sensitive to salinity were selected from a vast number of indices. The ensemble model showed better predictive performance on the independent test set, achieving an R² of 0.758, an S_RMSE of 0.285, and an RPIQ of 3.382, outperforming the single Ridge, RF, and ET models under the current experimental conditions. Based on this model, the spatial distribution map of soil salinity in the experimental area was generated. The integrated and interpretable workflow proposed in this study, combining UAV multispectral imagery, PSO-SFLA-based feature selection, ensemble learning, and SHAP interpretation, provides a practical approach for accurate soil salinity inversion and dynamic agricultural monitoring in coastal saline-alkali lands. Full article

Landslides are among the most destructive geological hazards in Malaysia, especially in mountainous and forested areas. Unmanned aerial vehicle (UAV) imagery offers high spatial resolution and flexible data capture, but deep learning performance is highly sensitive to training hyperparameters. In this study, the TransU-Net model for UAV-based landslide detection was adopted and a systematic ablation study on learning-rate and epoch settings using a coarse-to-fine tuning strategy. The Berembun Forest Reserve dataset was first used to determine the optimal training configuration. Then, the optimised configuration was tested on multiple UAV sub-datasets in the CAS Landslide dataset to evaluate performance stability under different terrain properties and spatial resolutions. The optimised configuration yielded the best F1-score (0.9598) and IoU of 0.9507 on the Berembun Forest Reserve dataset, and consistently high F1-scores across the evaluated CAS Landslide sub-datasets. Qualitative visualisation analysis also revealed good spatial correspondence between the predicted segmentation masks and the ground-truth annotations. Variations in Intersection over Union (IoU) values were mainly associated with boundary delineation uncertainty rather than severe misclassification. Overall, the results show that the performance of UAV-based landslide segmentation can improve by systematic hyperparameter tuning, and the optimised TransU-Net configuration under the evaluated terrain conditions yields promising results. Full article

This paper investigates time-varying output formation (TVOF) tracking for heterogeneous small fixed-wing unmanned aerial vehicles (UAVs) and nonholonomic unmanned ground vehicles (UGVs). The small fixed-wing UAVs operate in three-dimensional space, and the UGVs move on a two-dimensional plane, leading to heterogeneous dynamics with nonholonomic constraints, asymmetric velocity constraints, and input saturation. To address these challenges, distributed adaptive control protocols are developed under switching directed communication topologies. Unlike existing TVOF tracking methods that require global information, the proposed protocols do not rely on the upper bound of the leader’s unknown input or the eigenvalues of the Laplacian matrix. A constructive parameter-selection algorithm is provided, and the closed-loop stability is established using Lyapunov theory. Numerical simulations involving heterogeneous UAV-UGV formations verify that the proposed method achieves TVOF tracking under random disturbance while satisfying the prescribed motion constraints. Full article

Heterogeneous airground robotic systems, which integrate unmanned ground vehicles and unmanned aerial vehicles, have shown significant potential in complex autonomous missions. However, when deployed in urban canyons, dense high-rise buildings impose severe communication constraints on ground vehicles, necessitating the introduction of aerial vehicles as relays to maintain reliable connectivity. The resulting cooperative trajectory planning problem is challenging for three reasons. First, the kinematic and communication constraints are tightly coupled. Second, the optimization landscape is highly non-convex and non-differentiable. Third, the planner must balance topological exploration with real-time efficiency. To address these challenges, we propose a hierarchical cooperative trajectory planning framework for an air–ground robotic system. Specifically, in the upper layer, a heuristic-search-guided reinforcement learning mechanism is employed to narrow the search space and circumvent the sparse reward problem, rapidly generating an initial solution. Subsequently, the lower-layer planner utilizes an optimization-based solver, together with a corridor-based constraint formulation method, to refine the initial solution into a kinematically feasible cooperative trajectory. Ultimately, this strategy improves real-time efficiency while improving the quality of feasible cooperative trajectories. Extensive ablation studies and comparative experiments with representative baselines demonstrate that the proposed framework improves collision avoidance, communication reliability, trajectory smoothness, and computational efficiency in the tested urban canyon scenarios. Full article

Vision–Language–Action (VLA) models unify visual perception, natural-language understanding, and action generation within a single foundation model, allowing a robot to follow instructions such as “fold the towel” or “fly to the red building” directly from camera images. Because VLAs inherit world knowledge from internet-scale pre-training, they have become the dominant framework for learning-based manipulation, with bimanual coordination serving as the most demanding testbed: two arms with 7+ degrees of freedom each must move in concert to fold, assemble, and reorient objects. Unmanned aerial robotics faces a structurally similar challenge: a drone must coordinate thrust, attitude, and increasingly gripper commands from visual observations under strict latency and payload constraints. This review covers 183 contributions spanning 2017–2026 and organized along seven dimensions: VLA architectures, training recipes, action representations, bimanual coordination (2022–2026), unmanned aerial vehicle (UAV) navigation and control (2017–2026), language grounding, and cross-cutting concerns including memory and world models. We show that the coordination strategies, training recipes, and action representations developed for bimanual VLAs transfer to unmanned aerial systems and identify fourteen research directions across both domains. Full article

Unmanned aerial vehicles (UAVs) are increasingly expected to support both wireless communication and logistics missions, creating a need for integrated operation strategies that jointly manage data collection and physical item handling. This paper investigates a UAV system that simultaneously performs uplink communication with multiple ground nodes (GNs) while completing time-constrained item-pickup tasks. To enhance both throughput and fairness across GNs, we maximize the proportional fair spectral efficiency of GNs while ensuring that all items are collected within the required mission duration under payload and geographical constraints. The resulting formulation constitutes a mixed-integer nonconvex optimization problem involving binary pickup assignments, binary communication scheduling, and trajectory-dependent channel coupling, making direct global optimization intractable. To address this challenge, we develop an iterative convexification framework that integrates the successive convex approximation and the penalty convex–concave procedure within a block coordinate descent structure, enabling efficient joint optimization of trajectory, pickup timing/sequence, and GN scheduling. Simulation results validate that the proposed scheme dynamically shapes the UAV trajectory to improve channel conditions without violating the pickup deadline and compensates disadvantaged GNs through proportional fair scheduling. As a result, it consistently outperforms the baseline strategies under various system parameters. Full article

Existing self-supervised methods have achieved significant success in ground-level autonomous driving scenarios, but applying them directly to Unmanned Aerial Vehicle (UAV) videos remains challenging. On the one hand, rapid pose changes in UAVs often lead to oblique-view imaging, making it difficult for conventional methods to handle the perspective distortion in oblique imagery. On the other hand, complex UAV viewpoints may cause depth blurring in low-texture regions. To address these challenges, we propose a lightweight self-supervised monocular depth estimation method for UAV scenarios. By utilizing a Dynamic Direction-Aware Module (DDaM), the network adaptively adjusts the sampling grid to correct distorted features during feature extraction, while enhancing its ability to capture features at different spatial locations. Furthermore, to mitigate the loss of spatial information caused by multiple downsampling operations, we integrate a Coordinate Attention Mechanism into the encoder. This mechanism captures features along two separate spatial axes, preserving the spatial coordinates of object boundaries. Our experiments demonstrate that the synergy between DDaM and the Coordinate Attention Mechanism enables the prediction of more accurate object boundaries and richer local details. To validate the effectiveness and practical applicability of the proposed method, we conduct experiments on both the MidAir synthetic dataset and the UAVid real-world dataset. The results show that, compared with current baseline methods, our approach maintains competitive performance while requiring the fewest parameters. Full article

The rapid advancement of intelligent unmanned systems has brought new opportunities to mobile crowd sensing (MCS). Compared with traditional homogeneous frameworks, heterogeneous air-ground collaborative multi-agent frameworks consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) exhibit superior flexibility and efficiency in complex sensing tasks. Task allocation among agents is crucial for improving overall MCS quality. To achieve efficient task allocation for heterogeneous collaborative agents, this study investigated two typical complex multi-agent task allocation scenarios with dual optimization objectives: (1) For the Air-Ground Few-Agents-More-Tasks (AG-FAMT) scenario, the objectives are to maximize task completion and minimize total travel distance; (2) For the Air-Ground More-Agents-Few-Tasks (AG-MAFT) scenario (task allocation based on agent locations), the objectives are to minimize total travel distance and travel time cost. Overall, in this paper, we proposed two algorithms: a multi-task minimum cost maximum flow optimization algorithm called Multi-Task Minimum-Cost Maximum-Flow (MT-MCMF) tailored for AG-FAMT, and a multi-objective optimization algorithm called Weighted Integer Linear Programming (W-ILP) for AG-MAFT (with a focus on optimizing UAV charging path planning). Experiments on a large-scale real-world dataset demonstrated that both proposed algorithms outperform baseline methods under varying experimental settings (task quantity, difficulty, and distribution), providing a novel approach to enhance the overall quality of air-ground MCS tasks. Full article