Search Results (9,284)

In recent years, 3D digitalisation has experienced significant growth, revolutionising the way we capture, process and use geometric data. Initially conceived for industrial applications, these technologies have expanded to multiple fields, offering unprecedented accuracy and versatility. Depending on the accuracy and efficiency to be achieved in a specific field of application, and on the analytical capacity, a specific 3D digitalisation technique or another will be used. This review aims to delve into the application of 3D scanning techniques, according to the implementation sector. The optimal geometry capturing and processing 3D data techniques for a specific case are studied as well as their limitations. Full article

Mobile edge computing (MEC) deployed in unmanned aerial vehicles (UAVs) has shown special strength by enhancing computational capacity and prolonging the battery lives of terrestrial user equipment (UE). Nevertheless, current research lacks studies of robust offloading scheme scheduling and trajectory planning using terrestrial random channels. The state-of-the-art joint task-offloading and trajectory-planning optimization techniques for UAV-mounted MEC are focused on scenarios where only air–ground channels exist rather than time-varying terrestrial channels. By contrast, this paper considers the scenario where both the time-varying/random terrestrial channels and the line-of-sight air–ground channels occur. Aiming at robust resource scheduling for energy-efficient UAV-assisted MEC, we formulate a novel joint optimization of UAV trajectory planning and task offloading, which, however, is highly nonconvex. As a countermeasure, the original optimization is recast as subproblems related to task offloading and trajectory planning and solved by a novel robust iterative optimization algorithm that combines the methods of weighted minimum mean square error, S-procedure, successive convex approximation, etc. Numerical results indicate that, compared to various baselines, the proposed algorithm can effectively reduce energy consumption and optimize the trajectory in the presence of a large number of input tasks. In addition, in terms of stability and effectiveness, the proposed robust iterative optimization algorithm can reduce energy consumption more stably in time-varying/random channels compared to non-robust schemes. Full article

In the field of real-time autonomous decision-making for Unmanned Combat Aerial Vehicles (UCAVs), reinforcement learning is widely used to enhance their decision-making capabilities in high-dimensional spaces. These enhanced capabilities allow UCAVs to better respond to the maneuvers of various opponents, with the win rate often serving as the primary optimization metric. However, relying solely on the terminal outcome of victory or defeat as the optimization target, but without incorporating additional rewards throughout the process, poses significant challenges for reinforcement learning due to the sparse reward structure inherent in these scenarios. While algorithms enhanced with densely distributed artificial rewards show potential, they risk deviating from the primary objectives. To address these challenges, we introduce a novel approach: the homotopy-based soft actor–critic (HSAC) method. This technique gradually transitions from auxiliary tasks enriched with artificial rewards to the main task characterized by sparse rewards through homotopic paths. We demonstrate the consistent convergence of the HSAC method and its effectiveness through deployment in two distinct scenarios within a 3D air combat game simulation: attacking horizontally flying UCAVs and a combat scenario involving two UCAVs. Our experimental results reveal that HSAC significantly outperforms traditional algorithms, which rely solely on using sparse rewards or those supplemented with artificially aided rewards. Full article

Unmanned Aerial Vehicle (UAV) development has garnered significant attention, yet one of the major challenges in the field is how to rapidly iterate the overall design scheme of UAVs to meet actual needs, thereby shortening development cycles and reducing costs. This study integrates a “Decision Support System” and “Live Virtual Construct (LVC) environment” into the existing Model-Based Systems Engineering framework, proposing a Modified Model-Based Systems Engineering methodology for the full-process development of UAVs. By constructing a decision support system and a hybrid reality space—which includes pure digital modeling and simulation analysis software, semi-physical simulation platforms, real flight environments, and virtual UAVs—we demonstrate this method through the development of the electric vertical take-off and landing fixed-wing UAV DB1. This method allows for rapid, on-demand iteration in a fully digital environment, with feasibility validated by comparing actual flight test results with mission indicators. The study results show that this approach significantly accelerates UAV development while reducing costs, achieving rapid development from “demand side to design side” under the “0 loss” background. The DB1 platform can carry a 2.5 kg payload, achieve over 40 min of flight time, and cover a range of more than 70 km. This work provides valuable references for UAV enterprises aiming to reduce costs and increase efficiency in the rapid commercialization of UAV applications. Full article

Accurately understanding the stand composition of shelter forests is essential for the construction and benefit evaluation of shelter forest projects. This study explores classification methods for dominant tree species in shelter forests using UAV-derived RGB, hyperspectral, and LiDAR data. It also investigates the impact of individual tree crown (ITC) delineation accuracy, crown morphological parameters, and various data sources and classifiers. First, as a result of the overlap and complex structure of tree crowns in shelterbelt forests, existing ITC delineation methods often lead to over-segmentation or segmentation errors. To address this challenge, we propose a watershed and multi-feature-controlled spectral clustering (WMF-SCS) algorithm for ITC delineation based on UAV RGB and LiDAR data, which offers clearer and more reliable classification objects, features, and training data for tree species classification. Second, spectral, texture, structural, and crown morphological parameters were extracted using UAV hyperspectral and LiDAR data combined with ITC delineation results. Twenty-one classification images were constructed using RF, SVM, MLP, and SAMME for tree species classification. The results show that (1) the proposed WMF-SCS algorithm demonstrates significant performance in ITC delineation in complex mixed forest scenarios (Precision = 0.88, Recall = 0.87, F1-Score = 0.87), resulting in a 1.85% increase in overall classification accuracy; (2) the inclusion of crown morphological parameters derived from LiDAR data improves the overall accuracy of the random forest classifier by 5.82%; (3) compared to using LiDAR or hyperspectral data alone, the classification accuracy using multi-source data improves by an average of 7.94% and 7.52%, respectively; (4) the random forest classifier combined with multi-source data achieves the highest classification accuracy and consistency (OA = 90.70%, Kappa = 0.8747). Full article

This research focuses on the dynamic response analysis of a quadcopter arm without an adapter mounted and with aerodynamic profile adapters mounted to enhance drone performance. Nine different adapters were simulated to assess their impact on the arm’s dynamic behavior during various motor operating regimes. The pressure force distribution from the airflow around the quadcopter arm was analyzed to determine the optimal adapter configuration. Numerical simulations revealed the best geometry for the adapter, which significantly reduced maximum displacement amplitudes compared to the non-adapter arm. The study also examined the effects of static imbalance from the rotor-propeller assembly, leading to the calculation of an eccentricity value of 0.022 mm for inertial force application. Experimental tests validated the numerical findings, with laser vibrometer measurements confirming improved dynamic responses with Adapter 8 across most operating regimes. Overall, the study shows the advantages of using better aerodynamic designs in quadcopter arms to improve stability and performance, contributing to advancements in drone technology through improved structural designs. Full article

During the process of urbanization, a large number of impervious land surfaces are replacing the biologically active surface. Land surface temperature is a key factor reflecting the urban thermal environment and a crucial factor affecting city livability and resident comfort. Therefore, the accurate measurement of land surface temperature is of great significance. Thermal infrared remote sensing is widely applied to study the urban thermal environment due to its distinctive advantages of high sensitivity, wide coverage, high resolution, and continuous measurement. Low-altitude remote sensing, performed using thermal infrared sensors carried by unmanned aerial vehicles (UAVs), is a common method of land surface observation. However, thermal infrared sensors may experience varying degrees of sway due to wind, affecting the quality of the data. It is still uncertain as to what degree angle changes affect thermal infrared data in urban environments. To investigate this effect, a near-ground remote sensing experiment was conducted to observe three common urban land surfaces, namely, marble tiles, cement tiles and grasses, at observation angles of 15°, 30°, 45°, and 60° using a thermal infrared imager. This is accompanied by synchronous ground temperature measurements conducted by iButton digital thermometers. Our results suggest that the temperature differences between the remote sensing data of the land surface and the corresponding ground truth data increase as a function of the increasing observation angle of the three land surfaces. Furthermore, the differences are minor when the observation angle changes are not more than 15° and the changes are not the same for different land surfaces. Our findings increase the current understanding of the effects of different angles on thermal infrared remote sensing in urban land surface temperature monitoring. Full article

Reducing pesticide use is a crucial step toward achieving the green and low-carbon transformation of agriculture. Analyzing the role and mechanisms of agricultural digital technologies—particularly plant protection unmanned aerial vehicles (UAVs) for aerial spraying—is essential for identifying viable strategies to reduce pesticide application intensity among farming households. This analysis is critical for facilitating the low-carbon transformation of rice production and advancing sustainable agricultural development. This study, using survey data from 455 farming households in Jiangxi Province, China, employs Ordinary Least Squares (OLS) and Propensity Score Matching (PSM) methods to investigate the relationship between plant protection UAVs and pesticide application intensity. The findings reveal that adopting plant protection UAVs significantly reduces pesticide application intensity in rice production by 24.9%. Further analysis indicates that the reduction effect is more pronounced among non-aged, large-scale, and part-time farming households. To achieve the low-carbon transformation of rice production, it is vital to enhance agricultural support policies and develop effective market promotion and application mechanisms to encourage the adoption of UAV-based aerial spraying and other digital agricultural technologies. Full article

A nondestructive approach for accurate crop yield prediction at the field scale is vital for precision agriculture. Considerable progress has been made in the use of the spectral index (SI) derived from unmanned aerial vehicle (UAV) hyperspectral images to predict crop yields before harvest. However, few studies have explored the most sensitive wavelengths and SIs for crop yield prediction, especially for different nitrogen fertilization levels and soil types. This study aimed to investigate the appropriate wavelengths and their combinations to explore the ability of new SIs derived from UAV hyperspectral images to predict yields during the growing season of spring maize. In this study, the hyperspectral canopy reflectance measurement method, a field-based high-throughput method, was evaluated in three field experiments (Wang-Jia-Qiao (WJQ), San-Ke-Shu (SKS), and Fu-Jia-Jie (FJJ)) since 2009 with different soil types (alluvial soil, black soil, and aeolian sandy soil) and various nitrogen (N) fertilization levels (0, 168, 240, 270, and 312 kg/ha) in Lishu County, Northeast China. The measurements of canopy spectral reflectance and maize yield were conducted at critical growth stages of spring maize, including the jointing, silking, and maturity stages, in 2019 and 2020. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained from the contour maps constructed by the coefficient of determination (R²) from the linear regression models between the yield and all possible SIs screened from the 450 to 950 nm wavelengths. The new SIs and eight selected published SIs were subsequently used to predict maize yield via linear regression models. The results showed that (1) the most sensitive wavelengths were 640–714 nm at WJQ, 450–650 nm and 750–950 nm at SKS, and 450–700 nm and 750–950 nm at FJJ; (2) the new SIs established here were different across the three experimental fields, and their performance in maize yield prediction was generally better than that of the published SIs; and (3) the new SIs presented different responses to various N fertilization levels. This study demonstrates the potential of exploring new spectral characteristics from remote sensing technology for predicting the field-scale crop yield in spring maize cropping systems before harvest. Full article

Three-dimensional (3D) path planning is a crucial technology for ensuring the efficient and safe flight of UAVs in complex environments. Traditional path planning algorithms often find it challenging to navigate complex obstacle environments, making it challenging to quickly identify the optimal path. To address these challenges, this paper introduces a Nutcracker Optimizer integrated with Hyperbolic Sine–Cosine (ISCHNOA). First, the exploitation process of the sinh cosh optimizer is incorporated into the foraging strategy to enhance the efficiency of nutcracker in locating high-quality food sources within the search area. Secondly, a nonlinear function is designed to improve the algorithm’s convergence speed. Finally, a sinh cosh optimizer that incorporates historical positions and dynamic factors is introduced to enhance the influence of the optimal position on the search process, thereby improving the accuracy of the nutcracker in retrieving stored food. In this paper, the performance of the ISCHNOA algorithm is tested using 14 classical benchmark test functions as well as the CEC2014 and CEC2020 suites and applied to UAV path planning models. The experimental results demonstrate that the ISCHNOA algorithm outperforms the other algorithms across the three test suites, with the total cost of the planned UAV paths being lower. Full article

The combination of UAV-LiDAR and LiDAR-SLAM (Simultaneous Localization and Mapping) technology can overcome the scanning limitations of different platforms and obtain comprehensive 3D structural information of forest stands. To address the challenges of the traditional registration algorithms, such as high initial value requirements and susceptibility to local optima, in this paper, we propose a high-precision, robust, NDT-VGICP registration method that integrates voxel features to register UAV-LiDAR and LiDAR-SLAM point clouds at the forest stand scale. First, the point clouds are voxelized, and their normal vectors and normal distribution models are computed, then the initial transformation matrix is quickly estimated based on the point pair distribution characteristics to achieve preliminary alignment. Second, high-dimensional feature weighting is introduced, and the iterative closest point (ICP) algorithm is used to optimize the distance between the matching point pairs, adjusting the transformation matrix to reduce the registration errors iteratively. Finally, the algorithm converges when the iterative conditions are met, yielding an optimal transformation matrix and achieving precise point cloud registration. The results show that the algorithm performs well in Chinese fir forest stands of different age groups (average RMSE—horizontal: 4.27 cm; vertical: 3.86 cm) and achieves high accuracy in single-tree crown vertex detection and tree height estimation (average F-score: 0.90; R² for tree height estimation: 0.88). This study demonstrates that the NDT-VGICP algorithm can effectively fuse and collaboratively apply multi-platform LiDAR data, providing a methodological reference for accurately quantifying individual tree parameters and efficiently monitoring 3D forest stand structures. Full article

In the autonomous navigation of drones, improving positioning accuracy is of significant importance to obtain highly accurate information on flight velocity. Traditional microwave and acoustic velocity measurement methods have the disadvantages of poor precision and susceptibility to interference. In this study, an unmanned aerial vehicle (UAV)-mounted two-dimensional laser Doppler velocimeter was developed and investigated, and a relevant drone flight navigation and positioning experiment was carried out. The UAV-mounted two-dimensional laser Doppler velocimeter (LDV) prototype developed in this study applies a scheme of dual-beam measurement light, sharing a focusing lens group. After process integration, the performance of the prototype was measured. It shows that a velocity measurement effect with a high signal-to-noise ratio can be achieved by using two measurement probe beams within a working distance range of 40 m–60 m. In the flight experiment, the flight trajectory calculated using the LDV-measured velocity data was compared with the global navigation satellite system (GNSS)-recorded trajectory. The result shows that LDV can achieve an odometer accuracy of 4.8‰. This study has validated the feasibility of the laser Doppler velocimeter in drone navigation and positioning, providing a novel method for reliable and high-precision velocity measurement in autonomous drone navigation. Full article

Timely and accurate yield estimation is essential for effective crop management and the grain trade. Remote sensing has emerged as a valuable tool for monitoring rice yields; however, many studies concentrate on a single period or simply aggregate multiple periods, neglecting the complexities underlying yield formation. The study enhances yield estimation by integrating cumulative time series vegetation indices (VIs) from multispectral (MS) and RGB (Red, Green, Blue) sensors to identify optimal combinations of growth periods. We utilized two unmanned aerial vehicle to capture spectral information from rice canopies through MS and RGB sensors. By analyzing the correlations between vegetation indices from different sensors and rice yields, the optimal MS-VIs and RGB-VIs for each period were identified. Following this, the relationship between the cumulative time series of MS-VIs, RGB-VIs, and rice yields was further examined. The results demonstrate that the booting stage is a crucial growth period influencing rice yield, with VIs exhibiting increased correlation with yield, peaking during this stage before declining. For the MS sensor, the rice yield model, based on the cumulative time series of MS-VIs from the tillering stage to the panicle initiation stage, achieves optimal accuracy (R² = 0.722, RRMSE = 0.555). For the RGB sensor, the rice yield model, based on the cumulative time series of RGB-VIs from the tillering stage to the grain-filling stage, yields the highest accuracy (R² = 0.727, RRMSE = 0.526). In comparison, the multi-sensor rice yield model, which combines the cumulative time series of MS-VIs from the tillering stage and RGB-VIs from the panicle initiation to grain-filling stages, achieves the highest accuracy with R² = 0.759 and RRMSE = 0.513. These findings suggest that cumulative time series VIs and the integration of multiple sensors enhance yield prediction accuracy, providing a comprehensive approach for estimating rice yield dynamics and supporting precision agriculture and informed crop management. Full article

As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on the State Transition function (STGBO) to address the spherical path planning problem for UAV clusters. The state transition function is applied on the scale of medium and high-dimensional cities, balancing the stability and efficiency of the algorithm. Through evolution and comparisons with many mainstream meta-heuristic algorithms, STGBO has demonstrated superior performance and effectiveness in solving Medium-Altitude Unmanned Aerial Vehicle (MUAV) path planning problems on three-dimensional spherical surfaces, contributing to the development of the low-altitude economy. Full article

Networked cooperation of multi-unmanned-aerial-vehicle (UAV) is significant, but their cooperative guidance presents challenges due to weak cross-domain communication. Given the difficulties in information transmission security, this paper proposes a prescribed-time cooperative guidance law (PTCGL) for networked multiple UAVs with intermittent communication. Supported by the directed internal communication, the proposed PTCGL can ensure the simultaneous arrival in the case of the pinning external communication is time-triggered intermittent. In the first stage, PTCGL is designed to combine with a time-scaling function and second-order intermittent pinning consensus. With the desired convergence performance, the convergence time can be pre-specified independent of initial conditions and parameter tuning in theory. In the second stage, begin with the suitable initial conditions at the prescribed convergence time, the multiple UAVs are organized by proportional navigation guidance, so as to ensure the cooperation regardless of weak communication. Finally, simulations are conducted to verify the effectiveness and robustness of the proposed cooperative guidance law. Full article