Search Results (2,568)

Monitoring and management of construction sites are critical to ensuring project success, efficiency, and safety. Traditional methods often struggle to provide real-time, accurate, and comprehensive data, leading to delays, cost overruns, and errors. This paper presents a novel methodology utilising a ground drone for auditing construction sites to detect changes and deviations from planned Building Information Modelling (BIM). The methodology focuses on developing a novel tool that facilitates Scan-vs-BIM auditing through time. Experimental results are presented, demonstrating the effectiveness and accuracy of the proposed methodology for assessing structural discrepancies. This research contributes to advancing construction auditing practices by integrating state-of-the-art technologies and innovative techniques, ultimately enhancing project monitoring and management processes in the construction industry. Full article

To address the formation control issue of multiple unmanned aerial vehicles (UAVs), a finite-time control scheme based on terminal sliding mode (TSM) is investigated in this paper. A quadcopter UAV with the vertical takeoff property is considered, with cascaded kinematics composed of rotational and translational loops. To strengthen the application in the low-cost UAV system, the applied torque is synthesized with an auxiliary rotational system, which can avoid utilizing direct attitude measurement. Furthermore, a terminal sliding mode surface is established and employed in the finite-time formation control protocol (FTFCP) as the driven thrust of multiple UAVs over an undirected topology in the translational system. To maintain the safe flight of the UAV clusters in an environment to avoid collision with obstacles or with other UAV neighbors, a pigeon-hierarchy-inspired obstacle avoidance protocol (PHOAP) is proposed. By imitating the interactive hierarchy that exists among the homing pigeon flocks, the collision avoidance scheme is separately enhanced to generate the repulsive potential field for the leader maneuver target and the follower UAV cluster. Subsequently, the collision avoidance laws based on pigeon homing behavior are combined with the finite-time sliding mode formation protocol, and the applied torque is attached as a cascaded structure in the attitude loop to synthesize an obstacle avoidance cooperative control framework. Finally, simulation scenarios of multiple UAVs to reach a desired formation among obstacles is investigated, and the effectiveness of the proposed approach is validated. Full article

Research on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stage learning efficiency. To address these issues, this paper integrates DRL with the Transformer architecture to propose the GTrXL-SAC (gated Transformer-XL soft actor critic) algorithm. The algorithm performs positional embedding on multimodal data combining visual and sensor information. Leveraging the self-attention mechanism of GTrXL, it effectively focuses on different segments of multimodal data for encoding while capturing sequential relationships, significantly improving obstacle recognition accuracy and enhancing both learning efficiency and sample efficiency. Additionally, the algorithm capitalizes on GTrXL’s memory characteristics to generate current drone control decisions through the combined analysis of historical experiences and present states, effectively mitigating long-term dependency issues. Experimental results in the AirSim drone simulation environment demonstrate that compared to PPO and SAC algorithms, GTrXL-SAC achieves more precise policy exploration and optimization, enabling superior control of drone velocity and attitude for stabilized flight while accelerating convergence speed by nearly 20%. Full article

Unmanned Aerial Vehicles (UAVs) hold great promise for Mobile Edge Computing (MEC) owing to their flexible mobility, rapid deployment, and low-cost characteristics. However, UAV-enabled MEC still faces challenges in terms of the real-time arrival of computational tasks, energy reservation, and the actual response efficiency of the system. In this study, we focus on a UAV-enabled MEC scenario, where multiple UAVs function as airborne edge servers, offering computation services to multiple ground-based user devices (UDs). We aim to minimize the cost of the MEC system by optimizing the computation offloading policy. Specifically, we take task latency into account to ensure the timeliness of real-time tasks. The Lyapunov optimization method is employed to maintain a uniform and stable queue for energy consumption. Additionally, we draw on the concept of maximum completion time in shop-floor scheduling to optimize the actual response latency. To this end, we propose a joint optimization algorithm. First, the joint optimization problem is transformed into a per-time-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a reinforcement learning method, LyraRD, is proposed to solve the PROP. Experimental results verify that the proposed approach outperforms the benchmarks in terms of system performance. Full article

Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods oversimplify obstacle geometries, and both fail to ensure safety and satisfy turning radius constraints for under-actuated AUVs in intricate environments. This paper proposes a robust Model Predictive Control (MPC) framework integrating an enhanced fast marching control barrier function, specifically designed for AUVs equipped with fully directional sonar systems. The framework introduces a novel improvement for moving obstacles by extending the control barrier function field propagation along the obstacle’s movement direction. This enhancement generates precise motion plans that ensure safety, satisfy kinematic constraints, and effectively handle static and dynamic obstacles. Simulation results demonstrate superior obstacle avoidance and motion planning performance in complex scenarios, with key outcomes including a minimum safety margin of 1.86 m in cluttered environments (vs. 0 m for A* and FMM) and 1.76 m in dynamic obstacle scenarios (vs. 0.13 m for MPC-APFCBF), highlighting the framework’s ability to enhance navigation safety and efficiency for real-world AUV deployments in unpredictable marine environments. Full article

Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence. Full article

When multiple unmanned aerial vehicles (UAVs) form a cluster, their flight process is divided into two phases. The first phase is the cruising stage, during which UAVs move from random positions toward the target, gradually forming a spherical topology. In the initial cruising phase, to address the oscillation phenomenon in traditional sliding mode control, we propose a new reaching law to overcome the typical residual oscillations present in conventional reaching laws, called the Control Law for Residual Chattering Oscillation Elimination (CL-RCO). Based on this proposed new law, we have designed an artificial potential field-based sliding mode formation controller for UAVs to manage the formation control of UAVs. The second stage is the clustering phase, which focuses on overcoming oscillations to establish a stable topology. In this phase, we design a controller that combines artificial potential fields with variable repulsion coefficients and backstepping control. This method addresses the persistent residual oscillations in formations maintained solely by artificial potential fields during the clustering phase. Lyapunov stability analysis is employed to confirm the feasibility of the designed controller. Eventually, numerical simulations and comparative analyses are performed, successfully demonstrating the proposed method’s effectiveness. Full article

Accurately monitoring total nitrogen (TN) content in field soils is crucial for precise fertilization management. TN content is one of the core indicators in soil fertility evaluation systems. Rapid and accurate determination of TN in the tillage layer is essential for agricultural production. Although UAV-based multispectral remote sensing technology has shown potential in agricultural monitoring, research on its quantitative assessment of soil TN content remains limited. This study utilized UAV (unmanned aerial vehicle) multispectral imagery and field-measured TN data from four key growth stages of silage corn in 2022 at Huari Ranch, Minle County, Hexi region. The support vector machine–recursive feature elimination (SVM-RFE) algorithm was applied to select vegetation indices as model inputs. A total of 18 models based on machine learning algorithms, including BP neural networks (BPNNs), random forest (RF), and partial least squares regression (PLSR), were constructed to compare the most suitable inversion model for TN in the rhizosphere soil (0–30 cm) of silage corn at different growth stages. The optimal period for TN inversion was determined. The SVM-RFE algorithm outperformed the models built without feature selection in terms of accuracy. Among the nitrogen inversion models based on different machine learning algorithms, the PLSR model showed the best performance, followed by the RF model, while the BPNN model performed the worst. The PLSR model established for the mature growth stage at soil depths demonstrated the highest inversion accuracy, with R and RMSE values of 0.663 and 0.281, respectively. The next best period was the tasseling stage, while the worst inversion accuracy was observed during the seedling stage, indicating that the mature stage is the optimal period for TN inversion in the study area. Full article

High payload capacity multi-rotor aerial vehicles are typically configured with multiple propellers to achieve the required aerodynamic lift. However, this design approach often results in an increased overall dimensional envelope, which introduces significant operational limitations in confined spatial environments such as urban airspace. By utilizing a limited overlap rotor configuration, the spatial utilization rate of an aircraft can be greatly improved, ensuring a sufficient thrust of rotor while simultaneously reducing the size of the aircraft. However, the slipstreams of two rotors overlap, which may create a significant aerodynamic interface. This paper utilizes numerical simulation based on the unsteady RANS (Reynolds-averaged Navier–Stokes) method to analyze the influence of parameters such as distance, blade distance, and rotation direction on the interference flow field of overlapping rotors. Research indicates that aerodynamic interference only affects the overlapping area between two rotors at the inner blade, leading to the offset of loading distribution on the blade, which can be explained by the slipstream effect, suction effect, and induced effects generated by two rotors. As the axis distance between two rotors decreases, the strengthening of the slipstream and suction effects leads to a rapid decrease in the aerodynamic efficiency of the two rotors. When the blade between the two rotors increases, the weakening of the suction effect and induced effects causes the load on the lower rotor to translate to the upper rotor. Moreover, the variation in the spatial distribution of the blade tip–vortex leads to blade–vortex interaction, which causes a change in the spanwise distribution of the load on the lower blade. Full article

This study focuses on the distributed fixed-time fault-tolerant control problem for a network of six-degree-of-freedom (DOF) fixed-wing unmanned aerial vehicles (UAVs), which are subject to full-state constraints and actuator faults. The novelty of the proposed design lies in the incorporation of an enhanced asymmetric time-varying tan-type barrier Lyapunov function (BLF), which is applicable in both constrained and unconstrained scenarios. This function ensures that the UAV states remain within compact sets at all times while achieving fixed-time convergence. Additionally, a fixed-time performance function (FTPF) is developed to eliminate the dependency on exponential functions commonly used in traditional fixed-time control methods. The adverse effects of actuator faults, including lock-in-place and loss of effectiveness, are mitigated through a bounded uniform tracking control design. A rigorous Lyapunov function analysis demonstrates that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with both velocity and attitude tracking errors converging to residual sets near the origin. Experimental validation tests are conducted to confirm the effectiveness of the theoretical findings. Full article

The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not adequately consider the ground risks and secondary conflicts arising from high-density operations in urban VLL airspace. To address these challenges, this paper proposes a two-stage hierarchical 4D trajectory planning method to minimize multiple risks. Specifically, the method consists of a risk-aware global planning module (RAGPM) for preflight trajectory planning and a non-secondary conflict local planning module (NCLPM) for in-flight conflict avoidance. Consequently, low-risk trajectory without secondary conflict can be found in complex environments with high-density operations, as illustrated by extensive experiments. Full article

The characterization of dimensional stone deposits is essential for quarry assessment and design. However, uncertainties in mapping and designing pose significant challenges. To address this issue, an innovative approach is initiated to develop a virtual reality model by integrating unmanned aerial vehicle (UAV) photogrammetry for surface modeling and Electric Resistivity Tomography (ERT) for subsurface deposit imaging. This strategy offers a cost-effective, time-efficient, and safer alternative to traditional surveying methods for challenging mountainous terrain. UAV methodology involved data collection using a DJI Mavic 2 Pro (20 MP camera) with 4 K resolution images captured at 221 m altitude and 80 min flight duration. Images were taken with 75% frontal and 70% side overlaps. The Structure from Motion (SfM) processing chain generated high-resolution outputs, including point clouds, Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and orthophotos. To ensure accuracy, five ground control points (GCPs) were established by a Real-Time Kinematic Global Navigation Satellite System (RTK GNSS). An ERT method known as vertical electric sounding (VES) revealed subsurface anomalies like solid rock mass, fractured zones and areas of iron leaching within marble deposits. Three Schlumberger (VES-1, 2, 3) and two parallel Wenner (VES-4, 5) arrays to a depth of 60 m were employed. The resistivity signature acquired by PASI RM1 was analyzed using 1D inversion technique software (ZondP1D). The integrated outputs of photogrammetry and subsurface imaging were used to design an optimized quarry with bench heights of 30 feet and widths of 50 feet, utilizing open-source 3D software (Blender, BIM, and InfraWorks). This integrated approach provides a comprehensive understanding of deposit surface and subsurface characteristics, facilitating optimized and sustainable quarry design and extraction. This research demonstrates the value of an innovative approach in synergistic integration of UAV photogrammetry and ERT, which are often used separately, for enhanced characterization, decision-making and promoting sustainable practices in dimensional stone deposits. Full article

Unmanned aerial vehicles (UAVs) make power line inspections more safe, efficient, and cost-effective, replacing risky manual checks and expensive helicopter surveys while overcoming challenges like stability and regulations. The aim of this study is to conduct a systematic review of the application of UAVs for power line inspections. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is implemented to ensure a structured and comprehensive review process. The Scopus database is used to identify relevant publications, and after screening and applying eligibility criteria, 75 documents were selected for further analysis. The study results show a shift toward predictive maintenance, multi-UAV operations, and real-time data analysis. However, challenges remain, including UAV–grid connectivity, resilience to extreme weather, and large-scale automation. This work provides key insights into technological and algorithmic advancements and research trends on UAV-based power line inspections while pointing out gaps in the existing literature. Finally, future research directions to advance UAV-based power line inspections are suggested. Full article

Recently, autonomous flight has emerged as a key technology in the aerospace and defense sectors; however, traditional code-based autonomous flight systems face limitations in complex environments. Although reinforcement learning offers an alternative, its practical application in real-world settings is hindered by the substantial data requirements. In this study, we develop a framework that integrates a Generative Adversarial Network (GAN) and Hindsight Experience Replay (HER) into model-based reinforcement learning to enhance data efficiency and accuracy. We compared the proposed framework against existing algorithms in actual quadcopter control. In the comparative experiment, we demonstrated an improvement of up to 70.59% in learning speed, clearly highlighting the impact of the environmental model. To the best of our knowledge, this study is the first where a GAN and HER are combined with model-based reinforcement learning, and it is expected to contribute significantly to the practical application of reinforcement learning in autonomous flight. Full article

Multi-UAV path planning algorithms are crucial for the successful design and operation of unmanned aerial vehicle (UAV) networks. While many network researchers have proposed UAV path planning algorithms to improve system performance, an in-depth review of multi-UAV path planning has not been fully explored yet. The purpose of this study is to survey, classify, and compare the existing multi-UAV path planning algorithms proposed in the literature over the last eight years in various scenarios. After detailing classification, we compare various multi-UAV path planning algorithms based on time consumption, computational cost, complexity, convergence speed, and adaptability. We also examine multi-UAV path planning approaches, including metaheuristic, classical, heuristic, machine learning, and hybrid methods. Finally, we identify several open research problems for further investigation. More research is required to design smart path planning algorithms that can re-plan pathways on the fly in real complex scenarios. Therefore, this study aims to provide insight into the multi-UAV path planning algorithms for network researchers and engineers to contribute further to the design of next-generation UAV systems. Full article