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Peanut is a significant oilseed crop that is often affected by peanut southern blight, a disease that greatly reduces crop yield and quality. Therefore, accurate and timely monitoring of this disease is crucial to ensure crop safety and minimize the need for pesticides. Spectral features combined with texture features have been widely applied in plant disease monitoring. However, previous studies have mostly used original texture features, and its combination form has been rarely considered. This study presents a novel approach for monitoring peanut southern blight, integrating multiple spectral indices and textural indices (TIs). Firstly, a total of 20 vegetation indices (VIs) were extracted from the unmanned aerial vehicle multispectral images, while three TIs were constructed based on original textural features. Subsequently, Otsu-CIgreen algorithm was used to find the optimal threshold to eliminate the complex background of the image. Lastly, monitoring models for peanut southern blight were constructed using three machine learning models based on the screened VIs, VIs combined with TIs. Among these models, the K-nearest neighbor model using VIs combined with TIs demonstrates the best performance, with accuracy and F1 score on the test set reaching 91.89% and 91.39% respectively. The results indicate that the monitoring models utilizing VIs and TIs were more effective compared to models using only VIs. This approach provides valuable insights for non-destructive and accurate monitoring of peanut southern blight. [ABSTRACT FROM AUTHOR]

By analyzing the characteristics of collaborative decision-making behavior of heterogeneous clusters, this paper introduces quantum decision-making model to solve the autonomous decision-making problem of heterogeneous clusters. First, it is triggered from the OODA loop process to clarify the control mode of heterogeneous cluster collaboration. Then, aily ming at the specific problem of heterogeneous cluster cooperative cruise, the tasks of unmanned boat cluster and UAV cluster are split, and the application models of formation optimization and autonomous detection are constructed respectively. The formation optimization algorithm is designed to dynamically generate the best communication formation structure. The quantum decision cloud and feedback scoring mechanism are designed to select the most suitable decision content among many possible detection options. Finally, the effectiveness of formation optimization model and autonomous detection model in heterogeneous cluster cooperative cruise is demonstrated by simulation experiments. Experiments show that the quantum decision model has greater applicability and mobility in decision behavior selection and optimization than the conventional decision model. [ABSTRACT FROM AUTHOR]

UAV vehicle detection based on convolutional neural network exits a key problem of information imbalance of different feature layers. Shallow features have spatial information that is beneficial to localization, but lack semantic information. On the contrary, deep features have semantic information that is beneficial to classification, but lack spatial information. However, accurate classification and localization for UAV vehicle detection require both shallow spatial information and high semantic information. In our work, a bi-directional information guidance network (BDIG-Net) for UAV vehicle detection is proposed, which can ensure that each feature prediction layer has abundant mid-/low-level spatial information and high-level semantic information. There are two main parts in the BDIG-Net: shallow-level spatial information guidance part and deep-level semantic information guidance part. In the shallow-level guidance part, we design a feature transform module (FTM) to supply the mid-/low-level feature information, which can guide the BDIG-Net to enhance detailed and spatial features for deep features. Furthermore, we adopt a light-weight attention module (LAM) to reduce unnecessary shallow background information, making the network more focused on small-sized vehicles. In the deep-level guidance part, we use classical feature pyramid network to supply high-level semantic information, which can guide the BDIG-Net to enhance contextual information for shallow features. Meanwhile, we design a feature enhancement module (FEM) to suppress redundant features and improve the discriminability of vehicles. The proposed BDIG-Net can reduce the information imbalance. The experimental results show that the BDIG-Net can achieve accurate classification and localization for UAV vehicles and realize the real-time application requirements. [ABSTRACT FROM AUTHOR]

In today's world, the rapid development of aviation technologies, particularly unmanned aerial vehicles (UAVs), presents new challenges and opportunities. UAVs are utilized across various industries, including scientific research, military, robotics, surveying, logistics, and postal delivery. However, to ensure efficient and safe operation, UAVs require a reliable autopilot system that delivers precise navigation control and flight stability. This paper introduces a method for controlling and adjusting UAV trajectories, which enhances accuracy in environments and tasks corresponding to the first or second level of autonomy. It outperforms the linear-quadratic method and the unmodified predictive control method by 43% and 74%, respectively. The findings of this study can be applied to the development and modernization of new UAV, as well as the advancement of new UAV motion control systems, thereby enhancing their quality and efficiency. [ABSTRACT FROM AUTHOR]

A multispectral imaging system often cannot capture 3D spatial information owing to hardware limitations, which diminishes the effectiveness across various domains. To address this problem, we have developed a multispectral stereo imaging system along with an adaptive 3D reconstruction algorithm. Unlike existing unmanned aerial vehicle stereo imaging systems, our multispectral stereo imaging system uses two multispectral cameras with asymmetric spectral bands positioned at different angles. This design enables the acquisition of a higher number of bands and lateral spatial information while maintaining a lightweight structure. This system introduces challenges such as large geometric distortions and intensity differences between multiple bands. To accurately recover 3D spatial information, we propose an adaptive 3D reconstruction method. This method employs a position and orientation system-assisted projection transformation and a normalized threshold adjustment strategy. Finally, mutual information is used to reconstruct the multispectral images densely, effectively addressing nonlinear differences and generating a comprehensive multispectral point cloud. Our stereo system was used for two real data collections in different regions, and the efficacy of the proposed 3D reconstruction method was validated by comparing it with existing methods and commercial software. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). This solution is based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be applied as the first line of the response to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or in the open sea during transport activities in a fast, efficient, and low-cost way. The paper describes the work done in the development of a team of autonomous vehicles able to carry as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), and the development of a multi-robot framework for efficient oil spill mitigation. Field tests have been performed in Portugal and Spain's harbors, with a simulated oil spill, and the coordinate oil spill task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV) STORK has been validated. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive incremental nonlinear dynamic inversion (INDI) controller for unmanned aerial manipulators (UAMs). A novel adaptive law is employed to enable aerial manipulators to manage the inertia parameter changes that occur when the manipulator moves or picks up unknown objects during any phase of the UAM's flight maneuver. The adaptive law utilizes a Kalman filter to estimate a set of weighting factors employed to adjust the control gain matrix of a previously developed INDI control law formulated for the corresponding UAV (no manipulator included). The proposed adaptive control scheme uses acceleration and actuator input measurements of the UAV without necessitating any knowledge about the manipulator, its movements, or the objects being grasped, thus enabling the use of previously developed INDI UAV controllers for UAMs. The algorithm is validated through simulations demonstrating that the adaptive control gain matrix used in the UAV's INDI controller is promptly updated based on the UAM maneuvers, resulting in effective UAV and robot arm control. [ABSTRACT FROM AUTHOR]

As unmanned aerial vehicles (UAVs) have limited energy resources and diverse constraints, the reconfiguration of their formation encounters substantial challenges. In this paper, we employ a leader-follower method. In order to minimize flight distance and resource consumption, a greedy algorithm is used to allocate leader and follower positions. Based on the limitations of the receding horizon control (RHC) method and the control parameterization and time discretization (CPTD) method, we propose the no reference path RHC (NRPRHC) method. The proposed method transforms the formation reconfiguration into smaller local optimization problems, leading to a reduction in the size of the optimization stages and computational complexity. For each local optimization problem, we propose the adaptive population differential evolution (APDE) algorithm to optimize the control inputs. Finally, the results are provided to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the integral event-triggered time-varying formation tracking (TVFT) problem is investigated for unmanned aerial vehicle (UAV) swarm systems with switching directed topologies. An improved integral event-triggered mechanism (ETM) is introduced into the TVFT control protocol, which can further reduce communication frequency and resource consumption compared with traditional static ETM. The switching topology is considered to improve the reliability of the communication network. Different from traditional TVFT, the addition of the switching signal and the ETM brings challenges to the system analysis and design. To this end, the control protocol of TVFT, the admissible edge-dependent average dwell time switching signal and the ETM are co-designed, the global exponential stability criterion is further deduced. The simulation verifies the effectiveness and merits of the design scheme. [ABSTRACT FROM AUTHOR]

The application field for Unmanned Aerial Vehicle (UAV) technology and its adoption rate have been increasing steadily in the past years. Decreasing cost of commercial drones has enabled their use at a scale broader than ever before. However, increasing the complexity of UAVs and decreasing the cost, both contribute to a lack of implemented security measures and raise new security and safety concerns. For instance, the issue of implausible or tampered UAV sensor measurements is barely addressed in the current research literature and thus, requires more attention from the research community. The goal of this survey is to extensively review state-of-the-art literature regarding common sensor- and communication-based vulnerabilities, existing threats, and active or passive cyber-attacks against UAVs, as well as shed light on the research gaps in the literature. In this work, we describe the Unmanned Aerial System (UAS) architecture to point out the origination sources for security and safety issues. We evaluate the coverage and completeness of each related research work in a comprehensive comparison table as well as classify the threats, vulnerabilities and cyber-attacks into sensor-based and communication-based categories. Additionally, for each individual cyber-attack, we describe existing countermeasures or detection mechanisms and provide a list of requirements to ensure UAV's security and safety. We also address the problem of implausible sensor measurements and introduce the idea of a plausibility check for sensor data. By doing so, we discover additional measures to improve security and safety and report on a research niche that is not well represented in the current research literature. [ABSTRACT FROM AUTHOR]

Individual tree canopy extraction plays an important role in downstream studies such as plant phenotyping, panoptic segmentation and growth monitoring. Canopy volume calculation is an essential part of these studies. However, existing volume calculation methods based on LiDAR or based on UAV-RGB imagery cannot balance accuracy and real-time performance. Thus, we propose a two-step individual tree volumetric modeling method: first, we use RGB remote sensing images to obtain the crown volume information, and then we use spatially aligned point cloud data to obtain the height information to automate the calculation of the crown volume. After introducing the point cloud information, our method outperforms the RGB image-only based method in 62.5% of the volumetric accuracy. The A b s o l u t e E r r o r of tree crown volume is decreased by 8.304. Compared with the traditional 2.5D volume calculation method using cloud point data only, the proposed method is decreased by 93.306. Our method also achieves fast extraction of vegetation over a large area. Moreover, the proposed YOLOTree model is more comprehensive than the existing YOLO series in tree detection, with 0.81% improvement in precision, and ranks second in the whole series for m A P 50 - 95 metrics. We sample and open-source the TreeLD dataset to contribute to research migration. [ABSTRACT FROM AUTHOR]

Trees' structural defects are responsible for the reduction in forest product quality and the accident of tree collapse under extreme environmental conditions. Although the manual view inspection for assessing tree health condition is reliable, it is inefficient in discriminating, locating, and quantifying the defects with various features (i.e., crack and hole). There is a general need for investigation of efficient ways to assess these defects to enhance the sustainability of trees. In this study, the deep learning algorithms of lightweight You Only Look Once (YOLO) and encoder-decoder network named DeepLabv3+ are combined in unmanned aerial vehicle (UAV) observations to evaluate trees' structural defects. Experimentally, we found that the state-of-the-art detector YOLOv7-tiny offers real-time (i.e., 50–60 fps) and long-range sensing (i.e., 5 m) of tree defects but has limited capacity to acquire the patterns of defects at the millimeter scale. To address this limitation, we further utilized DeepLabv3+ cascaded with different network architectures of ResNet18, ResNet50, Xception, and MobileNetv2 to obtain the actual morphology of defects through close-range and pixel-wise image semantic segmentation. Moreover, the proposed hybrid scheme YOLOv7-tiny_DeepLabv3+_UAV assesses tree's defect size with an averaged accuracy of 92.62% (±6%). [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution. [ABSTRACT FROM AUTHOR]

This work experimentally addresses damage calibration of an unmanned aerial vehicle in operational condition. A wide range of damage level and types are simulated and controlled by an electric motor via pulse width modulation in this regard. The measurement is carried out via established protocols of using a piezo-patch on one of the 8 arms, utilising the vibration sensitivity and flexibility of the arms, demonstrating repeatability of such protocol. Subsequently, recurrence analysis on the voltage time series data is performed for detection of damage. Quantifiers of damage extent are then created for the full range of damage conditions, including the extreme case of complete loss of power. Experimental baseline condition for no damage condition is also established in this regard. Both diagonal-line and vertical-line based indicators from recurrence analysis are sensitive to the quantitative estimates of damage levels and a statistical test of significance analysis confirms that it is possible to automate distinguishing the levels of damage. The damage quantifiers proposed in this paper are useful for rapid monitoring of unmanned aerial vehicle operations of connection. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have emerged as efficient tools in disaster-stricken areas, facilitating efficient data dissemination for post-disaster rescue operations. However, the limited onboard energy of UAVs imposes significant constraints on their operational lifespan, thereby presenting substantial challenges for efficient data dissemination. Therefore, this work investigates a data dissemination scheme to enhance the UAVs' bandwidth efficiency in multi-UAV-enabled Internet of Vehicles, thereby reducing UAVs' energy consumption and improving overall system performance when UAVs hover along designated flight trajectories for data dissemination. Specifically, first, we present a software-defined network-based framework for data dissemination in multi-UAV-enabled IoV. According to this framework, we formulate a problem called C2BS (Coding-based Cooperative Broadcast Scheduling) that focuses on optimizing the UAVs' bandwidth efficiency by leveraging the combined benefits of coding and caching. Furthermore, we demonstrate the NP-hardness of the C2BS problem by employing a polynomial time reduction technique on the simultaneous matrix completion problem. Then, inspired by the benefits offered by genetic algorithms, we propose a novel approach called the Genetic algorithm-based Cooperative Scheduling (GCS) algorithm to address the C2BS problem. This approach encompasses a coding scheme for representing individuals, a fitness function for assessing individuals, operators (i.e., crossover and mutation) for generating offspring, a local search technique to enhance search performance, and a repair operator employed to rectify infeasible solutions. Additionally, we present an analysis of the time complexity for the GCS algorithm. Finally, we present a simulation model to evaluate the performance. Experimental findings provide evidence of the excellence of the proposed scheme. [ABSTRACT FROM AUTHOR]

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. [ABSTRACT FROM AUTHOR]

Safely landing unmanned aerial vehicles (UAVs) in unknown environments that are denied by GPS is challenging but crucial. In most cases, traditional landing methods are not suitable, especially under complex terrain conditions with insufficient map information. This report proposes an innovative multi-stage UAV landing framework involving (i) point cloud and image fusion positioning, (ii) terrain analysis, and (iii) neural network semantic recognition to optimize landing site selection. In the first step, 3D point cloud and image data are fused to attain a comprehensive perception of the environment. In the second step, an energy cost function considering texture and flatness is employed to identify potential landing sites based on energy scores. To navigate the complexities of classification for precise landings, the results are stratified by the difficulty of various UAV landing scenarios. In the third step, a network model is applied to analyze UAV landing site options by integrating the ResNet50 network with a convolutional block attention module. Experimental results indicate a reduction in computational load and improved landing site identification accuracy. The developed framework fuses multi-modal data to enhance the safety and feasibility of UAV landings in complex environments. [ABSTRACT FROM AUTHOR]

The majority of uses for UAVs are in precision agriculture, cinematography, aerial surveillance, military applications, disaster relief, construction inspection, and other applications. Nevertheless, a precise localization method is needed to accomplish these tasks on their own. This study concentrated on autonomous ground vision-guided landing of a fixed-wing unmanned aerial vehicle (UAV) in situations where GNSS is unavailable. To improve image detection and accuracy for UAV auto-landing, optimum Deep Learning (DL) approaches would be used in the design and implementation. Therefore, this research presents a DL-based auto-landing scheme for UAVs. Once the flying vehicle's motion consistency is confirmed to be unsatisfactory, an optimized Long Term Short Memory (LSTM) would be constructed to improve detection accuracy. To adjust the weight of the LSTM, an RDSBWO (Red Deer struck Black widow optimization) technique should be employed. The normal BWO and RDA are conceptually combined to create the RDSBWO model. The proposed DL concept uses image detection and spatial placement to automatically improve the inaccurate coordinates within a given range. Moreover, object tracking and localization were accomplished using Extended Kalman Filter (EKF-based) object localization. Comparing the adopted established LSTM + RDSBWO scheme to existing approaches such as LSTM + BWO (0.36), LSTM + RDA (0.439), LSTM + LA (0.489), LSTM + BOA (0.690), LSTM + AOA (0.489), LSTM + PRO (0.389), GRU (0.489), DBN (~ 0.610), and LSTM (~ 0.690), it achieves lower MSE (0.235) with better performance for the number of frames 4. [ABSTRACT FROM AUTHOR]

The object of this study is the process of forming a training dataset for diagnosing the technical condition of unmanned aerial vehicles (UAVs) using machine-learning algorithms. UAV flights are extremely important for various aspects of troop deployment. Combat UAV flights are performed under the influence of negative factors that cause flight special cases (FSC), which hinder the execution of combat missions, lead to mission failures, and result in the aircraft damage or loss. The available capabilities of autopilots are not enough for control under complex conditions, and in certain situations, the human operator cannot timely recognize a flight special case, including evaluation of the destructive impact of enemy’s electronic warfare systems on communication channels and operation of UAV. Therefore, the urgent issue is the intellectualization of onboard control systems, particularly towards recognizing the current technical state of UAV using artificial intelligence methods. To design such systems, labeled datasets are required. The procedure for forming datasets that consider the specificity of UAV construction and their combat use under adversarial conditions is not defined, necessitating the development of an appropriate method. Based on the well-known CRISP-DM methodology, a method for dataset formation has been proposed for subsequent use in artificial intelligence systems that use various machine-learning methods. This method differs from existing ones by considering the specificity of combat mission execution under adversarial conditions, which allowed for an 8.0 % increase in the accuracy of recognizing special cases in UAV flights by the onboard system. It also enabled timely detection of electronic warfare impacts on UAV and evaluation of the effectiveness of radio signal receivers jamming. [ABSTRACT FROM AUTHOR]

Over the years, thrips have transitioned from a minor nuisance to a major problem, significantly impacting the yield and quality of cotton. Unmanned aerial vehicles (UAVs) for plant protection have emerged as an effective alternative to traditional pesticide spraying equipment. UAVs offer advantages such as avoiding crop damage and enhancing pesticide deposition on the plants and have become the primary choice for pesticide application in cotton fields. In this study, a 2-year field experiment found that the thrips population in a cotton field in Xinjiang, China, exhibited gradual growth during the early flowering phase, peaking in late July. The thrips population gradually shifted from the lower canopy to the upper canopy as the cotton flowers opened layer by layer. From 09:00 to 11:00 (GMT+8) and 19:00 to 21:00 (GMT+8), thrips mainly flew outside the flowers, while from 17:00 to 19:00 (GMT+8), they mostly inhabited the inner whorls of flowers. The insecticides 10% cyantraniliprole oil dispersion and 10% spinetoram suspension concentrate, sprayed by UAV, had the best control effect on thrips, with 80.51% and 79.22% control effect after 7 days of spraying, respectively. The optimal spraying time for 10% cyantraniliprole oil dispersion was 19:00 (GMT+8), and the control effect on thrips reached 91.16% at 7 days of spraying. During the cotton flowering period, thrips inhabited flowers in the evening and flew outside during the day. The best control effect on thrips was achieved with UAV-sprayed 10% cyantraniliprole oil dispersion at 19:00 (GMT+8). [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) path planning is an important issue in UAV applications, with the goal of finding the optimal path to meet mission requirements, while considering factors such as avoiding obstacles and optimizing flight performance. To improve the efficiency of UAV path planning and enhance the smoothness and safety of UAV operation, this paper proposes a fusion optimization algorithm (GWO‐APF), which combines the grey wolf algorithm (GWO) and artificial potential field method (APF) for UAV path planning algorithms. Based on the GWO algorithm, this algorithm first sets planning constraints such as turning angle and tolerance to reduce the probability of local optima occurring; Second, using dimensionality reduction to improve the search efficiency of the GWO algorithm and generate the initial path; Finally, gravity is given to each point on the initial path, and the APF algorithm is used for path planning again. The simulation results show that the fused GWO‐APF algorithm has stronger path planning ability compared to the traditional GWO algorithm, and has the characteristics of short range and high safety. [ABSTRACT FROM AUTHOR]

This study focuses on developing a mathematical model for precise control and stabilization of unmanned aerial vehicles (UAVs) in various spatial conditions. Addressing the problem of achieving precise control and stability, the proposed solution designs a control system based on a linear-quadratic controller (LQR) and simulates it using a proportional-derivative (PD) controller implemented in Matlab/Simulink. The results demonstrate high precision and stability in controlling the UAV motion parameters - roll, pitch, yaw, and altitude. This important level of performance is achieved due to the adaptivity of the LQR-based control system, which optimizes control actions according to the unsteady dynamics of the UAV. The integration of the PD controller improves responsiveness and stability, providing precise motion control over a range of spatial states. These features effectively solve the problem by handling the complex dynamics of the UAV and providing precise control. The results are explained by the ability of the LQR to provide optimal control laws that minimize deviations using a quadratic cost function, while the PD controller quickly corrects errors and responds to disturbances. The benefits of this approach include a significant reduction in control errors by about 25-30 %, increased response speed to external disturbances, and reduced computational latency due to efficient processing compared to more resource-intensive methods such as model predictive control. The developed mathematical model can be applied in practice in conditions requiring robust real-time control and adaptation to dynamic changes in the environment. It is especially suitable for industries such as logistics, surveillance, and environmental monitoring, providing an effective and optimal solution for stabilizing and controlling the motion of UAVs in various spatial states. This approach improves the performance of UAVs and expands their capabilities in various operating conditions. [ABSTRACT FROM AUTHOR]

In the field of aviation, safety is a critical cornerstone, and the operation of the Unmanned Aerial Vehicle (UAV) systems is deeply connected with this principle. A thorough analysis and rigorous simulation and testing of aircraft systems are essential to avoid severe safety hazards. This paper delves into the safety issue in UAV operations, specifically regarding maintaining minimum safety distances under fluctuating wind conditions. The study introduces a novel solution based on a Deep Deterministic Policy Gradient (DDPG) model, a reinforcement learning method. The DDPG model was trained using a simulated environment created through the Gazebo simulator, with values for wind and gust conditions derived from historical records at the KLAF airport at Purdue University. The model's performance was evaluated regarding maintaining safe distances under these conditions. The results indicate that the DDPG model can accurately predict safety distances with relatively low error rates when predicting under different weather conditions. The findings significantly contribute to UAV safety operations, suggesting the potential future utilization of reinforcement learning methods to study enhancing airspace efficiency and obstruction avoidance in UAVs. [ABSTRACT FROM AUTHOR]

HEX Pixhawk 2.1 CUBE ORANGE+ in combination with Mission Planner software package is used for effective motion control of unmanned aerial and swimming vehicles. The article aims to develop efficient methods of flight trajectory generation for USVs to improve the accuracy and safety of autonomous missions. This research aims to develop and validate methods of trajectory construction for unmanned vehicles and analyze the motion of mobile robots taking into account the impact of currents. Different flight parameters such as altitude, velocity, and heading are considered, as well as methods for controlling the USVs according to the given targets. Special attention is given to safety and the configuration of sensors and devices required for autonomous flight. The author's results include the development of trajectory planning algorithms, evaluation of the effect of currents on the robot's motion, and development of software to acquire and process seafloor data. Graphs and computational results demonstrating the effect of current velocity on robot trajectory are presented for visualization and analysis. Software development methods for acquiring and processing seabed depth data using HEX Pixhawk 2.1 CUBE ORANGE+ hardware and an echolocation device are also described. The obtained results have both theoretical and practical significance. In the theoretical aspect, the proposed methods allow us to control USVs more efficiently and analyze their behavior in different conditions. In practical terms, it opens new perspectives for seabed exploration and autonomous missions with high accuracy and safety. [ABSTRACT FROM AUTHOR]

Path planning for the unmanned aerial vehicle (UAV) is to assist in finding the proper path, serving as a critical role in the intelligence of a UAV. In this paper, a path planning for UAV in three-dimensional environment (3D) based on enhanced gravitational search algorithm (EGSA) is put forward, taking the path length, yaw angle, pitch angle, and flight altitude as considerations of the path. Considering EGSA is easy to fall into local optimum and convergence insufficiency, two factors that are the memory of current optimal and random disturbance with chaotic levy flight are adopted during the update of particle velocity, improving the balance between exploration and exploitation for EGSA through different time-varying characteristics. With the identical cost function, EGSA is compared with seven peer algorithms, such as moth flame optimization algorithm, gravitational search algorithm, and five variants of gravitational search algorithm. The experimental results demonstrate that EGSA is superior to the seven comparison algorithms on CEC 2020 benchmark functions and the path planning method based on EGSA is more valuable than the other seven methods in diverse environments. [ABSTRACT FROM AUTHOR]

Due to the continuous growth of the world population and consequently the increase in the demand for food, the agricultural sector is required to be efficient and sustainable, that is, to guarantee food security, to promote local development and to reduce environmental pollution. On the other hand, we are currently experiencing a global climate crisis that causes alterations in hydrological cycles and an increase in temperature, which is causing economic losses in the agricultural sector. For this reason, it is necessary to incorporate modern Precision Agriculture (PA) technologies into traditional agricultural practices to improve yield and profits for farmers, while achieving environmental benefits. In this context, the present study monitored the phenological development and health of the maize crop in plots in the municipality of Atlacomulco, State of Mexico, Mexico through multispectral images from the Sentinel-2 satellite and an unmanned aerial vehicle (UAV). A spatiotemporal analysis of vegetation indices (VIs) was carried out to determine their sensitivity to changes in the greenness of the crop, in an environment of real conditions. In addition, the agricultural characteristics provided by the farmers managing the plots were used to confirm the findings. Similarly, a lack of homogeneity was detected in the evaluated plots, indicating a nutrient or water deficiency in some of them. Also, the VH and VV polarized backscatters from the Sentinel-1 satellite were analyzed, demonstrating a potential for VH polarization in the absence of data from optical sensors due to cloudiness. [ABSTRACT FROM AUTHOR]

In a device-to-device content delivery network (D2D-CDN), user equipment (UE) covered by a base station can forward content received from the base station to other uncovered UEs in a multi-hop manner to increase network coverage. However, infrastructures, such as local base stations, are destroyed and D2D-CDN cannot operate normally due to the lack of base stations as a content source in disaster scenarios. Unmanned aerial vehicle (UAV) can temporarily serve as a content source with its flexibility to deliver content to terrestrial UEs, which we call it UAV base station (UAV-BS). However, the placement of UAV-BS will be affected by many factors, such as UE distribution and communication interference. Therefore, how to place UAV-BS in a reasonable way to maximize the network throughput and the number of covered UEs while ensuring the communication quality of service (QoS) is still a challenging problem. To solve this problem, first, we propose a UAV-BS placement optimization model (U-POM) and formulate an optimization problem. Secondly, we prove that our maximization problem is NP-hard and propose an improved artificial bee colony algorithm (ABC), called the global optimal artificial bee colony (GOABC) algorithm. Finally, we construct an analysis and testing platform to analyze the accuracy and the stability of the GOABC algorithm. The results highlight the effectiveness of the proposed GOABC algorithm in maximizing network throughput and the number of covered UEs while ensuring the communication QoS. [ABSTRACT FROM AUTHOR]

. Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios. [ABSTRACT FROM AUTHOR]

In recent years, the estimation of tobacco field areas has become a critical component of precision tobacco cultivation. However, traditional satellite remote sensing methods face challenges such as high costs, low accuracy, and susceptibility to noise, making it difficult to meet the demand for high precision. Additionally, optical remote sensing methods perform poorly in regions with complex terrain. Therefore, Unmanned Aerial Vehicle multispectral remote sensing technology has emerged as a viable solution due to its high resolution and rich spectral information. This study employed a DJI Mavic 3M equipped with high-resolution RGB and multispectral cameras to collect tobacco field data covering five bands: RGB, RED, RED EDGE, NIR, and GREEN in Agang Town, Luoping County, Yunnan Province, China. To ensure the accuracy of the experiment, we used 337, 242, and 215 segmented tobacco field images for model training, targeting both RGB channels and seven-channel data. We developed a tobacco field semantic segmentation method based on PP-LiteSeg and deeply customized the model to adapt to the characteristics of multispectral images. The input layer's channel number was adjusted to multiple channels to fully utilize the information from the multispectral images. The model structure included an encoder, decoder, and SPPM module, which used a multi-layer convolution structure to achieve feature extraction and segmentation of multispectral images. The results indicated that compared to traditional RGB images, multispectral images offered significant advantages in handling edges and complex terrain for semantic segmentation. Specifically, the predicted area using the seven-channel data was 11.43 m² larger than that obtained with RGB channels. Additionally, the seven-channel model achieved a prediction accuracy of 98.84%. This study provides an efficient and feasible solution for estimating tobacco field areas based on multispectral images, offering robust support for modern agricultural management. [ABSTRACT FROM AUTHOR]

In the field of healthcare logistics, the reliance on conventional transport methods such as cars for the delivery of monoclonal antibodies (mAbs) is susceptible to challenges posed by traffic and infrastructure, leading to increased and unpredictable transport times. Recognizing the potential role of drones in mitigating these challenges, we aimed to investigate the impact of medical drone transport on the stability of mAbs. Compromised stability could lead to aggregation and immunogenicity, thereby jeopardizing the efficacy and safety of mAbs. We studied the transportation of vials as well as ready-to-administer infusion bags with blinatumomab, tocilizumab, and daratumumab. The methodology involved the measurement of both temperature and mechanical shock during drone transport. Moreover, the analytical techniques High Performance Size-Exclusion Chromatography (HP-SEC), Dynamic Light Scattering (DLS), Light Obscuration (LO), Micro-Flow Imaging (MFI), and Nanoparticle Tracking Analysis (NTA) were employed to comprehensively assess the presence of aggregates and particle formation. The key findings revealed no significant differences between car and drone transport, indicating that the stability of mAbs in both vials and infusion bags was adequately maintained during drone transport. This suggests that medical drones are a viable and reliable means for the inter-hospital transport of mAbs, paving the way for more efficient and predictable logistics in healthcare delivery. [ABSTRACT FROM AUTHOR]

Object detection in unmanned aerial vehicle (UAV) scenes is a challenging task due to the varying scales and complexities of targets. To address this, we propose a novel object detection model, AFE-YOLOv8, which integrates three innovative modules: the Multi-scale Nonlinear Fusion Module (MNFM), the Adaptive Feature Enhancement Module (AFEM), and the Receptive Field Expansion Module (RFEM). The MNFM introduces nonlinear mapping by exploiting the property that deformable convolution can dynamically adjust the shape of the convolution kernel according to the shape of the target, and it effectively enhances the feature extraction capability of the backbone network by integrating multi-scale feature maps from different mapping branches. Meanwhile, the AFEM introduces an adaptive fusion factor, and through the fusion factor, it adaptively integrates the small-target features contained in the feature maps of different detection branches into the small-target detection branch, thus enhancing the expression of the small-target features contained in the feature maps of the small-target detection branch. Furthermore, the RFEM expands the receptive field of the feature maps of the large- and medium-scale target detection branches through stacked convolution, so as to make the model's receptive field cover the whole target, and thereby learn more rich and comprehensive features of the target. The experimental results demonstrate the superior performance of the proposed model compared to the baseline in detecting objects of various scales. On the VisDrone dataset, the proposed model achieves a 4.5% enhancement in mean average precision (mAP) and a 5.45% improvement in average precision at an IOU threshold of 0.5 (AP50). Additionally, ablation experiments conducted on the challenging DOTA dataset showcase the model's robustness and generalization capabilities. [ABSTRACT FROM AUTHOR]

In the traditional picking and sorting process of fragrant pears, the quality control process is not perfect, and the identification and extraction methods are not mature enough. By combining the Internet of Things (IoT) and image detection technologies, the image data of pear planting area in the orchard was obtained, and the image of pear was preprocessed, denoised, and feature extracted by the visual background extraction algorithm. In order to verify the actual effect of the optimized identification and extraction method for fragrant pears using IoT and image detection technologies, a comparative experiment was conducted. The results show that compared with the traditional pear recognition and extraction method, the optimized pear recognition and extraction method has a better predictive effectiveness in the process of pear image processing, and the recall and accuracy rates are improved by approximately 8% and 12.7%, respectively. This verifies the reliability and efficiency of the identification extraction method for fragrant pear based on IoT and image detection technologies. In this paper, the traditional pear identification and extraction methods are improved by IoT technology and UAV (unmanned aerial vehicle), which improves the picking efficiency and sorting quality of pear picking robots. [ABSTRACT FROM AUTHOR]

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) offer invaluable contributions to wildlife observation, reducing costs and human risk while minimizing disturbance to animals during aerial surveys. However, within a population, individual animals can exhibit different responses to UAVs. This indicates that the level of UAV disturbance might differ between individuals even when the disturbance is mild at the population level. However, few studies have investigated UAV disturbance at the individual level. In this study, we explored the variation in response to UAVs among individual Weddell seals along the Terra Nova Bay coastline in Antarctica. We also examined how internal factors such as age, group composition and whether their eyes were open, as well as external factors such as UAV flight schemes, influenced responses. Our findings showed substantial variation among individual seals in both their overall response and changes in response to descending UAVs. Notably, seals were more sensitive to UAVs with vertical flight schemes than to those with horizontal flight schemes. When seals were in groups or had their eyes closed, their vigilance responses to UAVs decreased. Therefore, our findings highlight the importance of not only considering individual factors but also avoiding vertical flight patterns during UAV-based aerial surveys of Weddell seals, thus minimising disturbance. Additionally, we recommend developing more effective approaches for wildlife research that utilize UAVs by incorporating the consideration of individual differences in responses to UAVs. • UAV effects on wildlife rarely focus on individual impacts. • Individual Weddell seals' responses to UAVs varied. • UAV vertical flights made seals more vigilant than horizontal flights. • Seals in groups or with closed eyes showed less UAV vigilance. • Consider both individual states and UAV flight patterns to reduce disturbance. [ABSTRACT FROM AUTHOR]

Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN). The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows. [ABSTRACT FROM AUTHOR]

Intelligent identification of the different lithology of outcrops has been a major research challenge in the field of geology. The manual identification of outcrop lithology is labor-intensive and lacks systematicity and consistency. Intelligent lithology identification based on deep convolutional neural networks can significantly reduce such workload and also serves as the foundation for intelligent outcrop research. In this paper, we propose a workflow based on the DeepLabV3+ semantic segmentation neural network algorithm. The algorithm can intelligently identify the lithology of outcrop photos obtained by unmanned aerial vehicle (UAV) oblique photography. Based on this workflow, we can complete the tasks of dataset creation, model training, intelligent identification of outcrop lithology, and model accuracy evaluation. The dataset used in this paper is composed of high-resolution photos of the Fugu Tiansheng Bridge Shihezi Formation profile collected by UAV, which are annotated with three major categories and five subcategories of lithology. Then, we input the data into four types of neural networks, including a fully convolutional network (FCN), SegNet, U-Net, and DeepLabV3+, for training. The results indicate that the recognition accuracies of the four neural network algorithms, FCN, SegNet, U-Net, and DeepLabV3+, for outcrop lithology classification tasks based on UAV oblique photography images are 90.81%, 90.44%, 92.03%, and 93.88%, respectively. Among such algorithms, the DeepLabV3+ semantic segmentation neural network algorithm is reliable and accurate, and can provide a basis for intelligent lithology recognition. This is fundamental for promoting the automation, intelligence, and digitalization of geological outcrop research. [ABSTRACT FROM AUTHOR]

To address the issue of needing drone aerial photogrammetry in certain projects where there is a lack of basic control points around the survey area, or it is difficult for personnel to enter the area to set up photo control points, this paper proposed a fast aerial survey method for drones without photo control based on global gravity field models, and the accuracy of this method was verified through experiments. The research used three global gravity field models: EGM2008, XGM2019, and EIGEN-6C4, to calculate the height anomalies in six experimental areas and converted the geodetic heights in the image exterior orientation elements to normal heights, which were then input into the aerial survey software for aerial triangulation and 3D modeling. The analysis of the results shows that, the elevation accuracy of the photo control-free aerial survey results based on the EGM2008, XGM2019, and EIGEN-6C4 models is between 10 ~ 20 cm, which meets the requirements of 1:2 000 mapping precision. To further improve the accuracy of the results, this paper adopted a method based on the EIGEN-6C4 gravity field model and the "remove-fit-restore" normal height calculation method, which converted the geodetic heights in the exterior orientation elements of the images obtained by the drone aerial survey system into normal heights. The method calculated the height anomalies of 16 GNSS leveling points across the country, fitted a normal height model for the experimental area, and achieved higher accuracy drone aerial surveys without photo control. The experimental results show that, the elevation accuracy of the photo control-free aerial survey results based on the EIGEN-6C4 model "remove-fit-restore"method can reach the centimeter level, which is higher than the method based only on the EGM2008, XGM2019, and EIGEN-6C4 gravity models, and can meet the requirements of 1:500 mapping precision. [ABSTRACT FROM AUTHOR]

The relevance of this research topic lies in the need to consider various options for building financial models to commercialize the results of innovative R&D with account for the specifics, goals, and objectives of projects [1, 2]. A practical option is presented to form financial models of commercializing the result of an innovative project to create a software and hardware complex (PAC) for automated visual inspection control (VIC) of boilers of thermal power plants (TPPs). The research is based on the principles of the formation of financial models for the commercialization of R&D results. A sales plan as the volume of services provided for the inspection of TPP equipment and an operation schedule with account for the potential PAC capabilities are proposed, and the price of the service is justified. The cost of a flight hour of operation of an unmanned aerial vehicle (UAV) is taken as a unit of service. The efficiency of R&D investments has been calculated considering the profit that ensures the payback of the project. A graphical interpretation of the relationship between the price of the PAC service and the expected net discounted income is given. [ABSTRACT FROM AUTHOR]

The objective of this research was to assess the feasibility of remote sensing (RS) technology, specifically an unmanned aerial system (UAS), to estimate Bambara groundnut canopy state variables including leaf area index (LAI), canopy chlorophyll content (CCC), aboveground biomass (AGB), and fractional vegetation cover (FVC). RS and ground data were acquired during Malaysia's 2018/2019 Bambara groundnut growing season at six phenological stages; vegetative, flowering, podding, podfilling, maturity, and senescence. Five vegetation indices (VIs) were determined from the RS data, resulting in single-stage VIs and cumulative VIs (∑VIs). Pearson's correlation was used to investigate the relationship between canopy state variables and single stage VIs and ∑VIs over several stages. Linear parametric and non-linear non-parametric machine learning (ML) regressions including CatBoost Regressor (CBR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Huber Regressor (HR), Multiple Linear Regressor (MLR), Theil-Sen Regressor (TSR), Partial Least Squares Regressor (PLSR), and Ridge Regressor (RR) were used to estimate canopy state variables using VIs/∑VIs as input. The best single-stage correlations between canopy state variables and VIs were observed at flowering (r > 0.50 in most cases). Moreover, ∑VIs acquired from vegetative to senescence stage had the strongest correlation with all measured canopy state variables (r > 0.70 in most cases). In estimating AGB, MLR achieved the best testing performance (R2 = 0.77, RMSE = 0.30). For CCC, RFR excelled with R2 of 0.85 and RMSE of 2.88. Most models performed well in FVC estimation with testing R2 of 0.98–0.99 and low RMSE. For LAI, MLR stood out in testing with R2 of 0.74, and RMSE of 0.63. Results demonstrate the UAS-based RS technology potential for estimating Bambara groundnut canopy variables. [ABSTRACT FROM AUTHOR]

The precise positioning issue of oblique aerial image has been widely studied in recent years. However, there are still some deficiencies in applying the existing methods to highly time-sensitive engineering. For the real-time positioning issues of oblique images involved in Unmanned Aerial Vehicle's (UAV's) patrolling applications, existing photogrammetry method cannot meet the real-time positioning requirements, existing binocular vision method cannot meet the dynamic and precise positioning requirements, existing optical flow method cannot meet the absolute positioning requirements, and existing multi-source feature matching method cannot meet the robust positioning requirements. In order to meet the real-time, dynamic, precise, absolute and robust positioning requirements of UAV's patrolling images, a real-time positioning model for UAV's patrolling images based on airborne LiDAR point cloud fusion is proposed. First, a precise Digital Surface Model (DSM) is generated by rasterizing and imaging the raw airborne LiDAR point cloud, in which a pixel's grayscale is exactly equal to elevation of local area covered by the pixel. Second, the generated DSM and UAV's patrolling image are fused under specific geometric constrains, so as to realize real-time positioning of UAV's patrolling image pixel by pixel. Finally, more precise positioning of selected key points on UAV's patrolling image can be realized by performing Principal Component Analysis (PCA)on the raw airborne LiDAR point cloud that surrounds the selected key points. The above methods are analyzed and verified by three groups of practical experiments, and results indicate that the proposed model can achieve real-time positioning of a single UAV's patrolling image (4000 × 6000 pixels) with an accuracy of 0.5 m within 0.38 seconds in arbitrary areas, and can further realize precise positioning of any selected key point on UAV's patrolling image with an accuracy of 0.2 m in 0.001 seconds. [ABSTRACT FROM AUTHOR]

T his study presents a Geographic Information Systems (GIS) and Unmanned Aerial Vehicle (UAV) based approach to determine suitable roof patches of buildings for solar panel installation in Harran University (Şanlıurfa) campus area. Initially, the Solar Radiation Potential (SRP) of the study area was calculated using a UAV-based Digital Surface Model (DSM) in GIS. Then, a correction process was applied to this theoretically calculated SRP by using an adjustment coefficient derived from 5-year measurements of the Solar Power Plant (SPP) located in the region. This coefficient was used to adjust the calculated SRP and compared with the SPP measurements at a concurrent period. The rooftop objects were segmented by textural analysis to determine the suitable panel installation patches on the buildings. Then, the obtained suitable patches are divided into four different classes considering the adjusted total SRP to find panel installation priority. Finally, the calculated electricity potential of the suitable roof patches could meet approximately 65% of the yearly consumption of campus buildings. This paper reveals that in GIS-based SRP studies, it is necessary to detect the rooftop objects to obtain the solar panel installation area more accurately, and a correction should be applied to approximate the theoretically calculated SRP values to the actual values. [ABSTRACT FROM AUTHOR]

Ra pid and accurate surveying has always attracted great interest in all scientific and industrial activities that require high-resolution topographic data. The latest automation and advancement in geomatics engineering are remote sensing solutions using Unmanned Aerial Systems (UAS) and Structure from Motion (SfM) with Multi-View Stereo (MVS) photogrammetry. This research aimed to find the influence of flight height, Ground Control Point (GCP), and software on the geometric accuracy of UAS-SfM-derived Digital Surface Models (DSMs) and orthoimages, as well as to analyze and evaluate the accuracy of UAS-SfM as a rapid and low-cost alternative to conventional survey methods. To achieve the aim of the study, aerial surveys using a fixed-wing UAS and field surveys using RTK GNSS and total station were conducted. A total of 16 photogrammetric projects were processed using different GCP configurations, and detailed statistical analysis was performed on the results. Moreover, the contribution of cross flight on bundle adjustment was investigated empirically by conducting a combined photogrammetric image processing. The analysis revealed that flight height, GCP number and distribution, and the processing software significantly affect products' quality and accuracy. Evaluation of the achieved accuracies was made based on the American Society for Photogrammetry and Remote Sensing (ASPRS) positional accuracy standard for digital geospatial data. The findings of this study revealed that using the optimal flight height and GCP configuration, 3D models, orthomosaics and DSMs can be rapidly reconstructed from 2D images with the quality and accuracy sufficient for most terrain analysis applications, including civil engineering projects. [ABSTRACT FROM AUTHOR]

The research objects are gas turbine engines parts, manufactured using an innovative method of additive manufacturing - selective laser sintering. The main problem solved in this work is the low quality of the surface layer and the residual porosity of the parts obtained by this method, which significantly limits their operational characteristics and durability. As a result of the experimental studies, rational operating parameters of diamond smoothing were established. This allowed to significantly improve the surface quality and increase the operational characteristics of parts made of heat-resistant alloys INCONEL 718 and an intermetallic alloy based on titanium aluminide OX45-3ODS. The effectiveness of diamond smoothing is explained by local plastic deformation and compaction of the surface layer of parts under the influence of high contact pressures and temperatures. This leads to a significant reduction in surface roughness, an increase in the surface hardness due to strain hardening and a significant reduction in the size and number of residual pores. A characteristic feature of the obtained results is the ability to control the quality parameters of the surface layer by varying the diamond smoothing operating parameters - smoothing force, feed, radius, and geometry of the smoother's working part. The established regularities of the smoothing operating parameters have an impact on the quality characteristics of the surface. This information can be utilized in the development of highly efficient technological processes for the production and restoration of gas turbine engines, critical components of unmanned aerial vehicles, obtained through selective laser sintering. Implementing the elaborated technological recommendations will permit broadening the range of goods produced by additive manufacturing and enhancing their capacity and dependability during operation under conditions of cyclical loads and extreme temperatures. [ABSTRACT FROM AUTHOR]