Utilization Of Unmanned Aerial Vehicle Research Articles

Adaptive dynamic programming–based fault-tolerant control of multi-UAV formation system

With the increasing utilization of unmanned aerial vehicle (UAV) swarms in civilian and military field applications, it is crucial to eliminate the potential impacts of UAV faults on task completion and efficiency. To address this issue, the present paper investigates a fault-tolerant control scheme based on the adaptive dynamic programming (ADP) method and fault observer. The whole closed-loop system is composed of a position loop and an attitude loop. An ADP-based position-tracking controller is established with an improved cost function that can simultaneously represent actuator faults, control inputs, and tracking errors. In addition, a single-layer critic neural network is constructed to estimate the cost function and approximate control inputs. In the process of adjusting the weights of the neural network, a unique adjustment method combining policy gradient and prioritized experience replay is adopted to improve the data usage efficiency and speed up the weight convergence. Furthermore, a sliding mode–based attitude controller is utilized for the inner-loop attitude subsystem. The significant feature of this scheme is that it has the ability to self-learn and self-adapt. A Lyapunov stability analysis is performed to verify whether the signals are uniformly ultimately bounded. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme.

Utilization of Unmanned Aerial Vehicle for Monitoring and Surveillance of Aquaculture Farms: A Proposed Framework

Abstract: In this paper, unmanned aerial vehicles (UAVs) for monitoring and surveillance within aquaculture farms situated in Iloilo, Philippines are investigated. The principal aim is to create a framework that can provide guidance on how to use UAVs for effective monitoring, data collection, and regulatory control within the aquaculture sector under the supervision of BFAR – Department of Agriculture. The study examines the performance of UAVs in real-time surveillance mapping as well as geotagging and geo-referencing that would help in observing fish behavior and environmental settings. The research assesses the precision of UAVs in executing these assignments and compares various models based on their effectiveness in monitoring perimeters environmental data collecting and following changes in fish behavior. Findings reveal that UAVs especially those with high detection precision levels and advanced mapping capabilities may be good tools for improving management practices on aquaculture farms. The paper suggests some ways we may adopt UAV technology within aquaculture emphasizing its potential to minimize operational costs while at the same time increasing productivity. With the help of these findings, it is possible to develop initial guidelines for UAV use in the aquaculture industry that will serve as a basis for a regulatory framework ensuring the sustainable and efficient functioning of aquaculture farms. This framework seeks to support local government initiatives in enhancing farm monitoring, protecting the environment and managing resources by using advanced UAV technology.

Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

Soybean seedling detection and counting from UAV images based on an improved YOLOv8 Network

Abstract. The utilization of unmanned aerial vehicle (UAV) for soybean seedling detection is an effective way to estimate soybean yield, which plays a crucial role in agricultural planning and decision-making. However, the soybean seedlings objects in the UAV image are small, in clusters, and occluded each other, which makes it very challenging to achieve accurate object detection and counting. To address these issues, we optimize the YOLOv8 model and propose a GAS-YOLOv8 network, aiming to enhance the detection accuracy for the task of soybean seedling detection based on UAV images. Firstly, a global attention mechanism (GAM) is incorporated into the neck module of YOLOv8, which reallocates weights and prioritizes global information to more effectively extract soybean seedling features. Secondly, the CIOU loss function is replaced with the SIOU loss, which includes an angle loss term to guide the regression of bounding boxes. Experimental results show that, on the soybean seedling dataset, the proposed GAS-YOLOv8 model achieves a 1.3% improvement in mAP@0.5 and a 6% enhancement in detection performance in dense seedling areas, when compared to the baseline model YOLOv8s.When compared to other object detection models (YOLOv5, Faster R-CNN, etc.), the GAS-YOLOv8 model similarly achieved the best detection performance. These results demonstrate the effectiveness of the GAS-YOLOv8 in detecting dense soybean seedlings, providing more accurate theoretical support for subsequent yield estimation.

Utilization of Unmanned Aerial Vehicle (UAV) Technology to Identify Land Cover in Kedai Damar Village, Serdang Bedagai, North Sumatera

Remote sensing has been widely used in various fields, one of which is to provide information related to land cover classification Kedai Damar Village is one of 13 villages in the Tebing Tinggi sub-district, Serdang Bedagai Regency. The settlement of Kedai Damar is the one that is most remote from the headquarters of the high cliffs sub-district. This settlement is situated in the center of the PT.Perkebunan Nusantara-IV plantation region in Pabatu North Sumatera. As a result, several rural towns have opened shops providing food and other essentials to farmers and farmworkers in order to rely on PT. Perkebunan Nusantara-IV plantation. This research was conducted in the Kedai Damar Village administrative center area using unmanned aircraft technology and then visually interpretation of aerial photos and mapping administrative area land cover in the Kedai Damar Village administrative center area using processed data in the form of orthophoto from agisoft metashape software. Based on the results of the study, the Kedai Damar Village area has 5 types of land cover classifications that can be identified visually based on 9 elements of interpretation (hue, color, shape, size, texture, pattern, shadow, site and association) as well as ground checks or data collection and observations. In the field. there are 5 types of land cover that can be visually identified, including non-oil Palm, open land, factory Area, settlement, oil palm plantations.

OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

Air Channel Planning Based on Improved Deep Q-Learning and Artificial Potential Fields

With the rapid advancement of unmanned aerial vehicle (UAV) technology, the widespread utilization of UAVs poses significant challenges to urban low-altitude safety and airspace management. In the coming future, the quantity of drones is expected to experience a substantial surge. Effectively regulating the flight behavior of UAVs has become an urgent and imperative issue that needs to be addressed. Hence, this paper proposes a standardized approach to UAV flight through the design of an air channel network. The air channel network comprises numerous single air channels, and this study focuses on investigating the characteristics of a single air channel. To achieve optimal outcomes, the concept of the artificial potential field algorithm is integrated into the deep Q-learning algorithm during the establishment of a single air channel. By improving the action space and reward mechanism, the resulting single air channel enables efficient avoidance of various buildings and obstacles. Finally, the algorithm is assessed through comprehensive simulation experiments, demonstrating its effective fulfillment of the aforementioned requirements.

Assessing narrow brown leaf spot severity and fungicide efficacy in rice using low altitude UAV imaging

The utilization of unmanned aerial vehicle (UAV)-based imaging systems offers precise detection of plant diseases and aids in decision-making regarding fungicide applications to optimize disease control. This study employed a twin-lens multispectral camera mounted on a low-altitude UAV to capture imagery data from rice field plots that were treated with different fungicides. The objectives of this study were to assess the severity of narrow brown leaf spot (NBLS) caused by Cercospora janseana and to evaluate the efficacy of fungicide control. Eighteen color features and vegetation indices were extracted from RGB images, while nine color features and vegetation indices were extracted from multispectral images. Through correlation analysis, four spectral features, namely Lab-a, ExGR, VDVI, and g, were found to exhibit high correlations with disease severity. Specifically, RGB imagery had greater correlation coefficients (exceeding 0.95) for both ExGR and Lab-a features compared to multispectral imagery. A multifeature inversion modeling approach was employed, using support vector regression with the top four spectral features to predict NBLS severity. The results indicated R2 values were above 0.93 for all support vector regressions. Furthermore, the efficacies of ten different fungicide treatments were evaluated, with UAV imaging consistently aligning with ground truth rating data in terms of efficacy ranking. These results demonstrate the potential of UAV imagery for use as a valuable tool for NBLS detection and assessing fungicide efficacy, offering significant benefits in the management of NBLS, which is a globally important disease in rice.

Utilization of Unmanned Aerial Vehicle In Smart Agriculture

Unmanned aerial vehicles (UAVs) with vertical take-off and landing (VTOL) are known as drones. The World Health Organisation estimates that there are millions of chemical cases each year. More than one large integer death is caused year by human handling and use of pesticides, mainly in developing countries. In addition to cancer, endocrine disruption, issues with reproduction and craniums from chemical exposure, respiratory illnesses, allergies, and hypersensitivity are among the detrimental effects of pesticides on human health. Other insecticides may cause inflammation of the skin and eyes. There are other pesticides with highly dangerous poisons. Other pesticides also have an effect on the body's internal secretions and systems. Low exposure levels during spraying may be harmful to your health. A wide range of health effects on the body brought on by chemical exposure include changed or uncontrollable mood, behavior, loss of coordination, slower response times, decreased eye sight, and state of mind. To save time and labour, crop fertilizer is sprayed using a drone. The primary goal of this project is to design a framework for the drone that sprays fertiliser over farms. Include a description of the creation and design of the quadcopter system's spaying mechanism.

Utilization of Unmanned Aerial Vehicle (UAV) for Topographic Survey Using Ground Control Points (GCP) from Geodetic GNSS

In connection with the development plan of the USK campus II area, it is necessary to map the area to support and complete the necessary data. Mapping with an Unmanned Aerial Vehicle (UAV) is a strategy or way for large-scale mapping with a faster and more efficient time. It can save time compared to using conventional survey methods. Ground Control Point (GCP) measurements are carried out with geodetic GNSS to improve the accuracy/correction of the geometry of the aerial portrait results. This research aims to provide preliminary information about the topographic conditions and current appearance of the USK campus II development location. The results of the aerial photo research with a spatial resolution of 5 cm/pixel show that most of the area looks green, which is a forest area. Some areas in the west and north, such as in the rectorate block, are C excavation mining areas. Based on contour data in the USK II area, the PON block's ground elevation is 66 to 301 meters, in the faculty block, 50 to 260 meters, and in the rectorate block, 90 to 188 meters.

Unmanned aerial vehicle implementation for pavement condition survey

The Utilization of Unmanned Aerial Vehicle (UAV) as a vehicle for comprehensive and fast road condition data acquisition is expected to minimize or even replace conventional road condition surveys in the field. In addition, the final UAV product in the form of a 2D/3D model of road conditions has been proven to be used as a medium for interpreting road damage and measuring the dimensions of road damage without having to do it in the field. The significant difference between this study and other studies is found in the use of the Surface Distress Index and Pavement Condition Index methods for assessing road conditions on the 2D/3D model of the UAV results. No research compares the use of the two methods in 2D/3D models. This research took photos of pavement conditions using drones to produce 2D and 3D models as visual media for assessing pavement conditions using the Pavement Condition Index and Surface Distress Index methods. The results of the assessment on the model compared with the assessment in the field. In comparison, the dimension level of accuracy of pavement distress in the model against the results of manual measurements in the field, edge cracking has the lowest accuracy value of 75.72%, and joint reflective crack has the highest accuracy value of 97.86%. The linear regression equation for the Pavement Condition Index method in the model against manual in the field is y = 0.931x + 3.6003 with a coefficient of determination r2 = 0.86 and an MSE of 121.333, while the linear regression equation for the Surface Distress Index method in the model against manual in the field is y = 0.5831x + 28.867 with a coefficient of determination that is r2 = 0.653 and MSE = 2115. Based on the comparison above, in this study, the application of UAV for pavement condition assessment is more precise using the Pavement Condition Index method.

Coast Guard Aviation Force Preparedness for the Utilization of Unmanned Aerial Vehicle

Coast Guard Aviation Force Preparedness for the Utilization of Unmanned Aerial Vehicle

Utilization of unmanned aerial vehicle (UAV) in identifying the characteristics of the riparian ecosystem of the Percut River, North Sumatra Province

The Percut River border area has changed its function. Changes in land use result in a decrease in the area and types of vegetation that grow on river borders. Considering that the area is quite long and inaccessible in some locations, it can identify riparian vegetation by utilizing remote sensing technology. This study aims to identify the characteristics of the Percut river riparian vegetation using a UAV. Eight vegetation indices were used to analyse land cover types in this riparian ecosystem, namely Green-red ratio (GR), Green-red vegetation index (GRVI), RGB-based vegetation index (RGBVI), Visible atmospherically resistant index (VARI), Simple blue-green ratio (BGI2), Excess green index (ExG), Normalized green-blue difference index (NGBDI) and Modified green-red vegetation index (MGRVI). The analysis of the vegetation index based on RGB images shows that not all indices used are good in separating vegetation from other land covers. Analysis of riparian vegetation characteristics based on the RGB index is recommended using the RGBVI index (RGB-based vegetation index).

Utilization of UAV (Unmanned Aerial Vehicle) technology for mangrove species identification in Belawan, Medan City, North Sumatera, Indonesia

The mangrove forests in Indonesia are starting to decrease over time because there is still a lack of knowledge among people around the coast about the importance of mangroves. Kampung Nelayan, Medan Belawaan, is a coastal community area whose life is very dependent on coastal, mangrove, and water resources. Unmanned Aerial Vehicle (UAV) technology has the potential to provide a fast, cost-effective, and efficient mangrove mapping technique. It is very useful because mangrove areas are located in remote areas, where field measurements are difficult, time-consuming, and expensive. The objective of this study is to analyze mangrove species using UAV imagery with Object-Based Image Analysis (OBIA) classification. The object-based classification result for the overall accuracy is 82.94% where there are 7 classes of mangrove species based on the classification process, including: Avicennia alba, Avicennia officinialis, Avicennia Marina, Rhizopora apiculata, Nypah fruticans, Scyphipora hydrophylacea, Bruguiera gymnorriza. There are also two classes for non-mangrove, consisting of the water body and non-mangrove. The largest area of mangrove species in the research site is Avicennia Marina with a percentage of 33.86% covering an area of 7.80 Ha. The second-largest mangrove species with a percentage of 21.88% is Avicennia officinalis with an area of 5.04 Ha.

상호 다중 모델을 이용한 멀티로터 프로펠러 고장 감지

Many studies are being promoted due to the miniaturization of equipment installed in the UAV(Unmanned Aerial Vehicle). Among them, the quadrotor of MAV(Multirotor Aerial vehicle) is the most popular UAV and has been developed in various fields. Practical utilization of UAV requires stable flight or emergency action in fault of UAV parts, but a quadrotor with one motor damaged cannot perform stable flight due to unbalanced force applied to the quadrotor frame. For this reason the development of hexa- or octorotor with more motors is underway and the need to develop FDI(Fault Diagnosis and Isolation) algorithms to use with MAV is increasing. In this paper we designed a simulation based on MAV dynamic and FDI used to classify fault that occurred in the simulation. For fault diagnosis the model based FDI algorithm IMM(Interacting Multiple Model) was applied because the fault affects the system dynamic and output. IMM uses multiple filter models in simultaneously and then compare to the filters estimation and target system output to give weight to suitable filter model. IMM showed accurate and fast fault perceive and classification performance when a fault occurred in the MAV simulation.

Unmanned Aerial Vehicle (UAV)-Based for Slope Mapping and the Determination of Potential Slope Hazard

This paper discusses the applications of unmanned aerial vehicle (UAV) for slope mapping and the determination of potential slope hazard of three selected areas. With the development of modern technology, the utilization of UAV to gather data for slope mapping can be considered as finest method as it is quick, reliable, precise, cost-effective and also easily to operate. High imagery quality is essential for the effectiveness and nature of normal mapping output such as Digital Surface Model (DSM) and also Ortho Images. The utilization of UAV to capture aerial photo helps to gather information from a normal area to an area which almost impossible to reach. With the data obtained by the UAV, it will later process in established software and the analysis of slope profile of certain selected study areas will be done. Mapping using UAV was within the area of Pahang Matriculation College. Three slope area namely as Area 1, Area 2 and Area 3 are marked. Further analyses are conducted in these three areas to obtain the information of potential slope hazard based on slope angles. For Area 1, the slope is marked at the coordinate of (286136.265, 412066.821) to (286265.423, 411783.038). The slope angle between the two vertexes was 46.18° and considered as very steep slope. For Area 2, the slope is marked at the coordinate of (286054.203, 412021.343) to (285963.651, 411913.543). The slope angle between the two vertexes was 77.66° and considered as very steep slope. The slope at Area 3 is marked at the coordinate of (285847.833, 411775.762) to (285887.450, 411251.046). The slope angle between the two vertexes was 47.43° and considered as very steep slope. In conclusion, the use of UAV in geotechnical engineering proven to be very useful for slope mapping and determination of slope hazard based on slope angle.

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

The utility of unmanned aerial vehicles (UAV) imagery in retrieving phenotypic data to support plant breeding research has been a topic of increasing interest in recent years. The advantages of image-based phenotyping are related to the high spatial and temporal resolution of the retrieved data and the non-destructive and rapid method of data acquisition. This study trains parametric and nonparametric regression models to retrieve leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional vegetation cover (fCover), leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and grain yield (GY) of winter durum wheat breeding experiment from four-bands UAV images. A ground dataset, collected during two field campaigns and complemented with data from a previous study, is used for model development. The dataset is split at random into two parts, one for training and one for testing the models. The tested parametric models use the vegetation index formula and parametric functions. The tested nonparametric models are partial least square regression (PLSR), random forest regression (RFR), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The retrieved biophysical variables along with traditional phenotypic traits (plant height, yield, and tillering) are analysed for detection of genetic diversity, proximity, and similarity in the studied genotypes. Analysis of variance (ANOVA), Duncan’s multiple range test, correlation analysis, and principal component analysis (PCA) are performed with the phenotypic traits. The parametric and nonparametric models show close results for GY retrieval, with parametric models indicating slightly higher accuracy (R2 = 0.49; RMSE = 0.58 kg/plot; rRMSE = 6.1%). However, the nonparametric model, GPR, computes per pixel uncertainty estimation, making it more appealing for operational use. Furthermore, our results demonstrate that grain filling was better than flowering phenological stage to predict GY. The nonparametric models show better results for biophysical variables retrieval, with GPR presenting the highest prediction performance. Nonetheless, robust models are found only for LAI (R2 = 0.48; RMSE = 0.64; rRMSE = 13.5%) and LCC (R2 = 0.49; RMSE = 31.57 mg m−2; rRMSE = 6.4%) and therefore these are the only remotely sensed phenotypic traits included in the statistical analysis for preliminary assessment of wheat productivity. The results from ANOVA and PCA illustrate that the retrieved remotely sensed phenotypic traits are a valuable addition to the traditional phenotypic traits for plant breeding studies. We believe that these preliminary results could speed up crop improvement programs; however, stronger interdisciplinary research is still needed, as well as uncertainty estimation of the remotely sensed phenotypic traits.

Research on edge intelligent recognition method oriented to transmission line insulator fault detection

Aiming at the problem of high delay in the existing centralized cloud insulator fault detection methods, an insulator self-explosion fault detection scheme integrating mobile edge computing(MEC) and deep learning is proposed in this paper. In order to run the deep neural network on the edge devices with limited computing resources, the lightweight network MobilieNetv3 is replaced by the backbone network CSPDarknet53 of YOLOv4, which can effectively reduce the network parameters. The model detection speed and generalization ability are further improved by improving the activation function of MobilieNetv3 network and the loss function of YOLOv4. In addition, in order to meet the real-time requirements of insulator fault detection and maximize the utilization of UAV (Unmanned Aerial Vehicle) computing resources, a deep neural network partition algorithm based on binary particle swarm optimization is proposed. The algorithm realizes the division of the optimal partition points of deep neural network under the constraints of UAV energy consumption and system delay. The experimental results show that the edge intelligent recognition method can realize the terminal level recognition of insulator self explosion. The detection accuracy of the proposed object detection algorithm can reach 94.5% and the detection speed is 58.5 frames/s. At the same time, the deep neural network partition algorithm proposed in this paper has a significant effect on reducing UAV energy consumption, system cost and network delay.

Coalitional Formation-Based Group-Buying for UAV-Enabled Data Collection: An Auction Game Approach

Unmanned aerial vehicles (UAVs) enable promising solutions in assisting data collection in wide-area distributed sensor networks, leveraging their advanced properties of high mobility and line-of-sight communication links. However, existing UAV-assisted data collection methods mainly focus on unilaterally maximizing the utility of UAVs or sensors. Unfortunately, the problem driven by the market economy is ignored, namely the game between buyer and seller, in the process of sensors competing for UAV services. To address this problem, we propose a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV data collection services. Then, a parallel variable neighborhood ascent search algorithm is designed to quickly search the approximately optimal group-buying coalition structure. We further propose a novel group-buying coalition auction method, named TRUST, which can ensure the economical properties, i.e., truthfulness, individual rationality, and maximization of social welfare. Numerical results show that the sensors' average age of information (AoI) under the proposed method is reduced by 16.7% and 44.5% compared with the coalition formation game (CFG) and joint trajectory design-task scheduling (TDTS) UAV-to-community methods. To our best knowledge, this is the first effort on truthful coalition formation-based group-buying auction.

Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.