Operation Of Unmanned Aerial Vehicles Research Articles

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs.

МЕТОД СТВОРЕННЯ НАВЧАЛЬНОГО БПЛА ЛІТАКОВОГО ТИПУ ЗА ДОПОМОГОЮ 3D-ДРУКУ

he concept of specialized training unmanned aerial vehicle (UAV) of aircraft type for operators of unmanned aerial vehicles is proposed. Training opportunities in various areas of operator training are highlighted. A method of manufacturing an aircraft-type training UAV taking into account aerodynamic and weight characteristics has been developed. The developed method is based on the production of aircraft parts using a 3D printer, which is modified to meet the requirements of small aircraft type aircraft. This technique also includes the process of selecting the most advantageous combination of materials to create a small aircraft. Tables with initial data of existing UAVs are given.The flight characteristics, aerodynamic schemes and type of engine that are most rationally suitable for unmanned aerial vehicles in accordance with this class are proposed. Based on these data, a prototype model was built, the method of UAV design was adopted, taking into account the choice of parameters of the power plant. A demonstration example of the choice of the main parameters of the training taking into account the UAV under specific requirements is given. The obtained main technical characteristics of the UAV are presented. Based on the calculated data, a three-dimensional geometric model of the UAV was developed and a flight sample was made. A series of test flights of the flight model was conducted, tested under difficult weather conditions, when the wind force is about 12 m / s. The ways of using the described method of developing UAV training and research, as well as many other factors that affect 3D printing without detecting defects and problems are considered.

(Retracted) Inspection of unmanned aerial vehicles in oil and gas industry: critical analysis of platforms, sensors, networking architecture, and path planning

The rapid growth of unmanned aerial vehicles (UAVs) has huge potential in the oil and gas industry (OGI). These are especially helpful in situations in which human lives are at risk. OGI pipelines are threatened by natural and man-made disasters, which are harmful to both people and assets. Usually, oil and gas pipelines pass through extreme environments for which standard inspection, maintenance, and repairing approaches such as rope access, scaffolds, telescopic elevation platforms supported by cranes, and manned helicopters are not secure and difficult and expensive to implement. However, technological advancements like device miniaturization have boosted the performance of UAVs, offering cost-effective, efficient, and high mission flexibility. UAVs are capable of carrying sensors and cameras to perform monitoring. As pipelines span thousands of kilometers, multi-UAV systems, commonly referred to as flying ad-hoc networks (FANETs), can collaboratively complete monitoring missions more effectively and economically as compared with single UAV systems. Moreover, many issues must be resolved before the effective use of UAVs can provide stable and reliable context-specific networks. Several OGI-specific issues of UAVs such as architecture design, platform, sensors, networking architectures, and path planning models for different OGI pipeline surveillance scenarios must be resolved to use FANETs effectively for robust and sustainable networks. The prime objective of this research is a state-of-the-art review of UAVs in OGI. We first present OGI midstream challenges and parameters to give a brief overview of the challenges faced by the OGI for pipeline surveillance. Then we discuss OGI-specific scenarios for sensor readings, visual leak detection, and detection of any unusual activity that happens in the pipeline while monitoring through UAVs. We also present different OGI-specific UAV platforms, UAV networking architectures, and path planning models of FANET for efficient communication and collaboration. Finally, the challenges of UAVs and future research prospects in OGI-specific UAVs are highlighted.

RISE: Rolling-Inspired Scheduling for Emergency Tasks by Heterogeneous UAVs

The multiple unmanned aerial vehicles (UAVs) system is highly sought after in the fields of emergency rescue and intelligent transportation because of its strong perception and extensive coverage. Formulating a reasonable task scheduling scheme is essential to raising the task execution efficiency of the system. However, the dynamics of task arrival and the heterogeneity of UAV performance make it more difficult for multiple UAVs to complete the tasks. To address these issues, this paper focuses on the multi-UAV scheduling problem and proposes a method of rolling-inspired scheduling for emergency tasks by heterogeneous UAVs (RISE). In order to ensure that emergency tasks can be allocated to UAVs in a real-time manner, a task grouping strategy based on a density peaks (DP) clustering algorithm is designed, which can quickly select UAVs with matching performance for the tasks arriving at the system. Furthermore, an optimization model with multiple constraints is constructed, which takes the task profit and UAV flight cost as the objective function. Next, we devise a rolling-based optimization mechanism to ensure that the tasks with shorter deadlines are executed first while maximizing the objective function, so as to obtain the optimal task execution order for each UAV. We conduct several groups of simulation experiments, and extensive experimental results illustrate that the number of tasks successfully scheduled and the utilization rate of UAVs by RISE are superior to other comparison methods, and it also has the fastest running time. It further proves that RISE has the capability to improve the completion rate of emergency tasks and reduce the flight cost of multiple UAVs.

Pollination Parameter Optimization and Field Verification of UAV-Based Pollination of ‘Kuerle Xiangli’

In this study, we investigated unmanned aerial vehicle (UAV) pollination of ‘Kuerle Xiangli’, and screened the pollination operation parameters to determine the precise parameters needed for the implementation of a ‘Kuerle Xiangli’ UAV pollination operation. Different flight height gradients, nozzle atomization particle sizes, spraying volume, and flight routes were tested and their effects on the droplets deposited were compared. UAV operation parameters were screened and field operations were conducted, comparing the fruit set rate, cost, and efficiency of different pollination methods of ‘Kuerle Xiangli’. The results show that the mist droplet effect of 1 m above the top of the tree is higher compared with that of 2 m and 3 m. The mist droplet effect of 2 L/667 m2 is better compared with that of 1.5 L/667 m2 and 1 L/667 m2. The mist droplet effect of 120 μm nozzle atomization particle size is better than that of 110 μm, 135 μm, and 150 μm. The mist droplet effect of flying above the canopy is better than that of flying between the rows of the canopy. The inflorescence and flower fruiting rates of ‘Kuerle Xiangli’ are 63.27% and 28.84%, respectively, and the inflorescence fruiting rate is not significantly different from hand and liquid sprayer pollination. The UAV pollination saves 12.69 USD/667 m2 and 3.32 USD/667 m2 compared with hand and liquid spray pollination, respectively. The efficiency of UAV pollination is greater than that of liquid and hand pollination. The best combination of parameters for pollination using a quadrotor UAV is 1 m from the top of the tree, 2 L/667 m2 spray volume, 120 μm spray nozzle particle size, and the flight path above the canopy. The cost of UAV pollination is 11.83 USD/667 m2 and the pollination efficiency 2.67 hm2/unit·h.

Methodology for Determining the Probability of Damage to UAV as a Result of Exposure to Atmospheric Environmental Factors

Introduction. For the effective use of unmanned aerial vehicles (UAV), it is required to take into account the impact of atmospheric environmental factors. Based on the existing level of development of the research and methodological apparatus, it is impossible to determine the probability of damage to the UAV under the conditions of complex exposure to atmospheric environmental factors and to assess the feasibility of further performance of the flight task. Violation of the UAV operation process is caused by the influence of atmospheric precipitation, wind and temperature conditions of the environment. The work aims at the development of a methodology for determining the probability of damage to UAV as a result of exposure to atmospheric environmental factors, as well as to evaluate the performance of the software package implementing the developed algorithm using triangular, trapezoidal, pentagonal, and Gaussian membership functions.Materials and Methods. A technique is proposed that makes it possible, using the theory of fuzzy logic, to determine the probability of damage to the UAV with inaccuracies and uncertainties in the description of atmospheric effects of the environment. It takes into account possible atmospheric forcing and enables to determine the probability of damage to the UAV under various atmospheric influences. The computational complexity of the algorithm implementing the technique depends significantly on the number of qualitative assessments of atmospheric impacts on UAV.Results. A method for determining the probability of damage to UAV as a result of exposure to atmospheric environmental factors based on fuzzy sets (triangular, trapezoidal, pentagonal, Gaussian) was proposed and tested. The use of fuzzy sets for estimating the conditions of the UAV application environment was described. An algorithm was developed to determine the probability of damage to the UAV as a result of exposure to atmospheric environmental factors. A computational experiment was carried out to determine the complexity of calculating the probability of UAV damage as a result of atmospheric forcing under various environmental conditions: «moderate conditions» at a wind speed of 3 m/s, precipitation intensity of 0.8 mm/h, and air temperature of 5 °C; «very heavy conditions» of the external environment at a wind speed of 12 m/s, precipitation intensity 3.5 mm/h, and air temperature of –6 ºС. It was established that the use of triangular membership functions to calculate the probability of damage to the UAV as a result of atmospheric forcing provided higher performance compared to the rest ones (trapezoidal, pentagonal, Gaussian).Discussion and Conclusions. The values of the UAV damage probabilities obtained during the implementation of the methodology under atmospheric environmental influences can be used at the pre-flight preparation stage and during the flight to assess the feasibility of further performance of the flight task. Based on the analysis of the application of the considered forms of membership functions, recommendations for their application are given. The use of algorithms with triangular membership functions will provide high performance of UAV control systems (UAV CS).

Mobile jammer enabled secure UAV communication with short packet transmission

This paper studies a mobile jammer enabled secure unmanned aerial vehicle (UAV) communication network in the finite packet length scenario, where the external friendly jammer UAV will transmit jamming signal to deteriorate the quality of eavesdropping channel, and help to improve the secure communication performance of the source UAV. Specifically, an average effective secrecy rate (AESR) maximization problem is formulated by jointly designing the trajectories and transmit power of the source/jammer UAV subject to the UAVs’ mobility constraints and power constraints. It is also verified that there exists a unique optimal packet error rate to maximize the AESR. The formulated problem is mathematically intractable, as it is nonconvex and involves coupled optimization variables. Therefore, we first decompose the trajectories and power optimization problem into four non-convex subproblems, and then transform them into standard convex problems by applying first-order Taylor expansion. Then, an efficient alternating optimization algorithm for AESR problem based on Bisection method and successive convex approximation (SCA) technology is presented by addressing the five subproblems iteratively. Simulation results are provided to verify that our proposed scheme can obtain considerable better AESR gain compared to other four benchmark schemes.

A Lightweight Aerial Power Line Segmentation Algorithm Based on Attention Mechanism

Power line segmentation is very important to ensure the safe and stable operation of unmanned aerial vehicles in intelligent power line inspection. Although the power line segmentation algorithm based on deep learning has made some progress, it is still quite difficult to achieve accurate power line segmentation due to the complex and changeable background of aerial power line images and the small power line targets, and the existing segmentation models is too large and not suitable for edge deployment. This paper proposes a lightweight power line segmentation algorithm—G-UNets. The algorithm uses the improved U-Net of Lei Yang et al. (2022) as the basic network (Y-UNet). The encoder part combines traditional convolution with Ghost bottleneck to extract features and adopts a multi-scale input fusion strategy to reduce information loss. While ensuring the segmentation accuracy, the amount of Y-UNet parameters is significantly reduced; Shuffle Attention (SA) with fewer parameters is introduced in the decoding stage to improve the model segmentation accuracy; at the same time, in order to further alleviate the impact of the imbalanced distribution of positive and negative samples on the segmentation accuracy, a weighted hybrid loss function fused with Focal loss and Dice loss is constructed. The experimental results show that the number of parameters of the G-UNets algorithm is only about 26.55% of that of Y-UNet, and the F1-Score and IoU values both surpass those of Y-UNet, reaching 89.24% and 82.98%, respectively. G-UNets can greatly reduce the number of network parameters while ensuring the accuracy of the model, providing an effective way for the power line segmentation algorithm to be applied to resource-constrained edge devices such as drones.

Season-Invariant GNSS-Denied Visual Localization for UAVs

Localization without Global Navigation Satellite Systems (GNSS) is a critical functionality in autonomous operations of unmanned aerial vehicles (UAVs). Vision-based localization on a known map can be an effective solution, but it is burdened by two main problems: places have different appearance depending on weather and season, and the perspective discrepancy between the UAV camera image and the map make matching hard. In this work, we propose a localization solution relying on matching of UAV camera images to georeferenced orthophotos with a trained convolutional neural network model that is invariant to significant seasonal appearance difference (winter-summer) between the camera image and map. We compare the convergence speed and localization accuracy of our solution to six reference methods. The results show major improvements with respect to reference methods, especially under high seasonal variation. We finally demonstrate the ability of the method to successfully localize a real UAV, showing that the proposed method is robust to perspective changes.

Comparative Manufacturing of Hybrid Composites with Waste Graphite Fillers for UAVs.

Materials of Unmanned Aerial Vehicles (UAVs) parts require specific techniques and processes to provide high standard quality, sufficiently strong, and lightweight materials. Composite materials with a proper technique have been considered to improve the performance of UAVs. Usually, the hybrid composite is developed by mechanical properties with the addition of the filler component (i.e., particle) in a matrix. This research work aims to develop the effective composite materials with better mechanical properties. Considering the manufacturing of hybrid composite materials, the vacuum process is an affecting factor on mechanical properties. The comparison of the hand lay-up process (HL) and vacuum infusion process (VI) with controlled pressure and temperature are studied in this research. In addition, graphite fillers (i.e., 5 wt%, 7.5 wt%, 10 wt%, and 12.5 wt%) are added to the studied matrix. Obviously, the ply orientation is one of the factors that affects mechanical properties. Moreover, two types of ply orientation (i.e., [0°/90°]4s and [−45°/45°]4s) are comprehensively investigated to improve mechanical properties in the three-point bending test. The experimental results show that the vacuum infusion process of ply orientation [0°/90°]4s with the addition of 10 wt% graphite filler exhibits remarkable flexural strength from 404 MPa (without filler) to 529 MPa (10 wt% filler). Especially, the ply orientation of [0°/90°]4s has higher flexural strength than [−45°/45°]4s in both processes. Considering the failure, the fracture of the specimen propagates along the trajectory of fiber fabric orientation, leading to the breakage. Subsequently, the flexural strength under the vacuum infusion process is more significant than in the hand lay-up process. Effectively, it is found that the hybrid composite in this manufacturing has a higher strength-to-weight ratio to use in the structure of UAV instead of pure aluminum. It should be noted that the proposed hybrid composite strategy used in this study is not only limited to the UAV parts. The contribution can be extended to use in other applications such as automotive, structural building, and so on.

A Performance Evaluation and Analysis Method for Multi-Rotor UAV under Actuator LOE

Loss of Actuator Effectiveness (LOE) reduces the performance parameters of Multi-rotor Unmanned Aerial Vehicle (UAV), which affects the reliability of the mission. However, in practice, researchers used to evaluate the performance of a UAV through lots of flight experiments or by experience, which is normally inefficient and costly. In order to evaluate the performance of UAVs, this paper presents a performance evaluation and analysis method under actuator LOE. First, models are established for the components of a propulsion system, mainly including actuators and batteries. Second, the performance evaluation problem is simplified and decomposed into two sub-problems. performance indices are established by kinetic analysis and then solved, considering the influence of actuator LOE and battery performance. Finally, based on the analysis results, the evaluation method is demonstrated in several cases. The results showed that the evaluation method is effective compared with the simulation results of www.flyeval.com/. In addition, LOE reduces the performance parameters of the UAV, which has a greater impact on the maximum flight speed and maximum cruising distance.

UAV safe route planning based on PSO-BAS algorithm

In order to solve the current situation that unmanned aerial vehicles (UAVs) ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace, a UAV route planning method that considers regional risk assessment is proposed. Firstly, the low-altitude airspace is discretized based on rasterization, and then the UAV operating characteristics and environmental characteristics are combined to quantify the risk value in the low-altitude airspace to obtain a 3D risk map. The path risk value is taken as the cost, the particle swarm optimization-beetle antennae search (PSO-BAS) algorithm is used to plan the spatial 3D route, and it effectively reduces the generated path redun dancy. Finally, cubic B-spline curve is used to smooth the plan ned discrete path. A flyable path with continuous curvature and pitch angle is generated. The simulation results show that the generated path can exchange for a path with a lower risk value at a lower path cost. At the same time, the path redundancy is low, and the curvature and pitch angle continuously change. It is a flyable path that meets the UAV performance constraints.

Droplet Deposition Distribution Prediction Method for a Six-Rotor Plant Protection UAV Based on Inverse Distance Weighting.

The aim of this work is to establish a method for real-time calculating droplet deposition distribution of a six-rotor plant protection unmanned aerial vehicle (UAV). The numerical simulation of the airflow field was carried out using computational fluid dynamics (CFD). The airflow field distribution was obtained under seven flight speeds, six flight heights, and seven crosswind speeds. The relative error verified the accuracy of the numerical model within 12% between the spatial point wind speed test and the simulated value. The numerical simulation results showed that with the improvement of the UAV flight speed and the crosswind, the relative airflow produces a vortex in the downwash wind field below the UAV and reduces the stability of the downwash wind field. The discrete droplet phase was introduced in the flow field. The ground regions were divided using a small grid of 0.5 m × 0.5 m, and statistical calculations of droplet deposition rates within each grid yielded the distribution of droplets under 294 different parameter combinations. The statistical results show that the relative airflow and crosswind caused droplet convolution, and droplet drift was increased. In the actual operation of the UAV, the flight speed should be well controlled under the condition of low environmental wind to reduce the droplet drift rate and improve the utilization rate of pesticides. Based on the distribution under 294 different parameter combinations, one droplet deposition prediction method was established using inverse distance weighting (IDW). The proposed method lays a foundation for the cumulative calculation of droplet deposition distribution during continuous operation of plant protection UAV. It provides a basis for objectively evaluating the operational quality of plant protection UAVs and optimizing the setting of operation parameters.

A Multi-UCAV Cooperative Decision-Making Method Based on an MAPPO Algorithm for Beyond-Visual-Range Air Combat

To solve the problems of autonomous decision making and the cooperative operation of multiple unmanned combat aerial vehicles (UCAVs) in beyond-visual-range air combat, this paper proposes an air combat decision-making method that is based on a multi-agent proximal policy optimization (MAPPO) algorithm. Firstly, the model of the unmanned combat aircraft is established on the simulation platform, and the corresponding maneuver library is designed. In order to simulate the real beyond-visual-range air combat, the missile attack area model is established, and the probability of damage occurring is given according to both the enemy and us. Secondly, to overcome the sparse return problem of traditional reinforcement learning, according to the angle, speed, altitude, distance of the unmanned combat aircraft, and the damage of the missile attack area, this paper designs a comprehensive reward function. Finally, the idea of centralized training and distributed implementation is adopted to improve the decision-making ability of the unmanned combat aircraft and improve the training efficiency of the algorithm. The simulation results show that this algorithm can carry out a multi-aircraft air combat confrontation drill, form new tactical decisions in the drill process, and provide new ideas for multi-UCAV air combat.

Simultaneous Obstacles Avoidance and Robust Autonomous Landing of a UAV on a Moving Vehicle

For unmanned aerial vehicles (UAVs), landing on a moving vehicle robustly is an open challenge, especially under cluttered surroundings with the presence of unknown obstacles. Those undesired environmental factors could induce collisions and thus affect flight safety significantly. Currently, there are few solutions to address such a challenge. In this paper, we propose a systematic autonomous landing scheme that enables the robust autonomous landing performance of a quadrotor UAV. The proposed scheme integrates target detection, state estimation, trajectory planning, and landing control. The position and attitude information of the target ground vehicle and the test quadrotor are estimated by the onboard vision system and GPS. In order to detect landing markers at different altitudes, a particular landing pad with an Apriltag bundle is implemented. As a typical aerial–terrestrial cooperation system, the trajectory planner of the quadrotor updates continuously to avoid obstacles via real-time sensing and re-planning. A finite state machine is used to label the current flight status and triggers the control laws correspondingly. The effectiveness of the proposed method has been validated in a high-fidelity simulator with environmental obstacles.

Power Tower Inspection Simultaneous Localization and Mapping: A Monocular Semantic Positioning Approach for UAV Transmission Tower Inspection.

Realizing autonomous unmanned aerial vehicle (UAV) inspection is of great significance for power line maintenance. This paper introduces a scheme of using the structure of a tower to realize visual geographical positioning of UAV for tower inspection and presents a monocular semantic simultaneous localization and mapping (SLAM) framework termed PTI-SLAM (power tower inspection SLAM) to cope with the challenge of a tower inspection scene. The proposed scheme utilizes prior knowledge of tower component geolocation and regards geographical positioning as the estimation of transformation between SLAM and the geographic coordinates. To accomplish the robust positioning and semi-dense semantic mapping with limited computing power, PTI-SLAM combines the feature-based SLAM method with a fusion-based direct method and conveys a loosely coupled architecture of a semantic task and a SLAM task. The fusion-based direct method is specially designed to overcome the fragility of the direct method against adverse conditions concerning the inspection scene. Experiment results show that PTI-SLAM inherits the robustness advantage of the feature-based method and the semi-dense mapping ability of the direct method and achieves decimeter-level real-time positioning in the airborne system. The experiment concerning geographical positioning indicates more competitive accuracy compared to the previous visual approach and artificial UAV operating, demonstrating the potential of PTI-SLAM.

Decentralized Sampled-Data Fuzzy Tracking Control for a Quadrotor UAV with Communication Delay

This study deals with the decentralized sampled-data fuzzy tracking control of a quadrotor unmanned aerial vehicle (UAV) considering the communication delay of the feedback signal. A decentralized Takagi–Sugeno (T–S) fuzzy approach is adopted to represent the quadrotor UAV as two subsystems: the position control system and the attitude control system. Unlike most previous studies, a novel decentralized controller considering the communication delay for the position control system is proposed. In addition, to minimize the increase in computational complexity, the Lyapunov–Krasovskii functional (LKF) is configured as the only state required for each subsystem. The design conditions guaranteeing the tracking performance of the quadrotor UAV are derived as linear matrix inequalities (LMIs) that are numerically solved. Lastly, the validity of the proposed design method is verified by comparing the results through simulation examples with and without communication delay.

Finite time fault tolerant tracking control for multiple unmanned aerial vehicles with actuator faults

SummaryAn adaptive fast nonsingular integral terminal sliding mode control scheme is proposed for the distributed formation control problem of multiple unmanned aerial vehicles (UAVs) affected by actuator faults and external disturbances. In order to achieve a good cooperative tracking performance of UAVs during mission execution, the adverse effect of the lumped disturbances composed of actuator failures, wind and vortex disturbances is estimated by the cerebellar model articulation neural network, meanwhile the tangent function is introduced to eliminate the chattering of the sliding mode. Then the analysis of superior convergence performance of the fast nonsingular integral terminal sliding manifold is implemented, and the finite time stability of the closed‐loop formation flight control system is proved. Finally, the simulation experiments verify that the proposed approach is superior to the nonsingular integral terminal sliding mode control for the formation control system of faulty multiple UAVs.

Learning to Optimise a Swarm of UAVs

The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density.

MALE UAV selection in interval Type-2 fuzzy sets environment

Unmanned Aerial Vehicles (UAVs) are increasingly used in the military field. Especially in recent years, UAVs have been a very effective instrument in gaining airspace superiority and military success. Many countries compete with each other to develop better UAV technology or improve the technical features of UAVs. Therefore, it is critical to determine which UAV has the best performance, considering technical and operational characteristics, because the vehicles with more advanced performance can provide countries with strategic superiority. The purpose of this study is to investigate the technical, cost, and operational performance of Medium Altitude Long Endurance UAVs (MALE UAVs). In the study, as a result of a wide literature review, we determined a performance criterion for this type of vehicle. The model presented here uses an Interval Type-2 Fuzzy Analytical Hierarch Process (IT2FAHP) and an Interval Type-2 Fuzzy Technique for Order of Preference by Similarity to an Ideal Solution (IT2FTOPSIS) hybrid method. The findings indicate that some MALE UAVs have superior technical and operational performance over others and demonstrate that range, max take-off weight, and payload are important criteria in determining the performance and superiority of these vehicles.