Unmanned Aerial Vehicles Task Research Articles

Toward flexible and persistent UAV service: Multi-period and multi-objective system design with task assignment for disaster management

Usability of lightweight unmanned aerial vehicles (UAVs) limited in a variety of activities in disaster management due to two fundamental drawbacks: limited flight duration, and limited loading capacity. With the proper system design and task assignment methodologies, these limitations can be overcome to enable long-duration UAV missions over large areas. The present study set out to determine the optimal unmanned aerial system (UAS) system design and UAV task assignment for such applications. Based on a proposed multi-objective mathematical model, exact Pareto optimal solutions were derived using the ε–constraint method. To find approximate Pareto solutions, an approximate two-phase (ATP) approach was developed, which is computationally inexpensive compare to the optimal solution approach. Through numerical experiments based on a realistic case study, it was shown that the ATP approach results in high accuracy and a two-orders-of-magnitude reduction in computation time. Additionally, for prompt application of our study to continuously changing situations, a rolling-horizon UAS approach herein is suggested.

MATHEMATICAL MODELING WITH PARAMETER IDENTIFICATION FOR HEXAROTOR SYSTEM: A HAMILTONIAN APPROACH

This paper presents a mathematical modeling with parameters identification of Unmanned Aerial Vehicle (UAV) system or hexarotor system using the Hamiltonian approach. The mathematical model of the hexarotor is derived from the Hamiltonian approach which involved the storage, dissipation, and routing of energy elements from the UAV. This UAV model parameters identification method is proposed as an alternative to the commonly used wind tunnel testing, which is complex and tedious. This Hamiltonian model is made of a fully actuated subsystem with roll, pitch, and yaw angles as output, as well as an under-actuated subsystem with position coordinates as its output. Thrust constant, drag constant and speed of hexarotor are determined through the experimental setup while moment of inertia is determined by physical measurement and calculation. The outcome from this research works demonstrates an undemanding, yet effective method of modeling an UAV, and is useful for designing nonlinear controller to perform the important UAV tasks such as taking off, hovering, and landing.

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation.

Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air–ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV–TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.

Blockchain and UAV-Enabled Signal Source Identification with Edge Computing and Wireless Signal-Aerial Image Fusion

This paper proposes a novel signal source identification system composed of unmanned aerial vehicles (UAVs) and a blockchain, in which the identification method makes full use of binocular camera data and received signal strength. The UAV tasks are organized by using blockchain technology and smart contracts. To tackle the challenge that the transmit power of the object and the channel path loss coefficient are unknown to the UAV, a maximum likelihood estimation method is developed to estimate the parameters in the path loss log-normal shadowing model. Then, the mean squared error is used as the metric to distinguish the signalling object. The simulation results show that the proposed method can effectively complete the task. Also, a mobile edge computing- (MEC-) enabled UAV testbed system is designed and implemented in real environment. The system works accurately where the number of candidate objects is 3.

Group movement of UAVs in environment with dynamic obstacles: a survey

PurposeThe successful application of the group of unmanned aerial vehicles (UAVs) in the tasks of monitoring large areas is becoming a promising direction in modern robotics. This paper aims to study the tasks related to the control of the UAV group while performing a common mission.Design/methodology/approachThis paper discusses the main tasks solved in the process of developing an autonomous UAV group. During the survey, five key tasks of group robotics were investigated, namely, UAV group control, path planning, reconfiguration, task assignment and conflict resolution. Effective methods for solving each problem are presented, and an analysis and comparison of these methods are carried out. Several specifics of various types of UAVs are also described.FindingsThe analysis of a number of modern and effective methods showed that decentralized methods have clear advantages over centralized ones, since decentralized methods effectively perform the assigned mission regardless of on the amount of resources used. As for the method of planning the group movement of UAVs, it is worth choosing methods that combine the algorithms of global and local planning. This combination eliminates the possibility of collisions not only with static and dynamic obstacles, but also with other agents of the group.Originality/valueThe results of scientific research progress in the tasks of UAV group control have been summed up.

Competing Miners: A Synergetic Solution for Combining Blockchain and Edge Computing in Unmanned Aerial Vehicle Networks

Edge computing (EC) is very useful and particularly promising for many practical unmanned aerial vehicle (UAV) applications. Integrating the blockchain to this technology strengthens privacy protection and data integrity and also prevents data from being easily leaked. However, the required operations in the blockchain are computationally heavy because a blockchain requires devices to solve a complicated proof-of-work (PoW) puzzle to add new data (i.e., a block) to the blockchain. Solving a PoW requires substantial amounts of time and energy, which are big concerns for UAVs. In this article, we suggest a synergetic solution to address this issue based on multiple competing miners in a blockchain. Specifically, we present two novel frameworks for combining the blockchain and EC to effectively overcome several critical limitations when applying the blockchain to UAV and EC tasks, respectively. The goal of both of these proposed frameworks is to reduce both the time spent on mining and the energy consumption for the EC. We first look at the fundamentals of the blockchain with competing miners. Then, our proposed frameworks are described with experimental results, through which important insights are drawn. We finally discuss application scenarios for our proposed frameworks, the related technical challenges, and future research directions.

Joint flight scheduling and task allocation for secure data collection in UAV-aided IoTs

With the flexible mobility and deployment, Unmanned Aerial Vehicles (UAVs) have created a new dimension to provide data collection services for a large number of distributed wireless sensory devices. Unfortunately, the broadcast nature of wireless channels make it hard to guarantee the security of data transmission in Internet of Things (IoTs). In this paper, focusing on the scenario where potential eavesdroppers intend to intercept the data uploading of legitimate nodes, we take the advantages of the UAV technology and the physical-layer security approach to study the Secure Data Collection Problem (SDCP). We first explore the flight scheduling and task allocation, that is, determining which areas to be visited and who to be served by UAVs, to establish favorable communication channels. Then we set up the utility function with secrecy rates and non-negative budget constraints. Due to the coupling between the UAV scheduling and task allocation optimization variables in the SDCP, we construct a surrogate function to achieve decoupling and reformulate SDCP into the SDCP-m problem, which is analyzed and proved to be a nonnegative monotone submodular function subject to a general constraint. Finally, an efficient Cost–Benefit Greedy (CBG) algorithm, which is theoretically analyzed to seek the quantitative value of approximation ratio, is proposed. The simulation results show that the CBG algorithm not only outperforms benchmark algorithms in terms of the average secrecy rate and the iteration number, but also generates a nearly optimal trajectory.

Helicopter–UAVs search and rescue task allocation considering UAVs operating environment and performance

The task allocation of unmanned aerial vehicles (UAVs) is important in UAV search and rescue to guarantee an effective and orderly search and rescue activity. However, few studies have investigated the influences of the UAV operating environment and performance on search and rescue task allocation. Considering the influences of low-altitude wind and terrain on UAV energy consumption and performance, UAV release position (from helicopter) selection and task allocation models are proposed in this study. Given that the detection zone of UAVs is affected by topographic factors, the principal component analysis method was used to determine the search and rescue level at each search and rescue point and the UAV hovering endurance, which was determined using clustering analysis. Considering the influence factors of UAV battery energy consumption, such as UAV performance and low-altitude wind, an UAV release position selection model was constructed and solved via the improved binary bat algorithm which exhibited enhanced calculation accuracy comparing with three other algorithms. According to the release position planning result, a multi-objective (minimization of the total search and rescue cost and number of UAVs and multi-UAV task balance) optimization model was established and solved using the non-dominated sorting genetic algorithm-II. Finally, the experimental results indicated that the solution through multiple objectives had no cost advantage comparing with those under the single objective, but exhibited an evident advantage in task completion time through sensitivity analysis of UAVs task allocation problem.

İnsanız Hava Aracı (Drone) ile Erozyon Alanlarının Tespiti: Trakya Yarımadası Örneği

Bu çalışma, Trakya Yarımadası’nda tespit edilen erozyon bölgelerinin alan ve hacimlerinin Coğrafi Bilgi Sistemleri (CBS) kullanılarak hesaplanması amacı ile gerçekleştirilmiştir. Uzaktan algılama ile yapılan ölçümler hem zaman kazanımı hem de fazla işgücünün önüne geçmektedir. Ayrıca drone ile çekim yapılabilecek araziler için satın alınabilecek uydu görüntülerinden daha az maliyetli olmaktadır. Bu amaçla, bölgede doğal karakteristikleri farklılık gösteren 13 erozyon alanı incelenmiş ve insansız hava aracı (drone) ile görüntüler elde edilmiştir. Çalışmada İHA verilerinin kalibrasyonu amacıyla sınırları belli olan ve doğaya zarar vermeyen beyaz boya ile sınırları belirlenmiş ve çeşitli yüksekliklerden bu alanların tüm ölçümleri (uzunluklar, açılar vb.) yapılmıştır. Sınırları belirlenen alanlar daha sonra İHA görüntüleri alınarak CBS yazılımları ve grafik programı “Autodesk Inventor” ile çeşitli proseslerden geçirilerek hacim hesaplaması yapılmıştır. Görüntülerde farklılık olmaması için uygun hava koşulları beklenmiş ve birleştirilecek olan İHA görüntüleri benzer hava şartlarında elde edilmiştir. Farklı hava koşullarında alınan görüntülerin birleştirilmesinde ışık, gölge gibi faktörlerin hesaplamalarda herhangi bir sapmaya neden olmaması ve görsel olarak farklılık oluşturmaması için özen gösterilmiştir. Toplam 13 adet erozyon alanlarından görüntüler alınmış ve çalışmada koordinat sistemi olarak WGS 1984 (World Geodetic System 1984) kullanılmıştır.
 Bu görüntüler birleştirilerek bu alanların ortomozaik görüntüleri oluşturulmuştur. Görüntülerdeki bölgelerin alan ve hacimlerinin yanı sıra kalibrasyonları da yapılmıştır. Yapılan çalışmada, kalibrasyon için küçük bir alanın kullanılması alansal hesaplamada doğruluk oranını %90’ın üzerine çıkarırken hacim ölçümlerinde ise bu oran uçuş yüksekliğine göre %60 – 78 arasında bulunmuştur. 
 Yaklaşık 100 m2 alanda yapılan kalibrasyon çalışmasında 100 metre yükseklikten çekilen görüntüde 12 fotoğraf, 10 metre yükseklikten çekilen görüntüde ise 103 fotoğraf kullanılmıştır. Buna göre, yerden 10 metre yükseklikten çekilen görüntüde hacimsel doğruluk oranı %78,57 bulunmuştur.

A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.

A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs.

Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provide is extremely valuable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by the on-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases, UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the fly path task for UAVs, considering the actual perception needs. A convolutional neural networks (CNNs) model is proposed to detect and localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accurately recognize as many objects as possible with a minimum time cost. The simulation results demonstrate that the proposed method is effective and efficient, and can address the actual scene understanding and path planning problems for UAVs in the real world well.

Group Mobility Model for Complex Multimission Cooperation of UAV Swarm

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.

A Semantic Guidance and Transformer-Based Matching Method for UAVs and Satellite Images for UAV Geo-Localization

It is a challenging task for unmanned aerial vehicles (UAVs) without a positioning system to locate targets by using images. Matching drone and satellite images is one of the key steps in this task. Due to the large angle and scale gap between drone and satellite views, it is very important to extract fine-grained features with strong characterization ability. Most of the published methods are based on the CNN structure, but a lot of information will be lost when using such methods. This is caused by the limitations of the convolution operation (e.g. limited receptive field and downsampling operation). To make up for this shortcoming, a transformer-based network is proposed to extract more contextual information. The network promotes feature alignment through semantic guidance module (SGM). SGM aligns the same semantic parts in the two images by classifying each pixel in the images based on the attention of pixels. In addition, this method can be easily combined with existing methods. The proposed method has been implemented with the newest UAV-based geo-localization dataset. Compared with the existing state-of-the-art (SOTA) method, the proposed method achieves almost 8% improvement in accuracy.

Monocular Depth Estimation With Improved Long-Range Accuracy for UAV Environment Perception

Environment perception by computing the depth is a key task for unmanned aerial vehicle (UAV) type systems. Due to the limited load they can carry, most drones are equipped with a single camera. This prevents general-purpose depth perception methods based either on light detection and ranging (LiDAR) or stereo reconstruction to be effectively used on such platforms. Due to the success of convolutional neural networks (CNNs), monocular depth estimation (MDE) methods have become more and more trustworthy, so their usage on drones is convenient. However, very few such methods have been proposed in the literature, mainly due to the few existing constraints and high diversity that unstructured aerial environments pose. To bridge this gap, we propose a novel approach for MDE, capable to work on aerial images. The method initially proposes an original CNN, particularly adapted to such scenarios. This is done by finding an optimal feature extractor, introducing a new scene understanding module, a new loss and a novel softmax transformation layer that facilitate a better convergence. Furthermore, since both short- and long-range accuracy is required for a robust UAV perception, we introduce a learning-based correction method that redistributes the depth points across the entire depth interval. The proposed CNN gives accurate results, while the additional refinement further improves the accuracy with only a few additional computational resources (around 1–2 ms). We initially show the capabilities of our method on synthetic images captured in unstructured aerial scenarios. Then, we prove that our method can work in real-life situations, computing depth from a single image (at multiple pitch angles) captured by a drone flying in a series of field and forest-like environments. In all these situations, the depth is densely estimated with increased accuracy and reliability.

A Novel Tent-Levy Fireworks Algorithm for the UAV Task Allocation Problem Under Uncertain Environment

Recently, unmanned aerial vehicle (UAV) task allocation is a hot topic both in the civilian and military, while the research of considering uncertainty and multi-objective is still in its infancy. Firstly, based on the uncertainty theory, a mathematical model of the uncertain multi-objective UAV task allocation problem with uncertain variables in both objective function and constraint conditions is established. The expected value criterion and opportunity constraint are introduced to transform the model into a deterministic optimization model. Furthermore, because traditional fireworks algorithm (FWA) has the shortcomings of low solution accuracy and slow convergence speed in solving the UAV task allocation problem, a novel Tent-Levy FWA (TLFWA) based on discrete update process is designed by introducing integer coding, Tent chaotic mapping and Levy variation. Experimental results show that the mean cost calculated by TLFWA is 8.17% and 13.73% lower than that of FWA and particle swarm optimization algorithm respectively, which proves the effectiveness of TLFWA. This study provides a new way to solve multi-objective and uncertain decision-making problems.

Time-optimal of fixed wing UAV aircraft with input and output constraints

<p style='text-indent:20px;'>The route prediction of unmanned aerial vehicles (UAVs) according to their missions is a strategic issue in the aviation field. In some particular missions, the UAV tasks are to start a movement from a defined point to a target reign in the shortest time. This paper proposes a practical method to find the guidance law of the fixed-wing UAV to achieve time-optimal considering the ambient wind. The unique features of this paper are that the environment includes the moving and fixed obstacles as the route constraints, and the fixed-wing UAVs have to keep a given distance from these obstacles. Also, we consider the specific kinematic equation of the fixed-wing UAV and limitations on the flight-path angle and bank-angles as other constraints. We suggest a method for controlling a fixed-wing UAV to get time-optimal using the re-scaling and parameterization techniques. These techniques are useful and effective in maximizing the performance of the gradient-based methods as a sequential quadratic programming method (<inline-formula><tex-math id="M1">\begin{document}$ SQP $\end{document}</tex-math></inline-formula>) for numerical solutions. Then, all constraints of the time-optimal control problem are converted to a constraint using an exact penalty function. Due to being exact, finding the control variables and switching times is more accurate and faster. Finally, some numerical examples are simulated to explore the effectiveness of our proposed study in reality.</p>

Safe navigation for UAV-enabled data dissemination by deep reinforcement learning in unknown environments

Unmanned aerial vehicles (UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAV-enabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV's task completion time while satisfying the data transmission task requirement and the UAV's feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue, we propose a new solution approach by utilizing the most advanced dueling double deep Q network (dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.

Research on UAV Task Assignment Based on Fireworks Algorithm

From military, industry, agriculture to artificial intelligence and other fields, unmanned aerial vehicles (UAVs) have been widely used. Task allocation and path planning are the two most basic technologies in all the application research of UAV. As the core of multi-aircraft tactical deployment and cooperative control, the task allocation scheme largely determines the operational efficiency and execution effect of multi-UAVs. Due to the influence of the task environment and its own resources, the multi-machine cooperative task allocation problem is a multi-objective optimization and decision-making problem limited by many constraints. In recent years, many methods of task assignment and path planning have been put forward, and some progress has been made. According to the overall control of the application scenario, the task planning makes a reasonable task allocation scheme for the UAV to help it complete the task better. Multi-UAV task assignment is a complex multi-constraint multi-objective nonlinear optimization problem. Aiming at the respective defects of common intelligent algorithms, this paper proposes to use fireworks algorithm to deal with this problem.

Dynamic Reallocation Model of Multiple Unmanned Aerial Vehicle Tasks in Emergent Adjustment Scenarios

Due to their strong risk tolerance, low manufacturing cost and good maneuverability, unmanned aerial vehicles (UAVs) have been widely used in various fields. Among related challenges, coordinated task assignment is a key scientific issue for autonomous control of UAVs. In this paper, based on the idea of fuzzy C-means (FCM) clustering and the ant colony optimization (ACO) algorithm, a cooperative multiple task reallocation problem with target precedence constraints for heterogeneous UAVs is proposed. The contributions of this research are the performance evaluation of the original algorithms in a dynamic context, consideration of changes in some attributes of the environment, and the extension of these algorithms to properly address more realistic dynamic emergent adjustment scenarios. According to the corresponding task reallocation strategy, the scenarios are divided into three categories: the complete redistribution strategy can effectively cope with scenarios where tasks have changed significantly, the partial adjustment strategy can induce partial responses to the changes of individual tasks, and group redistribution can effectively solve the problem of task target threat rating changes. The simulation results show that the dynamic reallocation model of multi-UAV tasks in dynamic emergent adjustment scenarios can achieve better performance to complete the corresponding tasks based on the proposed scheme. In addition, we deployed the developed graphical modeling and analysis software (GMAS) platform to implement the dynamic reallocation model of multi-UAV tasks in dynamic emergent scenarios, and the validity and reliability of the proposed task reallocation model were verified.

Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning

Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.