Unmanned Aerial Vehicle Cluster Research Articles

A Task Planning Method for UAV Swarm Dynamic Reconstruction Based on a Fourth-Order Motif

The primary purpose of task allocation is to build each equipment platform and quickly complete integration planning at the actual combat speed to achieve efficient management of the entire task. In this process, higher requirements are put forward for dynamic, cooperative, and highly adaptive drone colony organization. In this paper, the scheduling problem of hybrid unmanned aerial vehicle (UAV) systems is studied under an uncertain environment. First, the system-capability-task organizational structure is defined and quantified, which lays a foundation for dynamic adjustment of the organizational structure in the future. Then, combined with the theory of flexible network and elastic network management, the model is calculated, and the linear transformation function and fuzzy theory are used to stratify and cluster the capability layers. On this basis, four motif structures are introduced for abnormal nodes in the process of dynamic adjustment, and a dynamic group reconstruction algorithm (DRA-M) is established. Finally, the time and communication load indexes are determined, and the alternative strategy is designed for the failure point. The performance of the classical scheduling algorithm is evaluated by benchmarking it under different conditions. The results show that the algorithm has a good dynamic adjustment ability in the event of a UAV swarm emergency, which is a bright light for the future study of highly adaptive UAV cluster organization.

Missing data reconstruction in attitude for quadrotor unmanned aerial vehicle based on deep regression model with different sensor failures

Forming a UAV cluster with multiple UAVs to collaborate in accomplishing tasks is the future development of battlefield. The UAV cluster can collaborate by communicating through inter-aircraft links and can quickly and accurately perform complex tasks such as path planning, collaborative reconnaissance, collaborative sensing and attack. In the process of Unmanned Aerial Vehicle (UAV) cooperative operation, obtaining the real-time attitude information of each UAV is preliminary to implement collaborative cooperation. However, in practice, due to the internal malfunction of sensors and mutual interference among UAVs, the acquired attitudes often suffer from data missing. This paper proposed a novel missing data reconstruction method for UAV’s attitude in the case of sensor failures. The attitude data of UAV is obtained through an advanced UAV simulation platform AirSim. Through fusing the temporal convolutional network (TCN) and Bi-directional long short-term memory (Bi-LSTM), a deep regression framework is established for missing data construction. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models and different combinations of data missing. The performance of our method is quantified using metrics of root-mean-square-error (RMSE), mean absolute error (MAE), determination coefficient (DC), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) score. The averaged performance values of our proposed method outperform other baseline algorithms. The results of the experiment show that the study could well-address the issue of missing data reconstruction and provide reliable attitude data.

UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net

Currently, we are facing an increasing trend in the use of unmanned aerial vehicles (UAV) in various activities, both civilian and military. The application of UAVs in the battlefield has received extensive attention amid a global new military revolution. A UAV cluster is a large and complex real-time feedback system that integrates a communication and sensor network, control system, calculation, and physical process. The heterogeneous UAVs conduct complex behaviors, which requires a comprehensive description and analysis of UAV cluster modeling. The integrated modeling of the heterogeneous UAV cluster is of great significance and value to test and verify the new combat mode. In this paper, we present a novel representation framework based on the Petri nets. We used a spatial-temporal hybrid Petri net to illustrate the discrete state and continuous process of a heterogeneous UAV cluster system, and effectively achieved the fusion of a physical and computational process and interaction event modeling in the cluster system. Finally, the formal verification of UAV cluster attack mission modeling was carried out by UPPAAL, suggesting the proposed modeling method is feasible and effective.

Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

The unmanned aerial vehicle (UAV) self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale, which can quickly and accurately complete complex tasks such as path planning, situational awareness, and information transmission. Due to the openness of the network, the UAV cluster is more vulnerable to passive eavesdropping, active interference, and other attacks, which makes the system face serious security threats. This paper proposes a Blockchain-Based Data Acquisition (BDA) scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario. Each UAV cluster has an aggregate unmanned aerial vehicle (AGV) that can batch-verify the acquisition reports within its administrative domain. After successful verification, AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage. There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports, respectively. The security analysis shows that the BDA construction can protect the privacy and authenticity of acquisition data, and effectively resist a malicious key generation center and the public-key substitution attack. It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem (ECDLP) assumption. The performance analysis demonstrates that compared with other schemes, the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

Connectivity Guarantee Within UAV Cluster: A Graph Coalition Formation Game Approach

Unmanned Aerial Vehicle (UAV) clustering is promising for performing large-scale missions because of high mobility and easy deployment. However, the connectivity problems caused by the high mobility of UAVs and the interferences are mostly ignored in existing UAV mission executions. Rapid restoration of UAV network connectivity is essential for preventing communication disruption and improving the overall network performance. Thus, a graph coalition formation game that integrates the UAV time-varying topology graph with the coalition formation game is proposed to rapidly restore the connectivity of the UAV network in need. The method improves the utility of UAV clustering and ensures the network connectivity. Then, a graph coalition formation game algorithm based on the shortest path tree (SPT-GCF) rapidly forms an approximately optimal coalition structure. The simulation results show that the proposed approach increases the average utility of UAV clustering by 6.5% and 14.5% respectively when compared with the existing non-overlapping coalition formation game (NOCFG) and the coalition formation game without considering the cluster connectivity.

RHM-δ-GLMB Tracking Algorithm on the Focal Plane for UAV Cluster Targets

This paper offers a new tracking algorithm for clustered targets of focal plane unmanned aerial vehicles (UAVs) by integrating the random hypersurface model (RHM) model into the δ-generalized label multi-Bernoulli (δ-GLMB) filter to guarantee a robust UAV cluster target tracking on such focal planes. We first investigated the infrared imaging features of UAV cluster targets by emphasizing the target imaging size and dispersion. Next, we designed an elliptical RHM-based measurement model to map the measurement, state parameters, scaling factors, and errors to pseudo-measurement "0", followed by establishing a pseudo-measurement equation to reflect the target’s extended shape size. Then, in order to enhance measurement estimation accuracy, the RHM model was implemented with the δ-GLMB filter and Gamma-Gaussian mixture, which can perform a real-time estimate of the targets’ centroid motion state and extended state. We also used the grid-based fast density-based spatial clustering of applications with noise (DBSCAN) segmentation algorithm to overcome the distance-based segmentation method restrictions for infrared radiation (IR) measurement data and diminish the algorithm’s complexity even more in the measurement update. Extensive simulations demonstrated that this algorithm outperformed existing matching filtering algorithms in the target centroid motion and extended states. Our investigations also revealed that the algorithm was less vulnerable to clutter and more adaptive, making it more straightforward to accomplish reliable tracking of UAV cluster targets.

Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm

As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objective optimization algorithm has a good performance in solving optimization problems. In this paper, the intelligent planning model of UAV cluster was established based on multi-objective optimization algorithm, and its path is optimized. In the aspect of modeling, this paper studied and analyzed online task planning, search rules and cluster formation control using an agent-based intelligent modeling method. For mission planning and optimization, it combined multi-objective optimization algorithm to build the model from three aspects of mission allocation, route planning and planning evaluation. The final simulation results showed that the UAV cluster intelligent planning modeling method and path optimization method based on multi-objective optimization algorithm met the requirements of route design and improved the path search efficiency with 2.26% task completion satisfaction.

Cooperative Networking Strategy of UAV Cluster for Large-Scale WSNs

To solve the shortcomings of wireless sensor networks (WSNs) in data collection in a wide range, long distance, and other complex environments, such as high networking cost, short network life cycle, and difficult data collection, a data collection strategy based on the unmanned aerial vehicle (UAV) cluster collaborative networking is proposed. First, the node structure and deployment application of WSNs are analyzed, and the architecture of UAV collaborative data collection is designed. Second, the related models of communication energy consumption between WSNs’ network and UAV cluster, UAV cluster task allocation, and path planning are established successively. Then, a simple but efficient task planning strategy considering UAV forward and return distances is proposed. Finally, for the problem of model solving, the solution strategy of the heuristic swarm intelligence algorithm is proposed. The simulation results verify that the strategy reduces the overall network energy consumption by 42.16% and the total range of UAV by 9.7% and realizes the relative minimization of WSNs energy consumption and UAV flight cost on the premise of ensuring the full coverage of WSNs’ nodes.

Threat-Oriented Collaborative Path Planning of Unmanned Reconnaissance Mission for the Target Group

Unmanned aerial vehicle (UAV) cluster combat is a typical example of an intelligent cluster application, and it is characterized by its large scale, low cost, retrievability, and intra-cluster autonomous coordination. An unmanned reconnaissance mission for a target group (URMFTG) is a significant pattern in UAV cluster combat. This paper discusses the collaborative path planning problem of unmanned aerial vehicle formations (UAVFs) and refueling tankers in a URMFTG with threat areas and fuel constraints. The purpose of collaborative path planning is to ensure that the UAVFs (with fuel constraints) can complete the reconnaissance mission for the target group with the assistance of refueling tankers, which is one of the most important constraints in the collaborative path planning. In this paper, a collaborative path-planning model is designed to analyze the relationship between the planning path of the UAVFs and the tankers, and a threat avoidance strategy is designed considering the threat area. This paper proposes a two-stage solution algorithm. It creates a UAVFs path-planning algorithm based on the fast search genetic algorithm (FSGA) and a refueling tanker path-planning algorithm based on the improved non-dominated sorting genetic algorithm II (NSGA-II). Based on simulation experiments, the solution method proposed in this paper can provide a better path-planning scheme for a URMFTG. That is, it decreases the rate of the UAVF’s distance growth from 3.1% to 2.2% for the path planning of UAVFs and provides a better Pareto solution set for the path planning of refueling tankers.

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET.

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.

Investigation on Unmanned Aerial Vehicle (UAV): An Overview

Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular these days. One among the major technological developments of today are UAVs or drones. The coordination and coverage capabilities of large clusters of UAVs, or their cooperative capabilities for such goals as terrain mapping, make them of particular interest. This paper explores the use of unmanned aerial vehicles in smart and modern cities in depth. Future wireless networks will likely include UAVs to facilitate wireless broadcasting and support high-speed transmissions. Various layer techniques are discussed in this paper. Moreover, an overview of the latest UAV communication technologies and network topologies has been presented. Military and commercial applications have attracted a lot of interest in unmanned aerial vehicles. Due to their low cost and flexible deployment, UAVs are considered valuable in 5G and 6G networks due to their communication capabilities. Like aerial base stations, relays, or mobile users in cellular networks, UAVs can provide airborne wireless coverage in a variety of ways. Wireless links can only be established temporarily with UAVs. A great challenge is to extend UAV communication's lifetime and develop low-power, green UAV communication. A comprehensive study of green UAV communications has been presented in this paper. Furthermore, an overview of UAV applications is also illustrated. Additionally, some promising research topics and methods are being discussed.

Research on Distributed Cooperative Intelligent Spectrum Sensing of UAV Cluster

Spectrum sensing is important to improving the survivability of unmanned aerial vehicles (UAVs) in complex electromagnetic environments. At a low signal-to-noise ratio (SNR), a UAV cluster has more prominent advantages in cluster distributed cooperative sensing than a single UAV. Aiming at this urgent need, joint optimal design is carried out for adaptive spectrum sensing algorithm and distributed estimation algorithm to realize the distributed cooperative intelligent spectrum sensing of UAV cluster. In this paper, the adaptive theory is analyzed first, and the performance of the conventional energy-aware sensing method and the least mean square (LMS) spectrum sensing algorithm is compared. In a complex electromagnetic environment, it is proposed to replan the real-time network by deleting erroneous data nodes in order to eliminate parameter estimation deviations caused by data errors. Under the condition of ensuring detection probability, the fast spectrum sensing algorithm based on SNR estimation is optimized by adaptively selecting and setting the SNR threshold to solve the problem of complex and slow calculation. The superiority of distributed spectrum estimation algorithm without erroneous data nodes is verified at a low SNR, showing that the algorithm has a good steady-state error curve and avoids the impact of data errors on detection results. In addition, the effectiveness of optimizing the fast spectrum sensing algorithm by selecting and setting the SNR threshold is verified to improve the distributed cooperative intelligent spectrum sensing rate of UAV cluster.

Configuration Transformation Planning Method of Unmanned Aerial Vehicle Cluster Based on Improved Hungarian Algorithm

For the planning problem of Unmanned Aerial Vehicle (UAV) formation configuration transformation, a formation configuration transformation planning method based on the improved Hungarian algorithm is proposed. By selecting a straight-line path as the movement path of UAVs in configuration transformation, the configuration transformation problem is transformed into a multi-UAV position assignment problem with constraint handling in a dynamic environment. For the Hungarian algorithm efficiency matrix power function treatment, the improved algorithm can be simultaneously bivariate integrated optimization; Introduce the time axis in the configuration space to establish the configuration-time space, and analyze the collision constraints during the dynamic transformation process in this space, and finally, the feasibility of the method is verified by simulation.

Distributed optimization of heterogeneous UAV cluster PID controller based on machine learning

To resolve the problem of membership value and the control effect reliant on expert knowledge, a PID control approach based on an adaptive fuzzy PID-UAV attitude controller is proposed. The experimental findings demonstrate that the Unmanned Aerial Vehicle (UAV) is stable at the desired height of 2 m within 6 s after takeoff, and that altitude control can be accomplished with a single PID controller. It has been established that the control system has the advantages of high precision and ease of implementation, but the fuzzy controller has the advantages of minimal overshoot and transient response, as well as the ability to achieve precise and rapid behaviour control. When obtaining continuous square wave instructions, fuzzy PID has more obvious advantages than regular PID. The system has superior tracking and antiinterference, can achieve stable control faster, and has great dynamic and static properties.

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

Large Unmanned Aerial Vehicle (UAV) clusters, containing hundreds of UAVs, have widely been used in the modern world. Therein, mission planning is the core of large UAV cluster collaborative systems. In this paper, we propose a mission planning method by introducing the Simple Attention Model (SAM) into Dynamic Information Reinforcement Learning (DIRL), named DIRL-SAM. To reduce the computational complexity of the original attention model, we derive the SAM with a lightweight interactive model to rapidly extract high-dimensional features of the cluster information. In DIRL, dynamic training conditions are considered to simulate different mission environments. Meanwhile, the data expansion in DIRL guarantees the convergence of the model in these dynamic environments, which improves the robustness of the algorithm. Finally, the simulation experiment results show that the proposed method can adaptively provide feasible mission planning schemes with second-level solution speed and that it exhibits excellent generalization performance in large-scale cluster planning problems.

Resource Allocation Computing Algorithm for UAV Dynamical Statements based on AI Technology

An unmanned aerial vehicle (UAV) is one of the complex and relevant communication networks of 5G and 2030 networks. Development of technologies for virtualization (NFV), containerization and orchestration of data systems. NFV technology can be implemented not only in the data center but also on the switch or router. Thus, by analogy with the above-described trend, flying network segments can also use computing power to solve any problems. For example, deployment on virtual distributed capacities of a flying station controller, an internal network controller. Within the framework of this direction, there are a number of interrelated tasks that need to be resolved by the trends and capabilities of Artificial Intelligence technologies. This paper proposes an algorithm for searching for computing resources in real time. The article defined the criteria for choosing the head node and the cluster with the highest total resources, considered the possibility of implementing the function of the SDN controller in the UAV cluster, the main possible functions and tasks of the UAV, proposed a three-level architecture based on the separation of the functions performed by the UAV. In this work, simulation was carried out in the Matlab program to detect areas of increased load, form UAV clusters, select a head node in clusters and select one UAV cluster with the highest total resources for its subsequent migration to the area of increased load.

Autonomous and cooperative control of UAV cluster with multi-agent reinforcement learning

AbstractIn this paper, we expolore Multi-Agent Reinforcement Learning (MARL) methods for unmanned aerial vehicle (UAV) cluster. Considering that the current UAV cluster is still in the program control stage, the fully autonomous and intelligent cooperative combat has not been realised. In order to realise the autonomous planning of the UAV cluster according to the changing environment and cooperate with each other to complete the combat goal, we propose a new MARL framework. It adopts the policy of centralised training with decentralised execution, and uses Actor-Critic network to select the execution action and then to make the corresponding evaluation. The new algorithm makes three key improvements on the basis of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The first is to improve learning framework; it makes the calculated Q value more accurate. The second is to add collision avoidance setting, which can increase the operational safety factor. And the third is to adjust reward mechanism; it can effectively improve the cluster’s cooperative ability. Then the improved MADDPG algorithm is tested by performing two conventional combat missions. The simulation results show that the learning efficiency is obviously improved, and the operational safety factor is further increased compared with the previous algorithm.

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.

Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

The evolving “Industry 4.0” domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs. Federated learning (FL) becomes a recent advancement of machine learning (ML) which aims to protect user data. In this aspect, this study designs federated learning with blockchain assisted image classification model for clustered UAV networks (FLBIC-CUAV) on IIoT environment. The proposed FLBIC-CUAV technique involves three major processes namely clustering, blockchain enabled secure communication and FL based image classification. For UAV cluster construction process, beetle swarm optimization (BSO) algorithm with three input parameters is designed to cluster the UAVs for effective communication. In addition, blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers. Finally, the cloud server uses an FL with Residual Network model to carry out the image classification process. A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach. The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

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.