Multiple UAVs Research Articles

Research on Passive Location Strategy of UAV

Aiming at the problem of how to reasonably correct the position of UAV formation, we take the circular formation composed of 10 UAVs and the conical formation composed of 15 UAVs in the same plane as examples to study how to adjust the number and angle of UAV signal transmission so that the UAVs in the entire formation are in the designated position. This paper uses geometric model, greedy strategy and other methods, uses python, matlab and other software to obtain a reasonable and effective mathematical model, and makes a test and evaluation. The research results show that no matter circular, conical or other graphics, we can construct a triangle by connecting the transmitting signal and the receiving signal UAV, and solve the problem to obtain the best transmitting signal angle and the minimum number of sorties required; If there is no triangle between the UAV transmitting the signal and each UAV receiving the signal, the UAV receiving the signal will not reach the designated position. Nowadays, the use of UAVs is more and more extensive. The cooperation of multiple UAVs can complete many functions such as detection and performance, which is also the significance of our research on how UAVs can better receive signals.

Global-and-Local Attention-Based Reinforcement Learning for Cooperative Behaviour Control of Multiple UAVs

Global-and-Local Attention-Based Reinforcement Learning for Cooperative Behaviour Control of Multiple UAVs

Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

AbstractUnmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.

Distributed Formation Control for Multiple Quadrotor UAVs Based on Distributed Estimator and Singular Perturbation System

Distributed Formation Control for Multiple Quadrotor UAVs Based on Distributed Estimator and Singular Perturbation System

Cooperative Decision-Making for Multiple UAVs Autonomous Confrontation

This paper presents a rule-based framework for addressing decision-making problems within the context of the “UI-STRIVE” Competition. First, two distinct autonomous confrontation scenarios are described: autonomous air combat and cooperative interception. Second, a State-Event-Condition-Action (SECA) decision-making framework is developed, which integrates the finite state machine and event-condition-action frameworks. This framework provides three products to describe rules, i.e. the SECA model, the SECA state chart, and the SECA rule description. Third, the situation assessment and target assignment during autonomous air combat are investigated, and the mathematical models are established. Finally, the decision-making model’s rationality and feasibility are verified through data simulation and analysis.

Offline and online task allocation algorithms for multiple UAVs in wireless sensor networks

In recent years, UAV techniques are developing very fast, and UAVs are becoming more and more popular in both civilian and military fields. An important application of UAVs is rescue and disaster relief. In post-earthquake evaluation scenes where it is difficult or dangerous for human to reach, UAVs and sensors can form a wireless sensor network and collect environmental information. In such application scenarios, task allocation algorithms are important for UAVs to collect data efficiently. This paper firstly proposes an improved immune multi-agent algorithm for the offline task allocation stage. The proposed algorithm provides higher accuracy and convergence performance by improving the optimization operation. Then, this paper proposes an improved adaptive discrete cuckoo algorithm for the online task reallocation stage. By introducing adaptive step size transformation and appropriate local optimization operator, the speed of convergence is accelerated, making it suitable for real-time online task reallocation. Simulation results have proved the effectiveness of the proposed task allocation algorithms.

Beampattern analysis of cooperative beamforming with carrier frequency offsets in three-dimensional wireless networks

Cooperative beamforming (CB), where spatially distributed nodes synchronize their transmit phases to maximize the power of their combined signal at a desired receiver, can be used as an effective solution for hardware-limited nodes to achieve communication range extension. Thus, CB demonstrates effectiveness in providing higher reliability, lower latency, and extended transmission range for nodes in non-terrestrial networks (NTNs), which inherently face power and hardware limitations. In this paper, we consider the CB technique using UAVs in three-dimensional (3D) networks and analyze the average beampattern of the virtual antenna array constructed by the multiple UAVs. Further, because the mobile nature of the UAVs may cause carrier frequency offsets (CFOs), we analyze the impact of the CFO using the non-parametric kernel method. The simulation and analytical results show that the peak average beampattern degrades by about 3 dB with the CFO standard deviation of 1 kHz, which emphasizes the significance of frequency synchronization.

Path-Following Formation Coordination for Multiple UAVs Considering Communication Constraints

Path-Following Formation Coordination for Multiple UAVs Considering Communication Constraints

Robust hierarchical geometry control for the multiple UAVs aerial transportation system with a suspended payload

Robust hierarchical geometry control for the multiple UAVs aerial transportation system with a suspended payload

Prescribed-time event-triggered fault-tolerant formation control of multiple UAVs under tracking error constraints

This paper studies an event-triggered fault-tolerant tracking control strategy design of multiple quadrotor UAVs (MQUAVs) formation subject to the composite influence of external disturbance, tracking error constraints and actuator faults. A prescribed time prescribed performance function is employed to solve tracking error constraints and it ensures the tracking error enters into the constraint boundary in the prescribed time. A first-order filter is designed to solve the problem of computational explosion caused by reference trajectory and unknown external disturbance. The adaptive method is used to deal with the unknown parameters and the comprehensive uncertainty which contains actuator faults and external disturbance. Furthermore, the event-triggered controller is designed to achieve the formation tracking with the reduction of update number of controller to save resources. The bounded stability of the tracking error is proved via the Lyapunov theory. At last, the effectiveness of the control strategy is verified by simulation.

Optimization of open optical communication with multiple UAV over non homogenous turbulent paths

The article is devoted to optimizing communication through open atmospheric channels with many UAV by using inhomogeneous turbulent paths. A probabilistic assessment of the total signal loss in the communication system between the central communication node and a group of UAV located at the same distance from this node is analyzed. The case is considered when the scintillation indices in the subzones of optical communication with the UAV are different, which leads to unequal signal losses in the corresponding optical channels. The target functional is compiled by using a certain integral of the lognormal probability density function, which includes the newly introduced function of the losses dependence from the scintillation index. An analytical dependence of these indicators, characterizing the worst case when the total losses in the network reach a maximum, is presented. Keywords optical communications, turbulence, UAV, optimization, atmosphere

Joint Caching, Communication, and Trajectory Optimization in Air-Ground Integrated Wireless Networks With Multiple UAVs and Multiple BSs

Joint Caching, Communication, and Trajectory Optimization in Air-Ground Integrated Wireless Networks With Multiple UAVs and Multiple BSs

Multi-UAV Cooperative Task Offloading and Resource Allocation in 5G Advanced and Beyond

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Optimizing Consensus Tracking Control of Multiple UAVs Formation Via Event-Triggered Critic Learning Neurodynamic Programming

Optimizing Consensus Tracking Control of Multiple UAVs Formation Via Event-Triggered Critic Learning Neurodynamic Programming

Coordinated Interference of Multiple UAVs on Networked Radar

Coordinated Interference of Multiple UAVs on Networked Radar

Cooperative Fusion Localization of a Nonstationary Target for Multiple UAVs Without GPS

Cooperative Fusion Localization of a Nonstationary Target for Multiple UAVs Without GPS

多无人机同时到达的可解集算法

多无人机同时到达的可解集算法

Formation Optimization for Multiple UAVs by Considering Constrained Effectiveness and Attitude Coupling Tracking

Formation Optimization for Multiple UAVs by Considering Constrained Effectiveness and Attitude Coupling Tracking

Safe Learning-Based Control for Multiple UAVs Under Uncertain Disturbances

Safe Learning-Based Control for Multiple UAVs Under Uncertain Disturbances

Distributed Cooperative Framework for Multiple UAVs Safety: A Capability-Triggered Mechanism

Distributed Cooperative Framework for Multiple UAVs Safety: A Capability-Triggered Mechanism