An Energy-Efficient Routing Algorithm for UAV Formation Based on Time-Aggregated Graph

The limited energy and high mobility of unmanned aerial vehicles(UAVs)lead to drastic topology changes in UAV formation.The existing routing protocols necessitate a large number of messages for route discovery and maintenance,greatly increasing network delay and control overhead.A energyefficient routing method based on the discrete timeaggregated graph(TAG)theory is proposed since UAV formation is a defined time-varying network.The network is characterized using the TAG,which utilizes the prior knowledge in UAV formation.An energyefficient routing algorithm is designed based on TAG,considering the link delay,relative mobility,and residual energy of UAVs.The routing path is determined with global network information before requesting communication.Simulation results demonstrate that the routing method can improve the end-to-end delay,packet delivery ratio,routing control overhead,and residual energy.Consequently,introducing timevarying graphs to design routing algorithms is more effective for UAV formation.

Nonlinear direct data-driven control for UAV formation flight system

This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,consider one nonlinear closedloop system with a nonlinear plant and nonlinear feed-forward controller simultaneously.To avoid the complex identification process for that nonlinear plant,a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly,whose detailed explicit forms are model inverse method and approximated analysis method.Secondly,from the practical point of view,after reviewing the UAV formation flight system,nonlinear direct data-driven control is applied in designing the formation controller,so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem.Since most natural phenomena have nonlinear properties,the direct method must be the better one.Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems,and the direct nonlinear controller design is the purpose of this paper.

Dynamic affine formation control of networked under-actuated quad-rotor UAVs with three-dimensional patterns

This paper focuses on the solution to the dynamic affine formation control problem for multiple networked underactuated quad-rotor unmanned aerial vehicles(UAVs)to achieve a configuration that preserves collinearity and ratios of distances for a target configuration.In particular,it is investigated that the quad-rotor UAVs are steered to track a reference linear velocity while maintaining a desired three-dimensional target formation.Firstly,by integrating the properties of the affine transformation and the stress matrix,the design of the target formation is convenient and applicable for various three-dimensional geometric patterns.Secondly,a distributed control method is proposed under a hierarchical framework.By introducing an intermediary control input for each quad-rotor UAV in the position loop,the necessary thrust input and the desired attitude are extracted.In the attitude loop,the desired attitude represented by the unit quaternion is tracked by the designed torque input.Both conditions of linear velocity unavailability and mutual collision avoidance are also tackled.In terms of Lyapunov theory,it is prooved that the overall closed-loop error system is asymptotically stable.Finally,two illustrative examples are simulated to validate the effectiveness of the proposed theoretical results.

MAV/UAV task coalition phased-formation method

The formation of the manned aerial vehicle/unmanned aerial vehicle(MAV/UAV) task coalition is considered. To reduce the scale of the problem, the formation progress is divided into three phases. For the task clustering phase, the geographical position of tasks is taken into consideration and a cluster method is proposed. For the UAV allocation phase, the UAV requirement for both constrained and unconstrained resources is introduced, and a multi-objective optimal algorithm is proposed to solve the allocation problem. For the MAV allocation phase, the optimal model is firstly constructed and it is decomposed according to the ideal of greed to reduce the time complexity of the algorithm. Based on the above phases, the MAV/UAV task coalition formation method is proposed and the effectiveness and practicability are demonstrated by simulation examples.

A Software-in-the-Loop Implementation of Adaptive Formation Control for Fixed-Wing UAVs

This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission(e.g.depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduP ilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.

Multiple UAVs cooperative formation forming control based on back-stepping-like approach

To ensure multiple unmanned aerial vehicles （UAVs）reach stable formation quickly, a cooperative guidance law basedon the back-stepping-like approach is designed in this paper.Adopting the guidance mechanism of virtue leader vehicle, thedynamic equation of tracking errors for each UAV is built. Thecommunication interactive relationships are described based ongraph theory, and the guidance law for formation reaching is ob-tained by the back-stepping-like approach. The formation stabilityis analyzed by constructing an appropriate Lyapunov function. Thesimulation results have shown that this guidance and control lawcan make each UAV converge to the trajectory of the virtue leaderultimately, and has the quicker rate of convergence and lowertracking error.

Coalition Formation for Multiple UAVs Cooperative Search and Attack with Communication Constraints in Unknown Environment

A coalition formation algorithm is presented with limited communication ranges and delays in unknown environment,for the performance of multiple heterogeneous unmanned aerial vehicles(UAVs)in cooperative search and attack missions.The mathematic model of coalition formation is built on basis of the minimum attacking time and the minimum coalition size with satisfying resources and simultaneous strikes requirements.A communication protocol based on maximum number of hops is developed to determine the potential coalition members in dynamic network.A multistage sub-optimal coalition formation algorithm(MSOCFA)with polynomial time is established.The performances of MSOCFA and particle swarm optimization(PSO)algorithms are compared in terms of complexity,mission performance and computational time.A complex scenario is deployed to illustrate how the coalitions are formed and validate the feasibility of the MSOCFA.The effect of communication constraints(hop delay and max-hops)on mission performance is studied.The results show that it is beneficial to determine potential coalition members in a wide and deep range over the network in the presence of less delay.However,when the delays are significant,it is more advantageous to determine coalitions from among the immediate neighbors.

Eagle-Vision-Based Object Detection Method for UAV Formation in Hazy Weather

Inspired by eagle’s visual system,an eagle-vision-based object detection method for unmanned aerial vehicle(UAV)formation in hazy weather is proposed in this paper.To restore the hazy image,the values of atmospheric light and transmission are estimated on the basis of the signal processing mechanism of ON and OFF channels in eagle’s retina.Local features of the dehazed image are calculated according to the color antagonism mechanism and contrast sensitivity function of eagle’s visual system.A center-surround operation is performed to simulate the response of reception field.The final saliency map is generated by the Random Forest algorithm.Experimental results verify that the proposed method is capable to detect UAVs in hazy image and has superior performance over traditional methods.

Leader-Follower UAV Formation Model Based on R5DOS-Intersection Model

This paper proposes a formation of multiple unmanned aerial vehicles(UAVs)based on the R5DOS(RCC-5 and orientation direction)intersection model.After improving the R5DOS-intersection model,we evenly arranged 16 UAVs in 16 spatial regions.Compared with those of the rectangular formation model and the grid formation model,the communication costs,time costs,and energy costs of the R5DOS model formation were effectively reduced.At the same time,the operation time of UAV formation was significantly enhanced.The leader-follower method can enhance the robustness of the UAV formation and ensure the integrity of communication during UAV formation operation.Finally,we conducted a simulation experiment on the model and found that the R5DOS model formation was stable and could maintain the desired formation.The randomly generated UAVs could quickly fly to the formation path in a short time,establish formation,and carry out operations.When the leader fails,the follower could travel to the original trajectory of the failed leader in a short time,replace the leader,and continue to communicate and improve the robustness of the formation.To sum up,the UAV formation based on the R5DOS model has the advantages of long operation time,strong endurance,low communication cost,and stable formation,which is of great significance for research on UAV formation.

A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy

This paper considers the formation control problem for a group of unmanned aerial vehicles( UAVs)employing consensus with different optimizers. A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle,and a single vehicle is limited by its undeveloped intelligence and insufficient load. Among the many formation methods,consensus has attracted much attention because of its effectiveness and simplicity. However,at the beginning of convergence,overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting,which are inborn shortcomings of consensus. It is natural to modify this method with lots of optimizers. In order to reduce overshoot and smooth trajectories, this paper first adopted particle swarm optimization( PSO), then pigeon-inspired optimization( PIO) to modify the consensus. PSO is a very popular optimizer,while PIO is a new method,both work but still retain disadvantages such as residual oscillation. As a result,it was necessary to modify PIO,and a pigeon-inspired optimization with a slow diving strategy( SD-PIO) is proposed. Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved.Finally,a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.

The Design of Small Micro-UAV Formation of ＂Sub-mother＂ type

This paper aims to provide a design about a kind of low-cost ＂sub-mother＂ UAV small formation by using the low-cost micro-UAV as the platform. Combined with the current international situation in the UAV formation research, the modeling is based on the distributed control method and the behavior-based strategy. In this paper, some research to a few key issues has been carried out.

Distributed Robust UAVs Formation Control Based on Semidefinite Programming

The formation control of unmanned aerial vehicle(UAV)swarms is of significant importance in various fields such as transportation,emergency management,and environmental monitoring.However,the complex dynamics,nonlinearity,uncertainty,and interaction among agents make it a challenging problem.In this paper,we propose a distributed robust control strategy that uses only local information of UAVs to improve the stability and robustness of the formation system in uncertain environments.We establish a nominal control strategy based on position relations and a semi-definite programming model to obtain control gains.Additionally,we propose a robust control strategy under the rotation setΩto address the noise and disturbance in the system,ensuring that even when the rotation angles of the UAVs change,they still form a stable formation.Finally,we extend the proposed strategy to a quadrotor UAV system with high-order kinematic models and conduct simulation experiments to validate its effectiveness in resisting uncertain disturbances and achieving formation control.

Event-triggered leader-following formation control for multi-agent systems under communication faults: application to a fleet of unmanned aerial vehicles

The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems(MASs)under communication faults.All the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the agents.Linear matrix inequalities(LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization error.Based on the closed-loop system,an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy consumption.The proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles(UAVs)under communication faults.A comparison between a state-of-the-art technique and the proposed technique has been provided,demonstrating the performance improvement brought by the proposed approach.

Fault Tolerant Control Scheme Design for Formation Flight Control System of Multiple Unmanned Aerial Vehicles

The command tracking problem of formation flight control system(FFCS)for multiple unmanned aerial vehicles(UAVs)with sensor faults is discussed.And the objective of the addressed control problem is to design a robust fault tolerant tracking controller such that,for the disturbances and sensor faults,the closed-loop system is asymptotically stable with a given disturbance attenuation level.A robust fault tolerant tracking control scheme,combining an observer with H∞ performance,is proposed.Furthermore,it is proved that the designed controller can guarantee asymptotic stability of FFCS despite sensor faults.Finally,a simulation of two UAV formations is employed to demonstrate the effectiveness of the proposed approach.

RVO-DDPG算法在多UAV集结航路规划的应用

针对传统智能优化算法处理不确定复杂环境下多UAV集结航路规划存在计算量大、耗时长的问题,提出了一种基于互惠速度障碍法(reciprocal velocity obstacle,RVO)的深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法。引入互惠速度障碍法指导UAV对不确定环境内障碍进行避碰,有效提高了目标actor网络的收敛速度,增强了算法的学习效率。设计了一种基于综合代价的奖励函数,将多UAV航路规划中的多目标优化问题转化为DDPG算法的奖励函数设计问题,该设计有效解决了传统DDPG算法易产生局部最优解的问题。基于Pycharm软件平台通过仿真验证了该算法的性能,并与多种算法进行对比。仿真实验表明,RVO-DDPG算法具有更快的决策速度和更好的实用性。

无人机集群协同控制技术综述

协同控制技术作为多智能体分工合作完成任务的关键核心技术,能解决无人机集群编队、队形重构和避障避碰等问题,是无人机集群正常运作的基础。针对近几年国内外无人机集群协同控制技术的发展历程,概述3种控制结构原理及其优缺点;分析了基于3种控制结构的编队控制方法及其优缺点;从无人机集群协同编队控制技术出发,分析基于编队控制的避障方法;指出了现阶段无人机集群协同控制技术面临的瓶颈问题,并对协同控制技术的未来发展方向进行了展望,为无人机集群编队控制和避障方法研究提供一定的借鉴。

微多普勒辅助的城市环境无人机编队检测方法

针对城市复杂环境下电磁环境复杂、多径杂波和干扰信号密集等现象,传统的无人机(UAV)检测方法通过获取回波信号提取目标多普勒信息进行检测,易受到环境影响导致检测效果不理想。该文提出微多普勒辅助的城市环境无人机编队检测方法,充分利用无人机的微动特征,能够在复杂环境下提高检测精度。首先,参数化建模表征城市复杂环境下无人机旋翼的雷达回波微多普勒信号,利用YOLOv5s检测微多普勒闪烁脉冲,有效提取位置信息;然后,引入雷达信号分选方法的脉冲重复间隔(PRI)变换,分类获得无人机编队数量;最后,利用Kmeans算法验证无人机编队检测方法的准确性。结果表明,所提方法在信噪比2 dB时7架无人机的检测精度高于90%,能够用于城市复杂环境存在干扰脉冲、多径效应、局部脉冲丢失的无人机编队检测。

基于改进势场和一致性的无人机编队避障方法

针对无人机编队在执行任务时存在的机间碰撞、与障碍物碰撞及编队通信等问题,将无人机动力学方程视为二阶积分模型,提出一种人工势场法和一致性理论相结合的协同避障控制策略。首先建立变增益函数来降低超调和稳态误差,并由此设计虚拟领航结构下的无人机编队一致性控制方法;然后应用分段思想,增加回环力、机间防碰撞力等来改进传统人工势场法,以解决局部最优和编队安全问题,从而实现编队避碰和避障;最后将一致性控制方法和改进后的人工势场法相结合,提出编队协同避障控制律,并利用小增益定理对其进行稳定收敛性分析。仿真结果表明,无人机编队可以实现精准避障后恢复预期编队队形,最终稳定到达目标位置。

多四旋翼无人机编队自适应容错控制

针对四旋翼无人机在编队控制时受到执行器故障的影响,引起无人机之间跟踪误差的问题,而传统PID控制的固定参数无法适用于四旋翼无人机面临复杂的环境,因此设计了模糊自适应PID容错控制器以及多无人机的滑模协同控制器.首先,针对执行器故障无人机进行系统建模,给定领导无人机的参考轨迹,通过设计的模糊自适应PID控制器使得执行器故障的无人机跟踪给定轨迹.其次,基于滑模控制理论,设计编队协同控制器,控制Follower无人机与Leader无人机之间的距离误差趋近于零,实现期望的编队队形.最后,通过仿真实验对比结果表明,所设计方法超调量小,鲁棒性强,对四旋翼无人机有较好的控制效果.

基于自适应迭代学习的多智能体系统编队控制

针对带未知时变参数的非线性多智能体系统的编队问题,提出一种分布式自适应迭代学习控制策略。首先,通过傅里叶级数对系统的不确定参数进行展开,采用一个收敛级数序列处理傅里叶级数展开产生的截断误差,结合多智能体运行过程中的编队误差推导自适应迭代学习控制律和参数更新律;其次,针对领导者动态对大部分智能体都是未知的情况,设计新的辅助控制来补偿未知动态和避免未知有界干扰;然后,基于李亚普诺夫能量函数证明了在所设计控制律作用下多智能体系统编队误差随着迭代次数的增加在有限时间内趋于0;最后,将该控制策略运用到多无人机编队系统中,并通过搭建半物理实验平台,验证了控制方法的有效性。实验结果表明该控制方法可以确保多智能体快速形成所需编队,并且每个智能体在有限时间内可以精确跟踪期望轨迹。所提方法充分考虑了多智能体系统的参数不确定性以及抗干扰的能力,为实际应用中复杂多智能体系统的精确控制提供了有效的方法。