带参改进一致性算法在多无人机集结任务的应用

多UAV集结是实现多UAV同时执行侦察和攻击任务的前提,是执行任务成功率最大化的根本保证；基于多智能体一致性理论的多UAV分布式协同控制已经广泛应用于多UAV协同作战中,通过一致性算法实现多UAV状态趋于一致；建立了多UAV集结问题的数学模型,基于协调变量和协调函数的分布式求解策略进行求解；改进了多智能体基本一致性算法和基于过去状态差值的一致性算法,提出了一种带参改进一致性算法,分析了算法的收敛条件；该算法提高了系统的收敛速度,缩短任务完成的时间；仿真实验验证了带参改进一致性算法的有效性和可行性。     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

面向不确定目标的多无人机协同搜索控制方法

目标搜索是多无人机协同控制的重要研究内容。多架UAV(Unmanned Aeiral Vehicle)同时对一个未知区域进行搜索,目的在于获取搜索区域的信息,尽可能多地发现目标。针对不确定目标的搜索问题,研究多无人机协同搜索控制的新方法。建立多UAV运动模型,用目标存在概率对搜索环境进行描述,给出基于Bayesian准则的搜索环境更新方法,考虑了环境探测回报、目标发现回报和无人机协同回报,采用MPC实现对多目标优化问题的迭代求解。通过仿真实验和对比分析,证明了该方法具有更好的搜索性能。     

地面无人系统的多智能体协同控制研究综述

本文结合多智能体系统控制理论与技术的研究现状,以地面无人系统为控制对象,对理论及其技术发展作了详细概述.从多智能体的行为协同控制和任务协同控制两个侧面讨论了相关的理论及应用问题,并指出当前协同控制研究中存在的一些问题以及今后的发展方向.地面无人系统的协同控制对于有效提升社会及军事效益,最大限度地发挥地面任务执行的效能具有重要意义.     

煤矿掘进多行为协同控制智能决策模型

智能决策支持的掘进多行为协同控制是煤矿掘进工作面智能化的核心之一,掘进多行为协同控制的最优时序规划是智能决策的关键。针对煤矿掘进多行为控制模式单一、固化、协同作业能力差等问题,设计了一种煤矿掘进多行为协同控制智能决策模型,实现了掘进多行为在最优时序下的协同作业。首先,提出了掘进多行为协同控制智能决策方法,确定了掘进多行为可行时序规划集和多目标最优时序规划策略;其次,根据掘进现场的规定和工艺要求,确定了掘进动作事件集,通过对事件集中两两动作事件之间时间关系的分析,求出掘进多行为时间关系约束矩阵;然后,根据时间点关系约束矩阵转换方法,将掘进多行为时间关系约束矩阵转换为时间点关系约束矩阵,再求出掘进多行为可行时序规划集;最后,定义不同掘进目标下的求解函数,求得不同掘进目标的最优时序。实验结果表明,在不同掘进目标下,按照模型决策出的掘进动作最优时序规划结果,掘进机器人可无干涉协同作业,且掘进作业1个工作循环的执行时间与决策模型计算的时间基本一致。     

基于Multi-Agent系统的多飞行器协同路径规划方法的研究

无人多飞行器(UAV)协同技术是当前分布式人工智能的一个热点领域,其中一个关键技术在于如何实现多UAV集群根据复杂环境中目标、威胁、地形变化以及各UAV之间的性能约束动态进行实时性航路规划。提出一种基于Multi-agent系统的多UAV对实时动态多目标进行路径规划的方法。其核心是基于Multi-agent系统的decen-tralized控制方案。在Multi-agent平台上,实现了agent对于环境、目标、任务等路劲规划约束条件的建模,同时提出了多agent动态路径规划方法的实现方案。方案使用DisCSP模型框架,将基于真实复杂战场环境的实时路径规划问题所涉及的多复杂限制条件,抽象成Multi-agent系统中的各个约束条件,通过多agent间Dynamic Programming过程求解多UAV实时动态多目标的路径规划和协同任务分配的ABT算法,并实现在动态威胁和地形以及动态目标下具备集群协同能力的多UAV实时仿真系统。     

多智能体系统及其协同控制研究进展

对多智能体系统及其协同控制理论研究和应用方面的发展现状进行了简要概述.首先给出 Agent 及多 Agent 系统的概念和特性等,介绍了研究多Agent系统协同控制时通常用到的代数图论;然后综述了近年来多Agent系统群集运动和协同控制一致性方面的研究状况,并讨论了其在军事、交通运输、智能机器人等方面的成功应用;最后,对多Agent系统未来的发展方向进行了探讨和分析,提出几个具有理论和实践意义的研究方向,以促使多Agent系统及其协同控制理论和应用的深入研究.     

基于集散式模型预测控制的多无人机协同分区搜索

针对多无人机在对大范围目标区域执行协同搜索任务时搜索资源分配不均、容易因频繁转场造成资源浪费等问题,借鉴集中式控制和分布式控制结构的优点,建立了集散式多无人机(unmanned aerial vehicle,UAV)协同搜索结构体系,通过聚类分析和V图划分等方法对目标区域进行分区,结合各子区域任务特点对无人机群进行搜索任务分配,并采用一种经改进后可有效增大UAV预测范围的预测控制模型,研究了动态环境下多UAV集散式协同分区搜索问题,最后,将所提方法与常见几种协同搜索方法进行对比仿真,获取仿真结果验证了所提方法在目标发现概率和搜索效率方面的有效性和优越性.     

面向无人机电力巡检的语义实体构建及航迹控制方法

航迹的合理控制是影响无人机（UAV）智能决策重要因素。考虑UAV巡检的局部观测性和任务环境的高空复杂性,以电力巡检领域知识为背景,提出面向UAV电力巡检的语义实体构建及航迹控制方法。首先,基于电力巡检领域的实体知识构建空间拓扑网络,并生成关于位置节点的语义航迹序列网络及其语义接口;然后,根据空间拓扑结构相似性度量的结果集,提出安全许可机制和基于强化学习的航迹控制策略,实现UAV电力巡检在统一的概念内涵和位置结构上的轨迹控制。实验结果表明：作为UAV巡检的实例,所提方法得到的最优策略能获得最大化的鲁棒性能;同时,该方法通过强化学习方法使目标网络的适应度稳定收敛且实体区域覆盖率高于95%,为UAV电力巡检任务决策提供了飞行依据。     

多无人机自适应编队协同航迹规划

针对多无人机(UAV)协同航迹规划中因编队队形约束而忽略部分较窄通道的问题,提出了一种基于自适应分布式模型预测控制的快速粒子群优化(ADMPC-FPSO)方法。该方法利用领航跟随法和虚拟结构法相结合的编队策略构造出虚拟编队引导点,以完成自适应编队协同控制任务。根据模型预测控制的思想,结合分布式控制方法,将协同航迹规划转化为滚动在线优化问题,且以最小距离等性能指标为代价函数。通过设计评价函数准则,使用变权重快速粒子群优化算法对问题进行求解。仿真结果表明,通过所提算法能够有效实现多无人机协同航迹规划,并可根据环境变化快速完成自适应编队变换,同时较传统编队策略代价更低。     

基于KnCMPSO算法的异构无人机协同多任务分配

随着无人机(Unmanned aerial vehicle, UAV)技术的广泛应用和执行任务的日益复杂,无人机多机协同控制面临着新的挑战.以无人机总飞行距离和任务完成时间为优化目标,同时考虑异构无人机类型、任务执行时序等多种实际约束,构建基于多种约束条件的异构无人机协同多任务分配模型.该模型不仅包含混合变量,同时还存在多个复杂的约束条件,因此,传统的多目标优化算法并不能有效地处理混合变量及对问题空间进行搜索并生成满足多种约束条件的可行解.为高效求解上述模型,提出一种基于拐点的协同多目标粒子群优化算法(Knee point based coevolution multi-objective particle swarm optimization,Kn CMPSO),该算法引入基于拐点的学习策略来更新外部档案集,在保证收敛性的同时增加种群的多样性,使算法能搜索到更多可行的任务分配结果;并基于二进制交叉方法,引入基于学习的粒子更新策略来提升算法的收敛性及基于区间扰动的局部搜索策略以提升算法的多样性.最后通过在四组实例上的仿真实验验证了所提算法在求解异构无人机协同多任务分配问题上的有效性.     

基于动态角色分配的一致性协同无人机编队控制

随着微电子技术、数字通信技术的飞速发展,无人机编队已应用到许多领域。针对无人机编队保持和编队中能耗问题,提出了一种基于动态角色分配的一致性协同无人机编队控制方法。根据无人机运动学模型设计了一种反馈线性化姿态控制器。在此基础上,基于长机僚机模式设计了一种一致性编队控制算法,提升了编队系统的鲁棒性。同时,设计了一种基于匈牙利算法动态角色分配方法,使多无人机在执行任务过程中可依据具体的周围环境情况来重新制定编队方案确定各无人机位置以此来缩小执行任务周期,同时减小整体能量消耗,并以5架无人机构成编队为例开展了编队飞行仿真分析。仿真结果表明,基于动态角色分配的一致性协同无人机编队控制算法保证了编队控制系统的控制精度和鲁棒性,有效地减小了整体的能量消耗。     

仿欧椋鸟大规模超机动行为的无人机集群转弯控制

为了在动态不确定环境下实现大规模无人机集群转弯的快速性和一致性,提出了一种仿欧椋鸟大规模超机动行为的无人机集群转弯控制方法．通过分析欧椋鸟大规模超机动行为,分别建立邻居选择、局部交互和信息传递机制,并将其映射到无人机集群转弯控制中．在这些机制的基础上,利用改进社会力模型进行无人机转弯控制,使无人机集群在没有外界刺激的情况下实现快速聚集和速度极化;而在有外界刺激的情况下,无人机切换模式,整个集群快速完成超机动转弯动作以避开危险．仿真实验结果表明,在该模型作用下无人机集群既能满足转弯曲率的要求,又能保持群集运动的一致性．     

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi–Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.     

基于元胞自动机的蜂群无人机故障影响模型

基于固定翼无人机飞行特性以及蜂群无人机控制策略,针对无人机控制器遭受恶意攻击的情形,采用时序网络与元胞自动机理论分析蜂群无人机故障影响机理.首先,通过时序网络分析蜂群无人机拓扑网络的变化情况,提出基于跳数的故障传播路径的确定方法;其次,考虑蜂群无人机状态信息,建立符合蜂群无人机特征的元胞对象,同时基于局部信息交互原则,确定元胞自动机的状态演变规则,并依据近邻信息对无人机控制律的影响,提出矢量投影法来确定故障影响权值,辨识出各无人机故障影响程度的动态变化情况;最后,建立仿真模型,利用预测与实际故障影响程度结果,基于DCG算法与模式距离验证所建故障影响模型的有效性.     

基于粒子群圆检测的无人机目标定位算法

无人机在室内等复杂环境中飞行时,存在GPS信号较弱、惯性传感器累计误差较大等问题,导致无法实现室内的精准定位.本文提出一种基于粒子群圆检测算法的无人机目标定位方法,该方法通过OpenCV视觉模组进行图像预处理,并通过增量式PID(Proportion Integration Differentiation)与图像滤波相...     

多无人机模糊多目标分布式地面目标协同追踪

针对城市环境中多约束条件下多无人机协同追踪地面目标问题,综合考虑具有不同重要性等级的多个优化目标,提出了一种基于分布式预测控制的模糊多目标航迹规划方法.首先,考虑城市环境中建筑物对无人机视线遮挡、无人机和传感器能量消耗等因素,分别采用目标覆盖度、控制输入代价和开关量形式传感器能耗等为目标函数,将多无人机协同追踪航迹规划转化为多目标优化问题;然后,基于分布式预测控制框架,利用每架无人机未来有限时域内的预测状态,构建多无人机之间的避碰约束,并结合最小转弯半径等约束,形成分布式协同航迹规划模型;最后,针对多个优化目标的不同重要性等级要求,利用模糊满意优化思想将目标模糊化,并根据更重要目标具有更重要满意度的原则,将优先等级表示为松弛满意度序,通过在线求解得到有限时域内每架无人机的局部航迹;与传统多目标加权算法仿真结果对比,验证了所提方法的有效性,充分说明了该方法能够获得同时满足目标优化和重要性等级要求的最优航迹.     

The Small and Silent Force Multiplier: A Swarm UAV—Electronic Attack

At the last two decades, according to UAVs concepts and technological advances, there have been lots of unimagined improvements. Nowadays there are serious works and researches about the usage of UAVs in military operations at electronic warfare (EW) missions. But most of the work on UAV platforms is based upon the advantages of a single, big, expensive, and non-expendable platform. In this study, to get rid off the disadvantages of a stand alone platform a new concept is developed consisting of multiple UAVs with smaller dimensions, at a cheaper price and a wider coverage. According to clarify the study, firstly the EW and RADAR systems and then the swarm UAV concepts are explained. In this manner the current and previous works are pointed out and then the use of the swarm UAVs for EA in military operations is stated. Objectively, the swarm UAV concept’s advantages and some outstanding challenges to the intra-theater space have been put forward regarding the information mentioned above. As a result it is considered that the swarm UAV systems will be tasked important EW missions in the future operation theatres, as soon as the technical handicaps are solved.     

An Optimal Control Strategy for Multi-UAVs Target Tracking and Cooperative Competition

An optimal control strategy of winner-take-all (WTA) model is proposed for target tracking and cooperative competition of multi-UAVs (unmanned aerial vehicles). In this model, firstly, based on the artificial potential field method, the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets. Secondly, according to the finite-time convergence high-order differentiator, a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory. Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking, high precision, strong stability and avoiding chattering. Finally, a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy. Theoretical analysis and numerical simulation results show that the model has the fast convergence, high control accuracy, strong stability and good robustness.      

Autonomous control for multi-agent non-uniform spraying

The use of computer controlled swarms of UAVs for crop spraying enables non-uniform coverage of high precision and time efficiency. For this purpose an algorithmic control method for autonomous UAV swarm spraying, based on multi-agent area coverage method Heat Equation Driven Area Coverage (HEDAC), is proposed. Motion control relies on suitable spraying model which enables of multi-agent spraying simulations for arbitrary agent’s trajectories. The HEDAC control method was tested in comparison with conventional (Lawnmower) and state-of-the-art (SMC) path planning methods on three numerical tests: two based on simple geometries and algebraically defined spraying goal densities, and one based on a real-world crop disease map. Additionally, the effects of spraying tool design (number of nozzles and their spraying density) on spraying accuracy were analyzed, with results consistently illustrating the direct causation between tool precision and overall spraying error. The results of the testing have shown HEDAC control to be significantly faster than Lawnmower (approximately 35%–65% less time needed) and SMC (approximately 15%–50% less time needed) in achieving convergence, while producing spraying density of comparable accuracy. Moreover, HEDAC spraying typically mitigates over-spraying by approximately 3%–8% when compared with conventional path planning. In additional tests, it is shown that an implementation of collision avoidance technique for HEDAC motion control provides collision-free UAV swarm spraying. The effect of HEDAC collision avoidance control on the spraying convergence rate and accuracy is practically insignificant. It may be concluded that in real-world application HEDAC controlled UAV spraying swarms are expected to significantly outperform UAVs operating with existing path planning methods.