无人机多阶段航迹预测协同任务规划

针对多无人机(Unmanned Aerial Vehicles, UAVs)协同控制问题,提出了一种UAVs多阶段航迹预测分布式任务规划方法。定义从一次任务分配开始到其中一项任务完成为一个任务周期。在每个规划周期,首先,各UAV使用A*算法快速预测到所有任务目标的路径,提供至任务分配;然后,采用聚类算法修改目标价值向量,协商分配结果,并实时计算探测范围内的最短路径;最后,采用三次B样条曲线平滑所分配的最短路径,在线规划出满足飞行约束的飞行航迹。通过仿真实验对算法的有效性进行了验证,结果表明,提出的算法能够实时获得近似最优的任务分配结果并规划出可飞行航迹,并有效处理突发任务。     

UAV path design with connectivity constraint based on deep reinforcement learning

Cellular networks are expected to communicate effectively with unmanned aerial vehicles (UAVs) and support various applications. However, existing cellular networks are primarily designed to cover users on the ground; thus, coverage holes in the sky will exist. In this paper, we investigate the problem of path design for cellular-connected UAVs, taking into account the interruption performance throughout the UAV mission to minimize the completion time. Two types of connectivity constraints requirements are assumed to be available. The first is defined as the maximum continuous time interval that the UAV loses connection with base stations (BSs) below a predefined threshold. For the second, we consider the sum outage of UAV is limited during the entire UAV mission. The UAV is tasked with flying from a starting location to a final destination while minimization the mission time, satisfying the two constraints, separately. The formulated path design problem which involves continues variables and a dynamic radio environment, is not convex and thus is extremely difficult to solve directly. To tackle this challenge, a deep reinforcement learning (DRL) based trajectory design algorithm is proposed, where the Dueling Double Deep Q Network(Dueling DDQN) with multi-steps learning method is applied. Simulation results demonstrate the effectiveness of the proposed DRL algorithm and achieve a trade-off between the trajectory length of the UAV and connection quality.     

基于遗传算法的输电线路无人机巡检路径规划

针对无人机有效、安全巡检输电线路的路径问题,提出了一种基于遗传算法的输电线路无人机巡检路径规划方法,采用极坐标编码方式对无人机巡检路径构造染色体。结合实际情况中的无人机巡检各种约束问题,设计了适合于无人机巡检路径规划的遗传算子。实验结果证明算法能综合考虑各种因素,提高了全局寻优能力,是解决实际输电线路无人机巡检路径规划问题的较好办法。     

基于DGPS航迹偏差的多旋翼无人机磁干扰检测技术研究

为了解决多旋翼无人机在飞行作业过程中受到环境磁干扰导致作业异常的问题,在使用DGPS进行差分定位的基础上,提出了一种基于航迹偏差的多旋翼无人机磁干扰检测技术。其基本原理是,当多旋翼无人机受到磁场干扰时,其飞行航迹会偏离预设航线,检测其航迹的偏离距离,通过与阈值比较,可以用来判断是否存在环境磁干扰。实验结果表明该方法可以有效检测环境磁场异常,在某些情况下比传统的磁航向角误差阈值检测方法可靠性更高,虚警率更小。综合使用航迹偏差检测方法和磁航向角误差检测方法,可有效（提高）环境磁场异常检测的准确度,降低虚警率。     

单目视觉融合激光投射的无人机障碍探测方法

针对微小型无人机在飞行作业任务中的主动避障需求,提出一种用于微小型无人机避障的、基于单目视觉与主动激光点阵投射的障碍探测方法。使用单目相机采集投射的激光点阵图案,经过图像分割、聚类、质心提取等处理过程,通过像面激光线方程约束快速排除特征一致激光点的歧义,使用激光点探测出无人机前方空间中障碍的方位信息。实验验证装置在基线距离为65 mm,工作距离为7 m的条件下,障碍探测的相对误差在1.5%以内。该方法精度高、时间复杂度低,可满足低算力的微小型无人机对障碍探测方法的需求,为进一步避障策略的生成提供数据支撑。     

基于模型的预警无人机飞控系统开发与验证

为应对城市低空预警任务对固定翼无人机飞控系统自主飞行需求,采用基于模型设计方法完成无人机飞控系统开发与验证。开发过程中以飞控算法模型为中心,逐级开展飞控算法模型设计与数学仿真、算法快速原型验证以及飞控系统硬件产品实现。飞控算法模型在Matlab/Simulink平台中完成了构建及数学仿真验证,结合Higale系统提供的实时仿真环境完成算法快速原型验证,基于代码自动生成方式将算法模型自动转换为可在DSP中运行的实时代码,下载到飞控计算机中,进行硬件在回路仿真验证,并进一步进行整机地面验证。经过实践,所提出的飞控系统开发与验证流程可以将系统设计中存在的问题缺陷在开发早期暴露出来并及时修正,保证了系统研制进度、成本和品质。     

A greedy-model-based reinforcement learning algorithm for Beyond-5G cooperative data collection

Data collection is an essential part of Beyond-5G and Internet of Things applications. In urban area, heterogeneous access points such as Wi-Fi routers and base stations can meet the required communication coverage and bandwidth in data collection processes. However, in remote area, without communication infrastructures, it is hard to guarantee the communication quality of a large-scale data aggregation network. An existing approach is to use an unmanned aerial vehicle (UAV) to act as a mobile sink to perform data collection and increase the coverage of intelligent wireless sensing and communications. The efficiency and the reliability of such a UAV-assisted data collection system can be significantly enhanced with an intelligent cooperative strategy for the sensors deployed in the field to communicate with the UAV. Furthermore, an energy-efficient trajectory planning algorithm is crucial to address the physical limitations of the UAV in this application. In this paper, a data collection process is modeled as a Markov decision process (MDP). The paper begins with proposing two heuristic greedy algorithms, namely distance-greedy (DG) algorithm and rate-greedy (RG) algorithm, which are designed based on prior knowledge of the system and can guarantee the completion of the data collection process in a remote area without the help of fixed communication infrastructures. Based on the outcomes, a multi-agent greedy-model-based reinforcement learning (MG-RL) algorithm is proposed, which specifically designs the environmental state and the reward scheme, and introduces multiple UAVs with different parameters to explore environments in parallel to accelerate the training. In conclusion, the two proposed greedy algorithms have lower complexity of implementation while the proposed MG-RL algorithm yields practical UAVs’ flight trajectories and shortens the time for completing a data collection task.     

A survey on UAV placement optimization for UAV-assisted communication in 5G and beyond networks

With the increase in capacity demands and the requirement of ubiquitous coverage in the fifth generation and beyond wireless communications networks, unmanned aerial vehicles (UAVs) have acquired a great attention owing to their outstanding characteristics over traditional base stations and relays. UAVs can be deployed faster and with much lower expenditure than ground base stations. In addition, UAVs can enhance the network performance thanks to their strong line-of-sight link conditions with their associated users and their dynamic nature that adapts to varying network conditions. Optimization of the UAV 3D locations in a UAV-assisted wireless communication network was considered in a large body of research as it is a critical design issue that greatly affects communication performance. Although the topic of UAV placement optimization was considered in few surveys, these surveys reviewed only a small part of the growing literature. In addition, the surveys were brief and did not discuss many important design issues such as the objectives of the optimization problem, the adopted solution techniques, the air-to-ground channel models, the transmission media for access and backhaul links, the limited energy nature of the UAV on-board batteries, co-channel interference and spectrum sharing, the interference management, etc. Motivated by the importance of the topic of UAV placement optimization as well as the need for a detailed review of its recent literature, we survey 100 of the recent research papers and provide in-depth discussion to fill the gaps found in the previous survey papers. The considered research papers are summarized and categorized to highlight the differences in the deployment scenario and system model, the optimization objectives and parameters, the proposed solution techniques, and the decision-making strategies and many other points. We also point to some of the existing challenges and potential research directions that have been considered in the surveyed literature and that requires to be considered     

无人机匹配地形飞行的无线紫外光引导方法

使用紫外光引导无人机飞行,具有抗干扰能力强、全天候非直视通信、保密通信能力强和适用于特殊场合等优点。因此,提供一种无人机匹配地形飞行的无线紫外光引导方法,以辅助无人机匹配不同地形飞行。提出一种基于紫外光的光功率控制方法,调整紫外信标发送端的角度和发射功率,使得到达无人机飞行平面的紫外光的功率相等,从而保障无人机安全飞行。对功率控制方法进行了计算机仿真。实验结果表明,紫外光LED发送张角不超过80°时,功率容易控制,信标集成易于实现,且受地形、高度等因素影响较小。     

Analysis of fluctuations of antenna pattern in U-V2X communications

The third-generation partnership project (3GPP) defined several scenarios of vehicle-to-everything (V2X) networks such as vehicle-to-vehicle, vehicle-to-pedestrian, and vehicle-to-infrastructure. Unmanned aerial vehicles (UAVs) exploiting such scenarios for vehicular applications like vehicular-platooning, autonomous driving, and traffic management have shown improved networks’ performance in UAV-V2X (U-V2X) communications. This promising performance is typically obtained by considering high directional UAV antennas. However, the UAV vibration makes UAV antennas’ beam-width sensitive to UAV fluctuations which results in degrading the networks’ performance. Thus, in this paper, the performance of a U-V2X network in the presence of UAV fluctuations is investigated. The UAVs are modeled using an independent homogeneous Poisson Point Process and the vehicular-nodes are modeled using an independent Poisson Line Process. To this end, the association probability and success probability of various links such as vehicular-node connected to a UAV link, vehicular-node connected to a near-by vehicular-node link, and the overall U-V2X link are derived. In addition, the effect of different network settings such as the number of vehicular-nodes, UAVs, roads, and antennas is quantified. Moreover, the spectral efficiency of a U-V2X network is evaluated. Extensive simulations revealed that V2X networks facilitate UAVs and that for less dense areas with fewer roads, buildings, and crowds; UAVs provide more reliable links for connectivity than vehicular-nodes. Furthermore, it is also revealed that UAVs provide less reliable connection for V2X networks with larger UAV antenna fluctuations.     

Deployment and trajectory design of fixed-wing UAVs in NOMA assisted wireless networks

This paper proposes a deployment and trajectory scheme for fixed-wing unmanned aerial vehicles (UAVs) deployed as flying base stations in multi-UAV enabled non-orthogonal multiple access (NOMA) downlink communication. Specifically, the deployment of UAVs and power allocation of users are jointly optimized to maximize the sum-rate. Thereafter, the energy efficiency maximization problem is formulated to optimize the trajectory of UAVs by jointly considering the quality of service (QoS) requirement of users, various flight constraints, limited on-board energy, and users’ mobility. Initially, the existing users are divided into clusters by k-means clustering, where each cluster is served by a single UAV. Then, the clusters are further divided into multiple sub-clusters, each having a pair of near and far users. Orthogonal multiple access (OMA) is applied among sub-clusters and NOMA is applied to intra sub-cluster users. Lastly, the Balanced-grey wolf optimization (B-GWO) algorithm is proposed for solving the non-convex optimization problems. Simulation results prove the superiority of the B-GWO based deployment and trajectory algorithms compared to the benchmarks. In addition, the proposed B-GWO based trajectory algorithm achieves a near-optimal performance with an optimality gap of less than 1.5% compared to the exhaustive search.     

A restricted-direction target search approach based on coupled routing and optical sensor tasking optimization

A key issue in UAVs (Unmanned Air Vehicles) and UAV-mounted sensor control is the target search problem: finding targets in minimum time. In this paper, we proposed a restricted-direction target search approach based on coupled routing and optical sensor tasking optimization. In this method, we consider a single UAV, which is equipped with two optical sensors to view a limited large region of the dynamic environment. The UAV moves in the dynamic environment, searches for targets of interest, and is capable of avoiding obstacles and threats immediately. The paths are obtained considering actual maneuverability limitations of the UAV and are evaluated according to optimization of the optical sensor tasks for the duration of the path. Series of comparative experimental results demonstrate that this algorithm makes effective use of the coupled method of optimization and performs significantly better than previously proposed approaches.     

基于GPS、磁罗盘与大气数据计算机的无人机风估计

针对无人机实时航路规划及适应环境变化的自主能力发展需求,提出了一种新的风估计与空速校准的方法;该方法基于GPS接收机、大气计算机和磁罗盘等传感器实现;针对定常风模型,风速、风向能够利用地速、风速和空速之间的速度矢量三角形关系计算得到;采用无导扩展卡尔曼滤波(DEKF),估计风场信息以及真空速的比例校准系数;利用某型无人机数字仿真平台,在2D定常风条件下进行了全过程自主飞行仿真;仿真结果表明:该方法在航路跟随的直线段、转弯段均能准确估计。     

基于LabVIEW Vision的无人机自主着降系统设计与实现

四旋翼无人机具有低成本、垂直起降、机动性好等优点,在民用领域诸如航拍、植保、物流、电力巡线等场合得到了广泛的关注和应用。随着四旋翼无人机应用的普及,一些新的问题也随之出现,其中一个厄待解决的问题是如何提高无人机的降落精度和可靠性,特别在超视距应用场景下,这一需求尤为突出。通过采用机器视觉技术,利用LabVIEW Vision编制视觉识别软件,可以控制四旋翼无人机实现高精度的自主降落。     

A novel algorithm for solving optimal path planning problems based on parametrization method and fuzzy aggregation

In this Letter a new approach for solving optimal path planning problems for a single rigid and free moving object in a two and three dimensional space in the presence of stationary or moving obstacles is presented. In this approach the path planning problems have some incompatible objectives such as the length of path that must be minimized, the distance between the path and obstacles that must be maximized and etc., then a multi-objective dynamic optimization problem (MODOP) is achieved. Considering the imprecise nature of decision maker's (DM) judgment, these multiple objectives are viewed as fuzzy variables. By determining intervals for the values of these fuzzy variables, flexible monotonic decreasing or increasing membership functions are determined as the degrees of satisfaction of these fuzzy variables on their intervals. Then, the optimal path planning policy is searched by maximizing the aggregated fuzzy decision values, resulting in a fuzzy multi-objective dynamic optimization problem (FMODOP). Using a suitable t-norm, the FMODOP is converted into a non-linear dynamic optimization problem (NLDOP). By using parametrization method and some calculations, the NLDOP is converted into the sequence of conventional non-linear programming problems (NLPP). It is proved that the solution of this sequence of the NLPPs tends to a Pareto optimal solution which, among other Pareto optimal solutions, has the best satisfaction of DM for the MODOP. Finally, the above procedure as a novel algorithm integrating parametrization method and fuzzy aggregation to solve the MODOP is proposed. Efficiency of our approach is confirmed by some numerical examples.     

Proactive eavesdropping rate maximization with UAV spoofing relay

Spoofing relay is an effective way for legitimate agencies to monitor suspicious communication links and prevent malicious behaviors. The unmanned aerial vehicles(UAVs)-assisted wireless information surveillance system can virtually improve proactive eavesdropping efficiency thanks to the high maneuverability of UAVs. This paper aims to maximize the eavesdropping rate of the surveillance system where the UAV is exploited to actively eavesdrop by spoofing relay technology. We formulate the problem to jointly optimize the amplification coefficient, splitting ratio, and UAV’s trajectory while considering successful monitoring. To make the nonconvex problem tractable, we decompose the problem into three sub-problems and propose a successive convex approximation based alternate iterative algorithm to quickly obtain the near-optimal solution. The final simulation results show that the UAV as an active eavesdropper can effectively increase the information eavesdropping rate than a passive eavesdropper.     

Secured data transmissions in corporeal unmanned device to device using machine learning algorithm

Cyber–physical systems (CPS) for device-to-device (D2D) communications are gaining prominence in today’s sophisticated data transmission infrastructures. This research intends to develop a new model for UAV transmissions across distinct network nodes, which is necessary since an automatic monitoring system is required to enhance the current D2D application infrastructure. The real time significance of proposed UAV for D2D communications can be observed during data transmission state where individual data will have huge impact on maximizing the D2D security. Additionally, through the use of simulation, an exploratory persistence tool is offered for CPS networks with fully characterized energy issues. This UAV CPS paradigm is based on mobility nodes, which host concurrent systems and control algorithms. In sixth-generation networks, when there are no barriers and the collision rate is low and the connectivity is fast, the method is also feasible. Unmanned aerial vehicles (UAVs) can now cover great distances, even while encountering hazardous obstacles. When compared to the preexisting models, the simulated values for autonomous, collision, and parametric reliability are much better by an average of 87%. The proposed model, however, is shown to be highly independent and exhibits stable perceptual behaviour. The proposed UAV approach is optimal for real-time applications due to its potential for more secure operation via a variety of different communication modules.     

UAV caching in 6G networks: A Survey on models,techniques, and applications

The rapid development road map of 6G networks has posed a new set of challenges to both industrial and academic sectors. On the one hand, it needs more disruptive technologies and solutions for addressing the threefold issues including enhanced mobile broadband, massive machine-type communications, and ultra-reliable and low-latency communications. On the other hand, the ever-massive number of mobile users and Internet of Things devices conveys the huge volume of traffic throughout the 6G networks. In this context, caching is one of the most feasible technologies and solutions that does not require any system architecture changes nor costly investments, while significantly improve the system performance, i.e., quality of service and resource efficiency. Ground caching models deployed at macro base stations, small-cell base stations, and mobile devices have been successfully studied and currently extended to the air done by unmanned aerial vehicles (UAVs) to deal with the challenges of 6G networks. This paper provides a comprehensive survey of UAV caching models, techniques, and applications in 6G networks. In particular, we first investigate the entire picture of caching models moving from the ground to the air as well as the related surveys on UAV communications. Then, we introduce a typical UAV caching system and describe how it works in connection with all types of the transceivers, end users, and applications and services (A&Ss). After that, we present the recent advancements and analyses of the UAV caching models and common system performance metrics. Furthermore, the UAV caching with assisted techniques, UAV caching-enabled mechanisms, and UAV caching A&Ss are discussed to demonstrate the role of UAV caching system in 6G networks. Finally, we highlight the ongoing challenges and potential research directions toward UAV caching in 6G networks.     

高功率电磁脉冲对无人飞行器的毁伤破坏

高功率电磁脉冲干扰甚至损毁无人飞行器是应对小型无人飞行器威胁的一种非常有效的办法,高功率电磁脉冲对无人机系统进行干扰和毁伤主要通过前门耦合和后门耦合的方式。完成了高功率电磁脉冲对无人飞行器的毁伤破坏实验,完成了无人机在电磁脉冲源毁伤作用下的目标易损性分析,根据毁伤效果将高频电磁脉冲对无人飞行器毁伤等级分为三级,即干扰级、毁伤级、失效级。分析了电磁脉冲作用的主要目标元件,结果表明,无人飞行器最有可能受干扰的部分为接收机和电子调速器。在相关研究和实际应用时,应着重研究电磁脉冲对无人飞行器的接收机和电子调速器的毁伤破坏,根据接收机和电子调速器内部的电路结构来确定毁伤最佳频率。     

改进蚁群算法的局部信息动态路径规划

针对传统蚁群算法收敛速度慢、对动态路径变化适应性低的局限性,提出了一种基于局部信息获取策略的动态改进型蚁群算法。该算法利用局部信息获取策略,进行最优局部目标点的获取,然后调用改进蚁群算法获取局部区域内的最优路径,再重复循环获取新的最优局部目标点,直到找到全局目标点。与此同时,将提出的改进型蚁群算法应用于动态路径规划中的路径寻优与避障,仿真结果表明：提出的算法在具有与传统蚁群算法相当的路径优化效果的同时,能够有效适应障碍变化、大大提高了路径规划的收敛速度。