Flight safety measurements of UAVs in congested airspace

Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles (UAVs) in a complex environment. A probabilistic approach is proposed to measure safety situation in congested airspace. The occupancy distribution of the airspace is represented with conflict probability between spatial positions and UAV. The concept of a safety envelope related to flight performance and response time is presented first instead of the conventional fixed-size protected zones around aircraft. Consequently, the conflict probability is performance-dependent, and effects of various UAVs on safety can be distinguished. The uncer-tainty of a UAV future position is explicitly accounted for as Brownian motion. An analytic approximate algorithm for the conflict probability is developed to decrease the computational consumption. The relationship between safety and flight performance are discussed for different response times and prediction intervals. To illustrate the applications of the approach, an experi-ment of three UAVs in formation flight is performed. In addition, an example of trajectory planning is simulated for one UAV flying over airspace where five UAVs exist. The validation of the approach shows its potential in guaranteeing flight safety in highly dynamic environment.     

穿越微下冲气流的飞翼布局无人机控制方法

微下冲气流是最危险的低空风切变形式,为在起降阶段安全穿越该气流,飞翼布局的无人机控制律应具有快速响应能力和良好的鲁棒性。针对大展弦比飞翼布局无人机舵面附加升力大和低速状态俯仰操纵效能低的特点,提出了舵面附加升力和机体气动力相结合的复合控制方案,改进了以输出误差为参考量的非线性指令分配策略,设计了基于迎角保护的指令分配策略。将风干扰和模型的不确定性视为未知扰动,采用自抗扰控制(ADRC)理论设计飞翼布局无人机非线性控制律,使之对风干扰和模型的不确定性进行估计补偿。仿真结果表明,复合控制与ADRC相结合的方法加速了航迹倾角的单位阶跃响应速度,使上升时间缩短了64%,同时能够实现对风干扰的有效观测和补偿,使高度损失低于2m;能够在风切变中有效保护迎角,使其维持在5.5°以内。因此,该方法能够为飞翼布局无人机安全平稳地穿越微下冲气流提供一种参考方案。     

基于序列图像的无人机自测速方法与试验

为自主、精确测量无人机的飞行速度,研究并发展了一种基于序列图像的无人机自测速方法并进行了相应的试验。该方法利用无人机上已有的惯导装置、高度计和摄像机,在连续成像的条件下,通过匹配跟踪得到地面同名点在相邻两帧光学或红外实时图中的位置,利用飞行高度、姿态信息和成像帧频计算得到无人机的瞬时飞行速度。在无人机的匀速平飞段,通过大量数据拟合得到高精度的平均飞行速度。通过挂飞试验对方法进行了验证,实时得到了小于0.2 m/s的测速结果,满足工程要求的精度,为工程应用打下了一定的基础。     

装载约束下飞翼布局气动与隐身综合设计

基于飞翼布局大鼓包式机身,在装载布置约束下开展了气动与隐身综合设计,提出了一种机身低鼓包和双鼓包模型。结合可靠的CFD和隐身计算方法,初步探索了低鼓包和双鼓包气动与隐身特性及机理。结果表明:双鼓包模型极大地降低了全机纵向基本气动性能,但在大迎角时会推迟外翼段前缘分离,对外翼气动特性有一定改善;低鼓包与双鼓包模型整体上RCS差异较小,但双鼓包模型侧向隐身性能更优异。     

Optimization of bits allocation and path planning with trajectory constraint in UAV-enabled mobile edge computing system

In this paper, an Unmanned Aerial Vehicle (UAV) enabled Mobile Edge Computing (MEC) system is studied, in which UAV acts as server to offer computing offloading service to the Mobile Users (MUs) with limited computing capability and energy budget. We aim to minimize the total energy consumption of MUs by jointly optimizing the bit allocation for uplink, computing at the UAV and downlink, along with the UAV trajectory in a unified framework. To this end, a trajectory constraint model is employed to avoid sudden changes of velocity and acceleration during flying. Due to high-order information in use, we lead to a more reasonable nonconvex optimization problem than prior arts. An Alternating Direction Method of Multipliers (ADMM) method is introduced to solve the optimization problem, which is decomposed into a set of easy sub-problems, to meet the requirement on the efficiency in edge computing. Numerical results demonstrate that our approach leads a smoother UAV trajectory, significantly save the energy consumption for UAV during flying.     

小型无人机纵向飞行品质研究

目前针对无人机的飞行品质评估标准缺失,都是参照有人机飞行品质准则对无人机进行评价,而且评价的标准也不统一。通过对某小型无人机增稳前后的性能进行分析对比,验证了有人机飞行品质标准中的CAP准则和带宽准则对小型无人机的适用性,进而提出了小型无人机纵向飞行品质评定方法,对无人机飞行品质的研究具有一定的借鉴作用。     

曲面叶片三维工作变形连续扫描激光测试

提出一种曲面叶片三维工作变形连续扫描激光多普勒振动测试方法并应用于某压气机叶片的振动测试。将图像几何变换引入激光连续扫描,发展了一种适合曲面叶片这类非规则几何结构的激光连续扫描路径算法,使连续扫描激光振动测试应用于非矩形区域,并通过实验验证了其准确性。基于此测试方法,提出了曲面叶片三维工作变形的测试方法,研究了三维激光测试坐标分解原理。对某压气机叶片进行了三维激光连续扫描测试,获取了3600Hz内的前9阶模态,与商用三维激光离散点扫描测试模态振型的相关性基本在0.95以上,验证了三维连续扫描激光多普勒振动测试的可行性和准确性。连续扫描激光多普勒测试的效率高、测点密集,对进一步工程应用具有重要意义。      

弹性飞翼无人机鲁棒姿态控制设计

针对大展弦比飞翼布局无人机的刚体运动与弹性运动耦合动力学模型,提出了一种基于反步法的鲁棒非线性控制方法。该方法考虑了飞翼无人机的非线性因素,将动态面反步控制作为内环控制抵消系统的非线性因素;同时考虑系统实际存在的弹性耦合项、参数不确定项以及外部扰动,将内环反步控制与飞翼无人机模型整体作为新的被控对象,引入最优化理论对新的被控对象设计了外环鲁棒控制器。仿真结果表明,所提出的控制器不仅满足飞翼无人机姿态跟踪性能的要求,且对模型不确定性和气动弹性影响具有鲁棒性。     

Proactive optimization of transmission power and 3D trajectory in UAV-assisted relay systems with mobile ground users

In this paper, an Unmanned Aerial Vehicle (UAV)-assisted relay communication system is studied, where a UAV is served as a flying relay to maintain a communication link between a mobile source node and a remote destination node. Specifically, an average outage probability minimization problem is formulated firstly, with the constraints on the transmission power of the source node, the maximum energy consumption budget, the transmission power, the speed and acceleration of the flying UAV relay. Next, the closed-form of outage probability is derived, under the hybrid line-of-sight and non-line-of-sight probability channel model. To deal with the formulated nonconvex optimization, a long-term proactive optimization mechanism is developed. In particular, firstly, an approximation for line-of-sight probability and a reformulation of the primal problem are given, respectively. Then, the reformulated problem is transformed into two subproblems: one is the transmission power optimization with given UAV’s trajectory and the other is the trajectory optimization with given transmission power allocation. Next, two subproblems are tackled via tailoring primal–dual subgradient method and successive convex approximation, respectively. Furthermore, a proactive optimization algorithm is proposed to jointly optimize the transmission power allocation and the three-dimensional trajectory. Finally, simulation results demonstrate the performance of the proposed algorithm under various parameter configurations.     

航拍视频帧间快速配准算法

 为应对相机运动的影响,提出了一种快速有效的无人机(UAV)视频相邻帧图像配准算法。通过空间分布约束和角点量限制来筛选有效的FAST特征点,引入自适应阈值提高特征点检测的环境适应性,采用训练得到的不相关采样点集对特征点进行二值描述,以获得准确快速的特征描述,并通过最近邻算法根据汉明距离获得特征匹配对,最后运用RANSAC方法得到帧间仿射变换模型参数,消除相机运动带来的影响,为后续运动目标检测与跟踪提供保障。实验结果表明该算法快速、稳定,具有较高的环境适应性,能够满足无人机系统视频图像配准的要求。     

Optimization of beamforming and path planning for UAV-assisted wireless relay networks

Recently, unmanned aerial vehicles （UAVs） acting as relay platforms have attracted considerable attention due to the advantages of extending coverage and improving connectivity for long-range communications. Specifically, in the scenario where the access point （AP） is mobile, a UAV needs to find an efficient path to guarantee the connectivity of the relay link. Motivated by this fact, this paper proposes an optimal design for beamforming （BF） and UAV path planning. First of all, we study a dual-hop amplify-and-forward （AF） wireless relay network, in which a UAV is used as relay between a mobile AP and a fixed base station （BS）. In the network, both of the AP and the BS are equipped with multiple antennas, whereas the UAV has a single antenna. Then, we obtain the output signal~to-noise ratio （SNR） of the dual-hop relay network. Based on the criterion of maximizing the output SNR, we develop an optimal design to obtain the solution of the optimal BF weight vector and the UAV heading angle. Next, we derive the closed-form outage probability （OP） expression to investigate the performance of the dual-hop relay network conveniently. Finally, computer simulations show that the proposed approach can obtain nearly optimal flying path and OP performance, indicating the effectiveness of the proposed algorithm. Furthermore, we find that increasing the antenna number at the BS or the maximal heading angle can significantly improve the performance of the considered relay network.     

基于自组网策略的多机编队与防撞任务研究

近年来多旋翼无人机编队飞行技术取得了巨大的突破,将无人机技术与自组网技术相结合将会促进无人机集群技术的发展。为了促进无人机编队飞行技术的发展,以5架六旋翼无人机为研究对象,重点研究了基于自组网策略的多旋翼无人机队形组成、队形保持、队形重构以及飞行过程中的避碰策略,设计了数据闭环的地面编队控制软件并进行试飞验证。测试结果表明,该数据闭环地面编队控制软件稳定可靠,实现了编队队形组成、保持、重构以及在队形变换过程中的机间避碰等功能,对多旋翼无人机编队飞行工程化实现具有一定的参考价值。     

基于神经网络补偿动态逆的飞翼布局无人机姿态控制方法

将动态逆控制技术应用于飞翼式布局无人机的姿态控制回路,以适应飞翼布局无人机控制系统要求。介绍了动态逆控制器解耦控制原理,以及神经网络补偿结构的作用和设计方法,并基于无人机非线性姿态运动学和动力学模型设计了基于神经网络补偿的动态逆控制器。在强耦合、强非线性的飞翼布局无人机模型上,通过数学仿真验证了系统具有良好的动态性能和稳态特性,控制器具有很强的鲁棒性。     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.     

飞翼布局无人机的稳定与操纵特性分析研究

从气动和控制综合设计角度,提出了解决飞翼布局无人机稳定能力问题的多轴静不稳定增稳控制规律,通过对多操纵面操纵特性和操纵需求分析给出了操纵面的配置策略.仿真分析表明,所提出的设计方法较好地解决了飞翼布局无人机特殊的稳定与操纵问题.     

一种基于亚像素匹配的高精度视觉定位方法

大多数应用到无人机景象匹配导航中的匹配算法都只能满足像素级精度,且当无人机利用景象匹配定位时图像之间往往存在较大的视觉差异,这种情况下图像匹配精度低且性能会急剧下降。所以选用具有仿射不变特性的特征点进行匹配,结合高斯亚像素拟合原理及特征描述符简化方法,并采用渐进采样一致性(PROSAC)算法剔除误匹配点,最终实现亚像素级的景象匹配定位。实验结果表明,采用此方法可以大大提高图像匹配的精度,提高到0.05像素以内。     

无人驾驶飞行器的气动特点和设计

首先论述了发展无人驾驶飞行器(UAV)的重要性,集中介绍了2种UAV的平台,包括:高空长航时UAV和无人战斗机(UCAV)。讨论了这2种飞行器空气动力特点和气动设计的主要问题。对于高空长航时UAV设计中要重点发展的专用翼型的设计,介绍了一种新型的杂交设计思想和提出了基于CFD技术的多目标多点优化设计的工具。讨论了此类飞行器三维机翼设计的基本要求和三维后掠自然层流机翼的设计。UCAV设计中强调了气动/隐身综合设计的重要性,讨论了无尾布局时新型先进气动控制和矢量推进相结合的必要和难点。     

无人机磁航向测量的自动罗差补偿研究

 利用地球磁场测量无人机的航向时,需要对机上铁磁材料引起的罗差进行补偿。为了降低补偿费用,减小补偿试验时周围环境的影响,提出一种利用飞机左右盘旋飞行时采样数据实现罗差自动补偿的方法。采用椭圆假设算法,可利用飞机平飞时在多于5个不同方向的采样数据来自动补偿平飞时的罗差。在任意姿态飞行时,把飞机左右盘旋时采样的数据分解为4个椭圆,并求出它们相对于椭圆假设的24个系数。再利用这24个系数和飞机的俯仰角、倾斜角以及地磁场垂直分量求出任意姿态下罗差补偿所需的12个系数。实验结果表明,该方法效果良好,方便可行。某无人机补偿前最大误差为21.5°;用传统方法补偿后最大误差为2.3°;用本文方法几乎不需要额外的费用,补偿后最大误差为1.6°。     

基于场景识别的惯性基类脑导航方法

针对无人机导航中惯性器件产生的漂移误差不断随时间累积进而影响导航精度的问题,以仿生类脑导航为研究背景,提出了一种新的导航方法。与传统导航策略不同的是,该方法从周期性校正累积位置误差的角度出发,采用设置位置细胞节点的思路,利用训练好的卷积神经网络模型在细胞节点处进行图像匹配,从而在位置细胞节点处实现惯导位置漂移误差校正。同时,建立节点之间的漂移误差模型,调整误差方程系数,以达到修正漂移误差的目的。最后,基于无人机飞行实验结果验证了该方法对自主导航的有效性和鲁棒性,该方法能够有效提高无人机导航的精度。     

Simulation Environment for Machine Vision Based Aerial Refueling for UAVs

The design of a simulation environment is described for a machine vision (MV)-based approach for the problem of aerial refueling (AR) for unmanned aerial vehicles (UAVs) using the USAF refueling method. MV-based algorithms are implemented within the proposed scheme to detect the relative position and orientation between the UAV and the tanker. Within this effort, techniques and algorithms for the visualization the tanker aircraft in a virtual reality (VR) setting, for the acquisition of the tanker image, for the feature extraction (FE) from the acquired image, for the feature matching (FM) of the features, for the tanker-UAV pose estimation (PE) have been developed and extensively tested in closed-loop simulations. Detailed mathematical models of the tanker and UAV dynamics, refueling boom, turbulence, wind gusts, and tanker's wake effects, along with the UAV docking control laws have been implemented within the simulation environment. This paper also presents the results of a study relative to the use of passive markers versus feature extraction for the problem of estimating in real time the UAV-tanker relative position and orientation vectors.