An Air Route Planning Model of Unmanned Aerial Vehicles Under Constraints of Ground Safety

With the rapid growth of the number and flight time of unmanned aerial vehicles(UAVs),safety accidents caused by UAVs flight risk is increasing gradually.Safe air route planning is an effective means to reduce the operational risk of UAVs at the strategic level.The optimal air route planning model based on ground risk assessment is presented by considering the safety cost of UAV air route.Through the rasterization of the ground surface under the air route,the safety factor of each grid is defined with the probability of fatality on the ground per flight hour as the quantitative index.The air route safety cost function is constructed based on the safety factor of each grid.Then,the total cost function considering both air route safety and flight distance is established.The expected function of the ant colony algorithm is rebuilt and used as the algorithm to plan the air routes.The effectiveness of the new air route planning model is verified through the logistical distribution scenario on urban airspace.The results indicate that the new air route planning model considering safety factor can greatly improve the overall safety of air route under small increase of the total flight time.

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles(UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately,this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learningbased heuristic, namely, population based adaptive large neighborhood search(P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size(20 targets)instances in much shorter time.

Optimal confrontation position selecting games model and its application to one-on-one air combat

In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.

基于多指标正交实验设计的UAV编队配系优化

以对抗条件下,无人机(unmanned aerial vehicle,UAV)编队执行对地攻击任务为作战背景,构建多UAV对地攻击模型,利用多指标正交实验设计原理,研究编队配系的优化方法。首先,以武器类型和目标类型为对象,建立对抗双方各自的目标分配模型;其次,以目标探测、识别、杀伤和评估为基本任务,建立编队的攻击过程模型;以任务执行总代价、目标毁伤程度和任务执行时间为评价指标,建立目标函数模型;最后,将UAV的种类和数量视为实验因素和水平,研究基于正交实验设计的编队最优配系方案选择方法。实验结果表明,针对特定的任务要求,该方法可以依据现有的资源条件,得出最优的编队编组策略。

基于正交实验设计的UAV编队配系优化方法

针对多无人机执行对地攻击作战任务的背景,建立了编队对地攻击模型,提出了基于正交实验设计的编队配系优化方法。在考虑多机协同作战因素下,构建了编队目标分配的基本模型;以探测、识别和杀伤为基本过程,构建了攻击过程模型;以空袭方付出代价、防空方毁伤程度和作战效率为依据,构建了目标函数模型,分析了模型中中间变量与目标函数之间的关系;利用正交实验设计原理,研究了编队配系优劣的评价方法。实例分析表明,该方法能够在已有的资源约束条件下,给出编队的最优配系方案。

基于MPEG2的视频捕获在UAV图像处理中的应用

图像处理系统是微型无人驾驶飞行器(UAV)的重要组成部分,而视频捕获则是整个系统的基础。为进一步提高无人机图像处理系统的图像质量,针对微型UAV的固有特点,提出了一种基于MPEG2格式的视频捕获模块构建方法,详细介绍了如何利用DirectShow实现一个MPEG2视频捕获程序。最后给出了捕获程序的运行结果及将其应用于UAV图像处理中的效果。实验证明,这种设计思路具有广泛的适用性,可以解决一般的MPEG2流的捕获问题。

基于无人机及BIM技术的既有建筑裂缝检测及三维展布系统研究

利用无人机进行建筑外立面裂缝检测是一种有效、便捷的方法。通过提取裂缝边界、计算裂缝宽度和长度,并记录该裂缝边界GPS数据,自动形成裂缝参数记录表。建立实物墙体与模型墙体间坐标转换关系,通过坐标转换得到该裂缝在模型墙体中的坐标,并利用裂缝参数化族,完成裂缝在BIM模型中的三维展布。通过无人机裂缝检测系统,快速实现裂缝识别、坐标转换、三维模型展布等,并应用于青岛某老旧小区改造项目。实践表明,该技术适用于城市更新项目建筑外立面裂缝统计及分析。

基于图标识别的微型UAV飞行高度估算

在基于DSP硬件平台的微型UAV视觉系统上,综合运用多种图像预处理算法及随机Hough变换算法,实现了针对特定图标的实时识别。同时通过对摄像头的简单标定,获取了拍摄图像与微型UAV实际飞行状况之间的映射关系。根据所设计的视觉系统在近地面对图标的识别,得到了摄像头与被摄图标的距离。最后通过对实验数据的分析,验证了视觉系统的有效性。

Optimal search for moving targets with sensing capabilities using multiple UAVs

This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.

UAV-DEM法计算土方量精度影响因素及分析

为了验证无人机影像地面分辨率和控制点布设方案对UAV-DEM法计算土方量精度的影响,按照5点、4点、长边、短边布设方案布置控制点,分别获取地面分辨率为10 cm、5 cm和3 cm的无人机影像进行实验,实验表明随着影像分辨率的提高,土方量的计算精度提高,且土方量随分辨的增高呈下降趋势;随着控制点数量的减少土方量计算精度降低,其中4点法和5点法布设时土方计算精度可靠,相对误差相差0.1%。

Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments

Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.

Intermediate carriers for UAV swarms:problem of fleet composition

This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles （mini-UAVs） to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air defense （SEAD） problem is presented to illustrate the proposed algorithm.

基于模糊PID控制的ARUAV姿态角控制器的设计与仿真

针对反辐射无人机(ARUAV)末制导阶段姿态角的控制器采用传统比例积分微分(PID)控制设计的精度不高、响应速度慢、超调量大等问题展开研究,提出一种基于模糊PID的控制方法。该控制方法具有精度高、响应速度快等优点。在Simulink平台上搭建了ARUAV模型,对其俯仰、滚转和航向3个通道进行模糊PID控制,并与经典PID控制进行比较。仿真结果验证了模糊PID控制能够使无人机更快达到稳态,并且能够在较小误差范围内完成对姿态角控制指令的正确响应,验证了该控制方法的有效性和合理性。

一种基于UAV的无人海岛监控网络数据收集策略

针对传统的最小跳路由无线传感器网络(WSN)在数据汇聚上较高的能量开销问题,提出了一种基于无人机(UAV)数据收集的动态分簇算法,其主要思想是利用节点剩余能量来确定那些节点可以当选簇首,同时利用节点坐标位置和设定地分簇半径来划分簇的大小。该算法的优势是能最大程度地均衡每个传感器节点的能量,使整体的节点剩余的能量维持在同一水平。为了提高数据收集的效率,采用蚁群算法规划了无人机数据收集的最短路径。仿真结果表明,与相同的分簇算法下传统的最小跳路由无线传感器网络相比,所提出的基于无人机的无线传感器网络(UAV-WSN)在能量利用率和生命周期方面分别提升了15%和25%,并且以上两种网络的能量利用率高达70%。

基于UAV-RS虚拟仿真系统的教学模式探究

针对无人机和遥感测绘(unmanned air vehicle for remote sensing,UAV-RS)相关专业在实际教学中存在的问题,如设备资源和实验时间无法满足学生需求,野外作业中无人机的安全难以管控,天气和环境条件要求苛刻等,以无人机遥感测绘外业为仿真对象,设计开发了虚拟仿真系统,改革实训教学模式。仿真系统确立了无人机飞行操控、地面场景模拟和遥感图像获取3个模块,引导学生自主完成无人机遥感测绘外业过程。评估实验中经过仿真系统学习的学生(实验组)的项目完成率为:初级90%、中级80%、高级75%,均明显高于对照组。表明基于仿真系统的教学模式可提升UAV-RS的外业教学效果,提高复杂项目完成率,并能够激发学生自主学习的积极性。

基于Agent的UAV智能导航技术研究

UAV智能导航是指在复杂动态环境下,UAV能够通过对周围环境的感知自主进行智能路径优化,减小轨迹中的误差安全到达目的地,同时尽量降低代价。智能导航需要解决自身定位、环境态势感知、导航方式决策、智能管理以及航路引导决策等关键技术,确保能够具有实时可靠的位置、速度、姿态等导航信息以及安全的路径。本文将智能Agent引入到导航系统中,建立了基于Agent的智能导航决策方法,给出了智能导航系统的组成、功能等。在此基础上,提出了一种基于人工势场的航迹规划方法,使得UAV在任意航路点之间进行智能路径规划。

基于无人机巡检与深度学习的河道整治施工进度图像识别

长线性小流域治理工程中的河道衬砌、生态护岸等距离长、范围广、布置分散,且工区交通不便,人工巡检费时费力,难以及时掌握工程的整体施工进度形象面貌。提出了基于无人机巡检与深度学习的河道整治施工进度智能图像识别方法,通过定位施工节点(施工区域起点和终点)的位置计算施工进度。首先,建立了施工区域目标检测模型,针对无人机航拍影像进行河道衬砌护岸施工区域的识别以及施工节点的定位;然后,利用尺度不变特征变换(scale-invariant feature transform,SIFT)算法对不同视频帧中的施工节点进行匹配,并基于单目视觉的运动视差法,计算施工节点的实际工区坐标;最后,计算当前衬砌护岸施工进度,并分析进度偏差。结果表明:该方法得到的施工节点定位平均误差为1.026 m,平均相对误差为0.74%,该方法能够较为准确地从航拍图像中识别得到当前衬砌护岸的施工进程,从而实现长线性工区快速巡检,及时掌控现场施工进度,提高工程管理的智能化水平。

基于无人机与深度学习的少样本混凝土表面裂缝检测方法

混凝土表面裂缝检测是混凝土建筑安全评估和风险预警的重要手段。传统人工检测方法工作量大,且需考虑复杂环境下影响人身安全等因素。基于无人机的裂缝检测方法得到了应用,但当受不确定因素影响时,无人机无法采集足够的训练样本,限制了其检测性能。为此,基于无人机与深度学习,提出少样本条件下混凝土表面裂缝检测方法,采用主流深度学习网络Faster-RCNN和YOLOX,利用WBF算法将检测结果进行融合,有效弥补了像素级标签信息较少导致的检测性能下降。在少样本裂缝图像库及户外场地进行了试验测试,试验结果表明,在少样本条件下基于无人机与深度学习的裂缝检测方法性能得到有效提升,对裂缝检测的准确率达到58.67%。

基于通信信号管制的城市空中交叉口设计研究

为确保城市低空空域内无人机规模化运行安全,缓解低空交通运行瓶颈,本文借鉴地面交通较为成熟的研究成果,结合城市低空及无人机运行特色,提出一种基于通信信号管制的城市空中交叉口设计模型。首先,考虑航路构型设计与航路运行主体,构建无人机运行所需的城市低空航路。其次,从进出口道、冲突响应距离2方面对空中交叉口几何结构进行设计,再从进口航道划分、通信信号管制、无人机高度调整3方面对交通组织进行设计,实现对空中交叉口的基本模型设计。最后,设置仿真实验验证该模型的可行性,并分析不同参数组合下的交叉口综合通行效率。实验结果表明:当飞行速度为18 m/s,纵向间隔为50 m,管制信号周期时长为226 s时,综合通行效率达到最佳。本研究可为未来城市空中交通航路网络精细划设与管理提供支撑,增强低空无人机交通的实践性。

基于需求的城市低空无人机航路网络构建方法

针对城市低空无人机运行需求不断增加的问题,综合考虑城市区域交通需求、空域环境以及无人机性能等限制条件,以最小化网络节点、最大化覆盖需求点和需求量为目标,提出了一种城市低空无人机航路网络构建方法.建立网络节点生成模型和航路网络构建模型;通过改进的greedy算法生成网络节点;结合使用k近邻(k-Nearest Neighbor,K-NN)和Prims算法构建无人机航路网络;选取上海地区物流业务需求量数据进行仿真验证,并运用复杂网络相关指标分析所构建的航路网络性能.结果表明:本文提出的城市低空无人机航路网络构建方法可以同时实现干线和支线航路网络的构建,且干线航路网络实现了网络全连通,还可以结合无人机任务需求设置网络节点实现航路网络灵活规划.