An aggregate flow based scheduler in multi-task cooperated UAVs network

Unmanned Aerial Vehicles (UAVs) cooperative multi-task system has become the research focus in recent years. However, the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex multi-task scheduling, because they are not able to perceive the different features. In this paper, a novel cooperated UAVs network framework for multi-task scheduling is proposed. It is a three-layer network including a core layer, an aggregation layer and an execution layer, which enhances the efficiency of multi-task distribution, aggregation and transmission. Furthermore, an AggreGate Flow (AGFlow) based scheduler is dedicatedly designed to maximize the task completion rate, whose key point is to aggregate flows belonging to one task during the multi-task transmission of UAVs network and to allocate priority by calculating the urgency-level of each AGFlow. Simulation results demonstrate that, compared with that of state-of-the-art scheduler, the average task completion rate of AGFlow based scheduler is raised by 0.278.     

Cooperative task scheduling for networked uninhabited air vehicles

In the work presented here, we study a cooperative control problem for a network of uninhabited air vehicles (UAVs) where it is assumed that after deployment a set of tasks is given to a group of UAVs and the UAVs must cooperate to decide which UAV should process each task. The cooperation must occur during real-time operation due to a need to repeatedly process each task, in spite of imperfect communications (e.g., messages with random but bounded delays), and the possibility that tasks "pop-up." We show how to view this as a cooperative scheduling problem, and how to derive bounds on mission-level performance metrics. Simulations are used to compare the approach with a noncooperative strategy and to provide design guidelines for the cooperative scheduler.     

一种基于语义分割的机场跑道检测算法

针对复杂电磁作战环境下无人机自主着陆应用场景,提出了一种基于图像语义分割的机场跑道检测算法,并设计构建了轻量高效的端到端跑道检测神经网络RunwayNet.在特征提取部分,使用空洞卷积对ShuffleNet V2进行改造,得到了输出特征图分辨率可调的主干网络,并利用自注意力机制设计了自注意力网络模块,使网络具备全局跑道...     

多无人机协同覆盖路径规划

多无人机协同覆盖路径规划(CPP)由于其并行性和容错能力,对于提高无人机完成侦察、监视、搜索等任务的效率具有重要意义。提出了一种基于无人机任务性能评价和任务区域划分的多无人机协同CPP算法。定量分析了无人机执行覆盖任务的能力,根据无人机及携带成像传感器的性能给出了计算无人机任务性能指数的数学公式;提出了一种基于任务性能和子区域宽度的任务区域划分算法,使无人机的总转弯次数达到最少。仿真结果表明,所提出的CPP算法能够规划出全局最优的多无人机协同覆盖路径。     

自主多无人机的分散化协同控制

协同前提是无人机(UAV)平台间的通信和信息共享,无人机平台之间信息和计算是高度分布的,无人机平台的运动以及通信拓扑的变化,使得集中式协调控制结构很难实现。以最小通信量为基础的分散协同控制具有可扩展性、异构性和动态可重构性等特点,可靠性和鲁棒性较好。针对多无人机平台分散化协同的特点和要求,建立了集中和分散相结合的多无人机平台协同控制系统结构,集中式任务管理系统主要完成目标分配、通信管理和编队管理功能,分散式协同部分主要实现局部任务规划、协调策略及协调控制等功能。分别以多机协同目标跟踪、多机和多编队一致性协调、多机协同编队控制与重构等多无人机平台分散化协同控制技术为应用对象,探讨了分散化协调机制、策略、控制及其与信息之间关系。给出了部分算法的仿真结果。     

基于深度学习的无人机航拍目标检测研究综述

目标检测是提高无人机（UAV）感知能力的关键技术之一,其研究对于无人机的应用有着重要意义。与基于手工特征的传统方法相比,基于卷积神经网络的深度学习方法具有强大的特征学习和表达能力,成为目前目标检测任务的主流算法。近年来,目标检测技术已经在自然场景图像上取得了一系列突破性进展,在无人机领域的研究也逐渐成为热点。首先系统阐述了基于深度学习的目标检测算法的研究进展,并总结了相关算法的优缺点。对常见的航空影像数据集进行了梳理并介绍了迁移学习的方法;从无人机影像背景复杂、目标较小、视场大、目标具有旋转性的特点出发,对无人机目标检测在近期的研究进行了归纳和分析。最后讨论了存在的问题和未来可能的发展方向。     

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。     

超长航时太阳能无人机关键技术综述

超长航时太阳能无人机（UAV）以其高效节能、原理上可实现无限巡航的特点受到广泛关注，而其独特的设计指标与任务特性也对各项关键技术提出了较高要求。多设计要素的高度耦合意味着不同于常规飞行器的总体设计方法，低密度、低速度的飞行条件使其具有明显的低雷诺数气动特性，柔性超大展弦比机翼带来了复杂的气动弹性问题，低翼载荷特性与较大的风场扰动增加了控制难度，极端的飞行环境与苛刻的任务指标对能源、动力系统带来了新挑战，飞行性能对能源系统的高度依赖开辟了飞行轨迹优化的研究方向。本文梳理了超长航时太阳能无人机关键技术的研究现状，在此基础上对各项技术中的难点问题进行了阐释，并对超长航时太阳能无人机未来发展趋势进行了展望。     

Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach

This paper investigates a formation control problem of fixed-wing Unmanned Aerial Vehicle (UAV) swarms. A group-based hierarchical architecture is established among the UAVs, which decomposes all the UAVs into several distinct and non-overlapping groups. In each group, the UAVs form hierarchies with one UAV selected as the group leader. All group leaders execute coordinated path following to cooperatively handle the mission process among different groups, and the remaining followers track their direct leaders to achieve the inner-group coordination. More specifically, for a group leader, a virtual target moving along its desired path is assigned for the UAV, and an updating law is proposed to coordinate all the group leaders’ virtual targets; for a follower UAV, the distributed leader-following formation control law is proposed to make the follower’s heading angle coincide with its direct leader, while keeping the desired relative position with respect to its direct leader. The proposed control law guarantees the globally asymptotic stability of the whole closed-loop swarm system under the control input constraints of fixed-wing UAVs. Theoretical proofs and numerical simulations are provided, which corroborate the effectiveness of the proposed method.     

无人机飞行品质规范浅析

未来无人机的飞行品质规范应是面向任务的飞行品质规范,其出发点是要保证无人机的飞行品质能够顺利完成规定的任务.规范中除了有关无人机的动态特性,特别是有关稳定性和机动性要求之外,还应该对无人机的信息管理、环境感知等提出有针对性的要求.规范不但针对特定的任务科目提供了相应的飞行品质客观评定指标,而且针对特定的任务作业要素提供了评定方法.     

Autonomous flight and navigation of VTOL UAVs: from autonomy demonstrations to out-of-sight flights

Future applications of UAV systems will depend on the aircraft autonomous behavior and decision capabilities. Search and Rescue is one complex possible mission and is here taken as a case study. The ReSSAC project is a multidisciplinary project at ONERA. Its main challenges are related to the architectures and algorithms for autonomous decision and information processing onboard UAVs that perform their mission in cooperation with operators. The feasibility demonstrations and results of the project are intended to be reused and extended in further studies, projects and collaborations. A first step of the project was to develop an autonomous control architecture for our two rotorcraft. In this paper, we present the current status and preliminary achievements of the ReSSAC project, especially some records of past experimental flights with our autonomous aircraft. We further discuss ongoing studies and research perspectives.     

Topology tracking of dynamic UAV wireless networks

Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology infere...     

绕月探测工程北京中心国际联网系统组播问题研究与解决

“嫦娥一号”CE-1任务中，北京航天飞行控制中心与欧洲空间操作中心进行了国际合作。在中心与欧空局的联试过程中，对SLE用户端（SUN服务器）通过组广播进行远程控制时，出现了一台服务器收发正常，另一台却无法正常接收的现象。本文对此故障进行了机理研究，通过分析证明，这是一个不同系统间对IGMP协议的兼容问题，并提出了相应的解决方案。     

重访机制驱动的多无人机协同动目标搜索方法

为实现多无人机高效捕获灰色任务区域内的移动目标,考虑传感器探测概率与虚警概率,提出了重访机制驱动的协同搜索规划（RMD-CSP）方法,以降低目标遗漏与误判概率。考虑无人机飞行性能约束,以最大化任务执行效能为目标建立多无人机协同搜索模型。根据目标先验信息初始化环境搜索信息图（包括目标概率分布图、环境不确定度图与环境搜索状态图）,利用无人机实时探测信息,基于贝叶斯准则持续更新搜索信息图。定制基于环境不确定度更新的重访机制,通过增加长时间未被重访区域的环境不确定度,引导无人机搜索该区域,降低移动目标的遗漏概率;定制基于目标函数权重更新的重访机制,引导无人机快速重访发现新的疑似目标的区域,对疑似目标进行再次确认,减少由于传感器虚警概率造成的目标误判概率。采用滚动时域规划架构,将搜索规划问题分解为一系列短时域规划问题,提升了求解效率。在典型任务想定下,通过数值仿真试验验证了所提方法的有效性。仿真结果表明,RMD-CSP能够在秒级时间内生成每个时域的搜索航迹,相比于光栅式搜索方法与标准的概率启发式搜索方法,能够引导无人机捕获更多的移动目标,同时减少误判次数,有效提升了多无人机协同搜索的任务效能。     

海上无人机多机协同巡检航迹规划方法

随着无人机技术应用的不断深入,如何提高无人机编队的协同能力及在复杂动态环境的自适应性,已成为“集群智能”的 1个重要研究方向。文章对海上无人机多机协同航迹规划整体流程进行了分析,分别阐释了静态和动态场景下的多机协同规划方法。静态场景下主要采用分支定界法建立静态问题模型,为每架无人机划分作业区域、生成作业路径,使得整个巡检作业的航迹长度代价与作业时间代价最小;动态场景下主要针对气象变化、连续跟监、海域变化 3种突发场景,侧重于协同决策和路径规划设计对应的目标函数,采用启发式算法为整个巡检作业进行自适应航迹规划,以确保安全性和效率性。实验结果显示,无人机协同路径规划能够根据环境变化和任务需求动态调整多无人机的巡检路径,快速给出不同突发情况下的最佳动态调整方案,以应对复杂的海上环境并动态规避障碍物。     

NOx emissions of turbofan powered unmanned aerial vehicle for complete flight cycle

Unmanned Aerial Vehicles (UAVs) have been getting more and more popular in both civil and military arena. Similar to manned aircraft, their propulsion systems or engines emit harmful gases such as nitrogen oxides. Since UAVs have different mission profiles and operational parameters than manned aircraft, it is worthy to investigate their NO_x emissions. Therefore, in this study, NO_x emissions of a turbofan powered UAV for complete flight cycle was calculated and optimized within a range of altitude and speed parameters. NO_x emissions were calculated based on ICAO ground test data and corrected to any speed and altitude during flight legs using both Boeing Fuel Flow Method 2 and DLR Fuel Flow Method. Total NO_x emissions were calculated for complete flight cycles for different altitude and speed parameters. Numerical results were presented graphically and additionally optimization studies were conducted. Optimization studies include determination and comparison of speed and altitude for minimum NO_x emissions by the two fuel flow methods and maximum loiter time achievable by UAV.     

面向无人机蜂群的航电云多层任务调度模型

在航空作战体系中，基于航电云的无人机（UAV）蜂群作战是提高未来无人机综合作战能力的一种新模式。针对无人机蜂群作战的航电云架构，如何将云端作战任务派发到无人机且保证作战任务完成时间是其中关键。在无人机蜂群分层分簇网络结构和模块级资源虚拟化的基础上，对传统单层平台级任务调度模型进行改进，提出了一种细化到模块级的多层任务调度模型，将作战任务从云端逐层调度到无人机功能模块上执行。利用OMNeT++对无人机蜂群多层任务调度模型以及传统的单层任务调度模型分别进行仿真，云端以攻击使命组为例构建使命组集进行分配，并对任务吞吐量、消息平均端到端延时和任务完成时间进行性能对比。仿真结果表明：与平台级单层任务调度相比，在执行任务方面，模块级多层任务调度模型将单个任务平均完成时间降低了46.2%，将使命组完成时间降低了52.1%，在保证任务吞吐量的基础上具有对复杂任务更稳定的调度能力；在网络性能方面，模块级多层任务调度模型消息端到端延时更低，延时分布更集中，提高了网络消息传输的实时性。     

轻小型太阳能/氢能无人机发展综述

轻小型无人机（UAV）在军民领域都有着广泛的用途，电动无人机由于其振动低、无污染、无排放等优势，已经成为无人机领域的发展热点。为了提高轻小型电动无人机的航时，清洁、高能量密度的太阳能和氢能成为非常可行的技术途径之一。本文总结了轻小型太阳能、氢能无人机的发展历程；梳理了相关的关键技术，并对太阳能、氢能无人机的总体设计方法和能源动力系统的发展进行了较为深入的探讨；最后，展望了该类无人机的发展趋势，并对所面临的挑战进行了预测。     

基于改进一致性算法的无人机编队控制

编队飞行是指多架无人机保持以一定队形进行飞行的状态,相比于单架飞机执行任务,无人机编队能够增加搜索面积,提高飞机飞行性能,增大完成任务成功率。编队控制是实现编队安全高效完成指定任务的前提。本文以一致性理论为基础,针对无人机运动模型的特点与实际飞行要求,对基本的一致性算法进行改进,提出了改进一致性无人机编队控制算法。首先利用纵向和横侧向解耦的自动驾驶仪模型给出了无人机的三自由度运动方程,根据机动性与飞行性能要求定义了各方向上的加速度、速度与角速度约束。基于一致性理论,将编队控制分为平面与纵向2个方向进行,在状态控制的基础上,利用各状态变量间的几何关系对无人机运动自由度进行转换,加入编队队形信息,设计了编队控制算法。为了使算法生成的指令信号满足约束条件,提出了"最小调整"约束条件处理策略。依据粒子群算法对各无人机的爬升加速度进行优化,以避免机间碰撞。仿真结果表明：提出的编队控制算法具备编队成形与变换功能,能够使无人机编队状态快速收敛到指定值,且保持指定队形,无人机飞行状态满足所有约束条件。