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。     

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.     

考虑断点续传的中继卫星调度模型及启发式算法

为提高中继卫星系统的应用效能及数传任务的完成率，在中继卫星调度中考虑了断点续传这一应用模式，即对单个数传任务进行合理拆分，使其在多个时间窗口内完成。首先构建面向断点续传的中继卫星单址天线的调度模型，然后提出一种基于冲突风险评估的冲突度量化方法，并设计考虑断点续传的两阶段调度算法。最后开展大量的仿真实验将该算法与贪婪算法、基于最小冲突度的启发式算法和基于任务优先级的启发式算法这3个不考虑断点续传的算法进行对比。实验结果表明，所提出的算法在任务完成率方面分别提高了7.67%、6.34%和8.67%。     

Evolution analysis of a UAV real-time operating system from a network perspective

With the flourishing development of Unmanned Aerial Vehicles (UAVs), the mission tasks of UAVs have become more and more complex. Consequently, a Real-Time Operating System (RTOS) that provides operating environments for various mission services on these UAVs has become crucial, which leads to the necessity of having a deep understanding of an RTOS. In this paper, an empirical study is conducted on FreeRTOS, a commonly used RTOS for UAVs, from a complex network perspective. A total of 85 releases of FreeRTOS, from V2.4.2 to V10.0.0, are modeled as directed networks, in which the nodes represent functions and the edges denote function calls. It is found that the size of the FreeRTOS network has grown almost linearly with the evolution of the versions, while its main core has evolved steadily. In addition, a k-core analysis-based metric is proposed to identify major functionality changes of FreeRTOS during its evolution. The result shows that the identified versions are consistent with the version change logs. Finally, it is found that the clustering coefficient of the Linux OS scheduler is larger than that of the FreeRTOS scheduler. In conclusion, the empirical results provide useful guidance for developers and users of UAV RTOSs.     

片间综合化互连时间触发通信调度方法

未来先进的分布式综合化航空电子系统需要实现跨越计算体系结构的信息综合,对微小型智能器件间的综合化互连提出要求。通过构建一种具有开放式接口的芯片间综合化互连结构,提出一种相应的时间触发（TT）通信调度方法。首先建立片间综合化互连模型和时间触发流量传输模型,给出负载均衡的选径方法;随后依据传输路径中各级节点各流量的发送时间偏移量计算流量传输的等待时间可行值,利用遗传算法优化调整各芯片发送端口的调度表相位,更新各流量在发送端口的时间偏移量,缩短最坏情况下的最大等待时间,得到具有全局优化意义的时间触发调度表。与使用Yices等SMT形式化求解器的TT调度表生成方法相比,本方法不会出现长时间不停机而无法判定的问题,且不论对于对称或非对称结构,案例研究表明本方法的可调度规模至少增加30%。此外,以流量传输等待时间占周期的比例作为归一化的传输延迟度量,与既有的基于特征任务的调度方法相比,案例研究表明本方法得到的传输延迟最多仅为后者的2%。     

面向任务协同的云端融合计算架构研究

以有人机/无人机自组云为场景,研究了基于云数据中心、有人机和无人机任务协同的云端融合任务协同处理架构,在该架构中,有人机作为边缘计算处理节点,无人机作为端节点。通过分层设计,将基于无人机自组云的云端融合架构自顶往下划分为用户应用层、组件服务层、资源抽象层、操作系统层、物理资源层五个层级,并对各层级进行功能划分设计。最后通过典型实例仿真试验做了架构性验证。     

基于任务聚类的多星观测调度方法

星上传感器侧摆次数有限,在卫星观测调度时进行任务聚类可以节省资源,提高观测效率.提出了动态聚类调度算法(DCSA)来解决多星多轨道圈次的观测调度问题,DCSA把聚类和调度动态结合起来,并使用模拟退火算法搜索全局最优解;分析了在满足分辨率需求前提下多任务聚类的约束条件,并对聚类任务的侧摆角度和时间窗口进行合理优化;根据任...     

Efficient scheduling algorithm for demand-assigned TDMA satellitesystems

An efficient scheduling algorithm (stations burst plan) for demand-assigned time-division multiple-access (TDMA) satellite network systems is introduced. The total demand for transmitting data through a transponder may exceed the available bit-rate capacity, and a scheduler of the system wishes to utilize the system with minimum changes of slot allocations while maximizing throughputs. By implementing such a burst-plan algorithm, transmission of all demanded data traffic can be completed with minimum unused resources (idle slots). The underlying ideas adopted for the algorithm are that jobs with shorter remaining processing times should have higher priorities and that as many jobs are processed at a time as possible. The algorithm is particularly useful for deriving smooth burst plans for a satellite system with a large number of ground stations     

Distortion minimization for multimedia transmission in NOMA HAP-UAV integrated aerial access networks

Aerial access networks have been envisioned as a promising 6G solution to enhance the ground communication systems in both coverage and capacity. To better utilize the spectrum and fully explore different channel characteristics, this paper constructs an integrated network comprising the High Altitude Platform (HAP) and Unmanned Air Vehicles (UAVs) with the Non-Orthogonal Multiple Access (NOMA) technology. In order to improve the transmission quality of images and videos, a power management scheme is proposed to minimize the distortion of the transmissions from the HAP and UAVs to the terminals. The power control is formulated as a non-convex problem constrained by the maximal transmit power and the minimal terminal rate requirements. The variable substitution and the first-order Tailor’s expansion is used to transform it into a sequence of convex problems, which are subsequently solved through the gradient projection method. Simulation demonstrates the signal distortion and error rate improvement achieved by the proposed algorithm.     

基于TMS320DM368 的光电吊舱视频压缩系统设计

无人机光电吊舱视频数据的实时压缩处理是无人机光电吊舱的关键技术之一.以DM368为核心处理器,以嵌入式Linux为板级操作系统,设计出一套可供无人机光电吊舱使用的低功耗视频实时压缩系统.与传统的嵌入式视频H.264软件编码压缩系统相比,本系统依赖DM368内嵌的视频协处理硬核处理H.264编码,具有功耗低、处理延迟小、外部电路简单等诸多优势.同时系统以嵌入式Linux为板级操作系统,实时性强,可稳定高效地管理视频采集、硬核调度、串口通信、数据输出等诸多任务.     

基于WKR结构片上多处理器系统任务分配策略

对现有CMP系统研究现状进行了研究，结合WKR结构模块性良好的特点，设计了基于WKR结构的16核CMP系统框架，在此基础上以提高任务的并行度为目标提出了静态任务层次分配策略。策略分为任务和线程两层分配过程，任务层分配定义任务的并行度，采用LDL胛算法将任务分配到以子WKR结构互连的内核子集上；线程层分配以减少核间通信和均衡核间负载为目标，通过TAGM算法完成任务内的线程在内核子集内的分配。仿真试验表明，静态任务层次分配策略能够实现负载较为均衡的分配，且与类似算法相比具有明显的时间开销优势。     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.     

机载网络时间触发通信调度设计优化与评价方法

机载网络是航空电子系统实现信息传输与功能综合的关键组成,引入时间触发（TT）机制并安排合理通信调度可以有效保证机载网络实时可靠通信,但需要首先面对多种调度设计方法的选择问题。在总结了时间触发通信过程与特征的基础上,以时间触发以太网为分析对象,给出了基于可满足性模理论（SMT）、混合整数规划（MIP）和启发式算法等不同约束引导下的调度模型,并形成了基于强化学习（RL）的调度方法;从调度设计的求解能力与性能保障角度出发,建立了包括求解时间、可求解规模、传输延迟和调度模式的评价指标。以工业规模网络为研究案例对不同调度方法进行了验证分析,在评价指标的引导下对比分析了各种方法的适用场景,其中SMT方法可支持小规模案例,MIP方法具有明显的TT消息传输和调度求解运算优势,以遗传算法为典型的启发式方法和RL方法可实现大规模案例调度,虽然其TT延迟略高于MIP方法,但速率约束消息延迟优化程度分别达到7.97%和12.35%。在此基础上,进一步探讨了未来航电系统机载网络时间触发通信调度设计优化技术的发展趋势。     

Improving multi-target cooperative tracking guidance for UAV swarms using multi-agent reinforcement learning

Multi-Target Tracking Guidance(MTTG) in unknown environments has great potential values in applications for Unmanned Aerial Vehicle(UAV) swarms. Although Multi-Agent Deep Reinforcement Learning(MADRL) is a promising technique for learning cooperation, most of the existing methods cannot scale well to decentralized UAV swarms due to their computational complexity or global information requirement. This paper proposes a decentralized MADRL method using the maximum reciprocal reward to learn cooper...     

时间触发以太网同步及调度机制的研究

说明并分析时间触发以太网（1TrE,Time—Triggered—Ethernet）的时间同步机制,对于采用时间触发机制调度的周期性任务,提出生成周期调度时刻表的方法,利用方法可以根据离线生成的调度时刻表进行调度,也可以动态地将通信任务加入或撤出调度时刻表,调度时刻袁的设计避免时间触发的数据包争用物理链路。构造相应的网络通信场景,通过生成调度时刻表的算例与仿真结果进行了验证。     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

基于Transformer层次预测的多星应急观测任务规划方法

应急观测任务规划是一个强时效性的复杂组合优化问题,必须在规定的时限内完成相应的计算。采用机器学习的方法对规划问题进行初始规划方案预测,可以有效地简化计算复杂度。为此,提出一种基于Transformer层次预测的多星应急观测任务规划方法,将多星任务规划的求解过程分解为3个步骤：首先,利用基于Transformer的任务可调度性预测模型预测待规划任务是否执行,得到预执行任务集合;然后,基于Transformer的任务分配模型对预执行任务集合分配卫星,得到初始规划方案;最后,利用基于随机爬山的约束修正算法对初始规划方案进行优化调整,得到可行规划方案。为验证所提方法的有效性,通过大量仿真实验与CPLEX优化器、标准遗传算法、长短期记忆网络等方法模型进行比较,实验结果表明所提方法计算耗时短,规划收益高,适用于多星观测任务快速规划。     

通信约束下异构多无人机任务分配方法

针对异构多无人机协同执行侦察和打击任务中,存在通信距离、时间延迟等约束条件下的局部任务分配问题,提出了一种基于合同网的分布式多无人机任务分配方法。首先建立了异构集群发现新目标时的局部任务分配问题模型,设计了局部无人机通信网络中的信息一致性算法,实现了任务分配过程中任务发布阶段各无人机的冲突消解。设计了任务分配过程中的联盟构建和无人机资源管理方法,使联盟中各无人机能够以更加平衡的方式消耗资源。仿真结果表明,该方法能够解决通信约束下,异构多无人机执行察打任务时,所触发的针对目标打击任务的任务分配问题,且能够获得最大的系统效能。     

一种多任务驾驶员模型的层次结构

针对现代飞机操作中的驾驶员行为特征．研究了多任务情况下的驾驶员模型。首先对多任务驾驶员行为进行了层次划分．提出了一种基于技能、规则和知识三个层次的驾驶员模型框架结构，分析了各层的功能及模型实现的基本思路。然后，将经典的驾驶员模型与智能控制理论相结合，探讨了一种多任务驾驶员模型底层结构的实现模式。最后，以某型民航飞机为例，对其降落航线进行了仿真。结果表明，模型是合理的。     

基于零空间方法的四旋翼无人机避障与协同编队控制

针对四旋翼无人机在编队飞行执行任务时可能遭遇障碍物问题,考虑多无人机避障及机间避撞的需求,提出 1种基于零空间方法的四旋翼无人机避障与协同编队控制算法。首先,建立四旋翼无人机动力学模型,并建立虚拟控制量简化控制模型;其次,基于零空间方法进行避障与协同编队控制算法研究,将无人机任务执行分解为目标趋向任务、避障避撞任务和协同编队任务,并根据优先级进行任务融合得到期望速度;再次,基于 PID方法设计控制律;最后,通过仿真验证所提控制算法的有效性。所提方法可保证四旋翼无人机在编队飞行中遭遇障碍物时的飞行安全。