面向无人机蜂群的航电云多层任务调度模型

doi:10.7527/S1000-6893.2019.23183

电子电气工程与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

面向无人机蜂群的航电云多层任务调度模型

王荣巍, 何锋, 周璇, 鲁俊, 李二帅

北京航空航天大学电子信息工程学院, 北京 100083

收稿日期:2019-05-24 修回日期:2019-07-09 出版日期:2019-12-03 发布日期:2019-09-02
通讯作者: 何锋 E-mail:robinleo@buaa.edu.cn
基金资助:
国家自然科学基金（61301086）；装备预研领域基金（61403120404），中国民航大学天津市民用航空器适航与维修重点实验室开放基金（2017SW02）

Avionics cloud multi-layer task scheduling model for UAV swarm

WANG Rongwei, HE Feng, ZHOU Xuan, LU Jun, LI Ershuai

School of Electronics and Information Engineering, Beihang University, Beijing 100083, China

Received:2019-05-24 Revised:2019-07-09 Online:2019-12-03 Published:2019-09-02
Supported by:
National Natural Science Foundation of China (61301086); Equipment Pre-Research Field Foundation (61403120404); the Civil Aircraft Airworthiness and Maintenance Key Laboratory Fund of Civil Aviation University of China (2017SW02)

摘要/Abstract

摘要： 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。

关键词: 无人机蜂群, 航电云, 任务调度, 分层分簇, 任务吞吐量

Abstract: In the aviation combat system, Unmanned Aerial Vehicle (UAV) swarm based on avionics cloud is a new model for improving the overall combat capability of future UAV. For the avionics cloud architecture of UAV swarm, how to distribute the cloud combat mission to the UAV and ensure the punctuality of mission completion is the key. Based on the hierarchical clustering network structure and module-level resource virtualization of UAV swarm, this paper improves the traditional single-layer platform-level task scheduling model, and proposes a multi-layer task scheduling model that is refined to the module level. The mission is scheduled from the cloud to the UAV function module. OMNeT++ is used to simulate the multi-layer mission scheduling model of UAV swarm and the traditional single-layer task scheduling model. The cloud uses the attack mission group as an example to construct the mission group for allocation and performance comparison of task throughput, message average end-to-end delay, and task completion time. The simulation results show that compared with the platform-level single-layer task scheduling, the module-level multi-task scheduling model reduces the average completion time of a single task by 46.2% and the mission group completion time by 52.1%. In addition to guarantee of throughput, the model proposed has more stable scheduling capability for complex tasks. In terms of network performance, the module-level multi-layer task scheduling model has lower end-to-end delay and more concentrated delay distribution, which improves the real-time performance of network message transmission.

Key words: Unmanned Aerial Vehicle (UAV) swarm, avionics cloud, task scheduling, hierarchical clustering, task throughput

中图分类号:

王荣巍, 何锋, 周璇, 鲁俊, 李二帅. 面向无人机蜂群的航电云多层任务调度模型[J]. 航空学报, 2019, 40(11): 323183-323183.

WANG Rongwei, HE Feng, ZHOU Xuan, LU Jun, LI Ershuai. Avionics cloud multi-layer task scheduling model for UAV swarm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(11): 323183-323183.

参考文献 26

[1]	LI Z, LI Q, XIONG H G. Avionics clouds:A generic scheme for future avionics systems[C]//Digital Avionics Systems Conference. Piscataway, NJ:IEEE Press, 2012:6E4-1-6E4-10.
[2]	屠晓杰. 航空电子云的系统设计与性能分析关键技术研究[D]. 北京:北京航空航天大学, 2013. TU X J. Research on key technologies in avionics clouds design and performance analysis[D]. Beijing:Beihang University, 2013(in Chinese).
[3]	鲁俊, 何锋, 熊华钢. 航空电子云系统架构与网络[J]. 航空电子技术, 2017, 48(3):1-9. LU J, HE F, XIONG H G. System and network architecture of avionics cloud[J]. Avionics Technology, 2017, 48(3):1-9(in Chinese).
[4]	KIM D H, SONG S, SHIN S J, et al. An extended cloud computing architecture for immediate sharing of avionic contents[C]//Advanced Communication and Networking. Berlin, Heidelberg:Springer, 2011:439-446.
[5]	JASTI A, MOHAPATRA S, POTLURI B, et al. Cloud computing in aircraft data network[C]//Integrated Communications, Navigation and Surveillance Conference (ICNS). Piscataway, NJ:IEEE Press, 2011:E7-1-E7-8.
[6]	钮伟, 黄佳沁, 缪礼锋. 无人机蜂群对海作战概念与关键技术研究[J]. 指挥控制与仿真, 2018, 40(1):20-27. NIU W, HUANG J Q, MIU L F. Research on the concept and key technologies of unmanned aerial vehicle swarm concerning naval attack[J]. Command Control & Simulation, 2018, 40(1):20-27(in Chinese).
[7]	赵尚弘, 陈柯帆, 吕娜, 等. 软件定义航空集群机载战术网络[J]. 通信学报, 2017, 38(8):140-155. ZHAO S H, CHEN K F, LYU N, et al Software defined airborne tactical network for aeronautic swarm[J]. Journal on Communications, 2017, 38(8):140-155(in Chinese).
[8]	吕娜, 刘创, 陈柯帆, 等. 一种面向航空集群的集中控制式网络部署方法[J]. 航空学报, 2018, 39(7):321961. LYU N, LIU C, CHEN K F, et al. A method for centralized control network deployment of aeronautic swarm[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(7):321961(in Chinese).
[9]	夏元清, 闫策, 王笑京, 等. 智能交通信息物理融合云控制系统[J]. 自动化学报, 2019, 45(1):132-142. XIA Y Q, YAN C, WANG X J, et al. Intelligent transportation cyber-physical cloud control systems[J]. Acta Automatica Sinica, 2019, 45(1):132-142(in Chinese).
[10]	王国庆, 谷青范, 王淼, 等. 新一代综合化航空电子系统构架技术研究[J]. 航空学报, 2014, 35(6):1473-1486. WANG G Q, GU Q F, WANG M, et al. Research on the architecture technology for new generation integrated avionics system[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(6):1473-1486(in Chinese).
[11]	李振, 李峭, 熊华钢. 基于使命任务分解的航空电子跨平台通信组织与仿真[J]. 航空电子技术,2015,46(1):10-14. LI Z, LI Q, XIONG H G. Organization and simulation of cross-platform communication of avionics based on mission decomposition[J]. Avionics Technology, 2015, 46(1):10-14(in Chinese).
[12]	Federal Aviation Administration. Task analysis tools used throughout development[M]. Washington, D.C.:FAA, 2008.
[13]	廖沫, 陈宗基, 周锐. 基于MAS的多UAV协同任务分配设计与仿真[J]. 系统仿真学报, 2007, 19(10):2313-2317. LIAO M, CHEN Z J, ZHOU R. Design and simulation of multi-UAV coordinated task allocation based on MAS[J]. Journal of System Simulation, 2007, 19(10):2313-2317(in Chinese).
[14]	赵振宇, 卢广山, 廖沫, 等. 基于DTC和GPGP的多UCAV任务规划方法[J]. 北京航空航天大学学报, 2011, 37(3):305-310. ZHAO Z Y, LU G S, LIAO M, et al. Mission planning of multiple UCAV based on DTC and GPGP[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(3):305-310(in Chinese).
[15]	魏政磊, 赵辉, 黄汉桥, 等. 基于SAGWO算法的UCAVs动态协同任务分配[J]. 北京航空航天大学学报, 2018, 44(8):1651-1664. WEI Z L, ZHAO H, HUANG H Q, et al. Dynamic UCAVs cooperative task allocation based on SAGWO algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(8):1651-1664(in Chinese).
[16]	BRUNET L, CHOI H L, HOW J. Consensus-based auction approaches for decentralized task assignment[C]//AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, VA:AIAA, 2008:6839.
[17]	KIM D, LEE J. Joint mission assignment and location management for UAVs in mission-critical flying ad hoc networks[C]//2018 International Conference on Information and Communication Technology Convergence (ICTC). Piscataway, NJ:IEEE Press, 2018:323-328.
[18]	SHIMA T, RASMUSSEN S J, SPARKS A G, et al. Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms[J]. Computers & Operations Research, 2006, 33(11):3252-3269.
[19]	谭何顺, 曹雷, 彭辉, 等. 一种多无人机层次化任务分配方法[J]. 解放军理工大学学报(自然科学版), 2014, 15(1):18-24. TAN H S, CAO L, PENG H. Method of multi-UAV hierarchical task allocation[J]. Journal of PLA University of Science and Technology:Natural Science, 2014, 15(1):18-24(in Chinese).
[20]	苏菲, 陈岩, 沈林成. 基于蚁群算法的无人机协同多任务分配[J]. 航空学报, 2008, 29(S1):184-191. SU F, CHEN Y, SHEN L C. UAV cooperative multi-task assignment based on ant colony algorithm[J]. Acta Aeronauticaet et Astronautica Sinica, 2008, 29(S1):184-191(in Chinese).
[21]	杨媛琦. 基于负载均衡的Multi-UAV任务分配算法的研究[D]. 哈尔滨:哈尔滨工业大学, 2016. YANG Y Q. Research of multi-UAV load-balanced task planning algorithm[D]. Harbin:Harbin Institute of Technology, 2016(in Chinese).
[22]	杜继永, 张凤鸣, 杨骥, 等. 多UCAV协同任务分配模型及粒子群算法求解[J]. 控制与决策, 2012, 27(11):1751-1755. DU J Y, ZHANG F M, YANG J, et al. Cooperative task assignment for multiple UCAV using particle swarm optimization[J]. Control and Decision, 2012, 27(11):1751-1755(in Chinese).
[23]	冉华明, 周锐, 董卓宁, 等. 空战中协同干扰、探测、攻击任务分配[J]. 北京航空航天大学学报, 2015, 41(5):911-918. RAN H M, ZHOU R, DONG Z N, et al. Cooperative jamming-detecting-attacking task allocation in air combat[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(5):911-918(in Chinese).
[24]	史恒亮. 云计算任务调度研究[D]. 南京:南京理工大学, 2012. SHI H L. Research of job scheduling on cloud computing[D]. Nanjing:Nanjing University of Science and Technology, 2012(in Chinese).
[25]	邓见光. 云计算任务调度策略研究[D]. 广州:华南理工大学, 2014. DENG J G. Research on task scheduling strategy of cloud computing[D]. Guangzhou:South China University of Technology, 2014(in Chinese).
[26]	HORNIG R. An overview of the OMNeT++ simulation environment[C]//International Conference on Simulation TOOLS and Techniques for Communications, Networks and Systems & Workshops, 2008:60.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

面向无人机蜂群的航电云多层任务调度模型

Avionics cloud multi-layer task scheduling model for UAV swarm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 26

相关文章 4

编辑推荐

Metrics

本文评价

[1]	赵长啸, 戴骏, 董方正, 李道俊. 机载时间敏感网络链路安全关键度均衡调度方法[J]. 航空学报, 2024, 45(6): 328870-328870.
[2]	路辉, 陈晓, 刘欣, 邓小乐. 基于图禁忌的并行测试任务调度算法[J]. 航空学报, 2011, 32(9): 1669-1677.
[3]	付新滑;肖明清;刘万俊;周越文 . 一种新的并行测试任务调度算法[J]. 航空学报, 2009, 30(12): 2363-2370.
[4]	路辉;李昕. 一种基于分枝定界法的串行测试任务调度算法[J]. 航空学报, 2008, 29(1): 131-135.