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

doi:10.7527/S1000-6893.2019.23183

航空学报 >

2019 , Vol. 40 >Issue 11: 323183 - 323183

DOI: https://doi.org/10.7527/S1000-6893.2019.23183

电子电气工程与控制

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

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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

网络出版日期: 2019-09-02

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）

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Avionics cloud multi-layer task scheduling model for UAV swarm

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

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

Received date: 2019-05-24

Revised date: 2019-07-09

Online 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)

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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

本文引用格式

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 . DOI: 10.7527/S1000-6893.2019.23183

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

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