Motif-based Intelligent Mission Planning Method for UAV Swarms

Authors

Weiping Wang, School of System Engineering, National University of Defense Technology, Changsha 410073, China;
Wang Tao, School of System Engineering, National University of Defense Technology, Changsha 410073, China;
Xiaobo Li, School of System Engineering, National University of Defense Technology, Changsha 410073, China;
Jiajie Liu, School of System Engineering, National University of Defense Technology, Changsha 410073, China;

Abstract

Abstract: Due to the restriction of communication among UAV swarms, configuration of UAV swarms needs to be changed dynamically along with the task changes. How to do mission planning under limited communication for UAV swarms has become a hot and difficult question. The UAV combat network motif model framework is built based on the function, and six different types of basic motif model are proposed. Through capability demand and communication demand, a task is mapped into types and numbers of motifs which are required to complete the task. The ‘multidimensional dynamic scheduling list’ method is used to automatically generate mission planning schemes. The NSGA-II algorithm is used to automatically select planning schemes towards optimizing the operational time and the quantity of changed connections through changing task execution order. The proposed method is validated through a case study.

Recommended Citation

Wang, Weiping; Tao, Wang; Li, Xiaobo; and Liu, Jiajie (2019) "Motif-based Intelligent Mission Planning Method for UAV Swarms," Journal of System Simulation: Vol. 30: Iss. 4, Article 1.
DOI: 10.16182/j.issn1004731x.joss.201804001
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss4/1

First Page

1211

Revised Date

2018-03-27

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201804001

Last Page

1220

CLC

C935

Recommended Citation

Wang Weiping, Wang Tao, Li Xiaobo, Liu Jiajie. Motif-based Intelligent Mission Planning Method for UAV Swarms[J]. Journal of System Simulation, 2018, 30(4): 1211-1220.

DOI

10.16182/j.issn1004731x.joss.201804001