[1]刘广瑞,王庆海,姚冬艳.基于改进人工蜂群算法的多无人机协同任务规划[J].郑州大学学报(工学版),2018,39(03):51-55.[doi:10.13705/j.issn.1671-6833.2017.06.026]
　Liu Guangrui,Wang Qinghai,Yao Dongyan.Multi-UAV Cooperative Mission Planning Based on Improved Artificial Bee Colony Algorithm[J].Journal of Zhengzhou University (Engineering Science),2018,39(03):51-55.[doi:10.13705/j.issn.1671-6833.2017.06.026]
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基于改进人工蜂群算法的多无人机协同任务规划()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
39
期数:
2018年03期
页码:
51-55
栏目:
出版日期:
2018-05-10

文章信息/Info

Title:
Multi-UAV Cooperative Mission Planning Based on Improved Artificial Bee Colony Algorithm
作者:
刘广瑞; 王庆海; 姚冬艳
郑州大学机械工程学院,河南郑州,450001
Author(s):
Liu Guangrui;  Wang Qinghai;  Yao Dongyan
School of Mechanical Engineering, Zhengzhou University, Zhengzhou, Henan 450001
关键词:
无人机; 协同; 任务规划; 动态评价策略; 人工蜂群算法
Keywords:
unmanned aerial vehicle; scoordination; mission planning; dynamic evaluation selection strategy; artificial bee colony(ABC) algorithm
DOI:
10.13705/j.issn.1671-6833.2017.06.026
文献标志码:
A
摘要:
多无人机协同任务规划是多无人机协同作战的关键。针对无人机信息共享、多任务能力等特点提高了任务规划难度，考虑战场威胁分布、目标任务时序、无人机续航时间等因素，建立了多无人机协同执行多目标的多任务规划数学模型。通过引入动态评价选择策略、引入Metropolis 准则等方式提出改进人工蜂群算法（IABC）对该模型求解。通过对多无人机协同任务规划模型进行求解分析，验证了该模型和规划算法的正确性和有效性。
Abstract:
Multi-UAV cooperative mission planning was the key to multi-UAV cooperative combat. UAVs could share information with others and tackle tasks, which make it difficult to plan mission. In this paper, considering threat distribution, task sequence restriction and time of endurance, a mission planning mathematical model of multi-UAV cooperative mission planning was developed. To increase mission planning efficiency, limitations of traditional ABC algorithm were improved by introduction of dynamic evaluation selection strategy, introduction of metropolis rule , etc. The correctness and effectiveness of proposed method were validated by the calculation and analysis for multi-UAV cooperative missionplanning. 

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更新日期/Last Update: 2018-05-03