基于改进人工蜂群算法的多无人机协同任务规划  

Multi-UAV Cooperative Mission Planning Based on Improved Artificial Bee Colony Algorithm

作　　者：刘广瑞[1] 王庆海 姚冬艳 LIU Guangrui;WANG Qinghai;YAO Dongyan(School of Mechanical Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学机械工程学院,河南郑州450001

出　　处：《郑州大学学报（工学版）》2018年第3期51-55,共5页Journal of Zhengzhou University（Engineering Science）

基　　金：国家自然科学基金资助项目(U1304510);郑州大学优秀青年教师发展基金(1421321076)

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.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,the traditional ABC algorithm were improved by introducting 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 mission planning.

关 键 词：无人机 协同 任务规划 动态评价策略 人工蜂群算法 

分 类 号：V279.2[航空宇航科学与技术—飞行器设计] TP242.6[自动化与计算机技术—检测技术与自动化装置]