摘要
为了降低多无人机协同的威胁代价,提高无人机群的环境生存率和任务成功率,以多无人任务机和无人支援机航迹协同为研究对象,描述时间与空间协同叠加、航程与威胁变量决策寻优的多无人机航迹协同实现过程。考虑任务航程与任务威胁的综合影响,提出多无人机航迹分段时序协同方法与协同模型。引入航程与威胁决策变量定义航迹代价协同函数,并构建基于近点与远点决策变量的协同函数模型,通过优化决策变量,实现多无人机最小航迹代价的路径寻优过程。为了满足航迹协同的实时性需求,提出利用自适应交叉率和变异率相结合的自适应变异遗传算法模型,实现航迹寻优过程的快速求解。通过半物理仿真试验,验证基于决策变量与改进遗传算法的多无人机航迹协同模型的有效性和实时性,可指导实际系统的构建与实现。
In order to reduce the threat cost of multi-UAV coordination and to improve the environment survival rate and mission success rate of UAV fleet,a track coordination of multi UAV and UAV support helicopter is studied. Multi-UAV track coordination realization process including the time and space coordination overlaying,flight path and threat variable decision-making optimization is described,range and threat variables decision optimization. The combined effect of mission flight path and mission threat is considered,multi-UAV track segmentation time sequence coordination method and coordination model are proposed. Flight path and threat decision-making variables are introduced to define the track cost coordination function,and the coordination function model based on near-point and far-point decision-making variables is established,by optimizing decision variables,the Path optimization process of multi-UAV with minimum track cost is realized. In order to meet the real-time requirement of track coordination,an adaptive mutation genetic algorithm model combining adaptive crossover rate and mutation rate is proposed to solve the track optimization process quickly. Finally,the effectiveness and real-time of the multi-UAV flight path coordination model based on decision-making variables and improved genetic algorithm are verified by semi-physical simulation experiment,which can guide the construction and implementation of the actual system.
作者
张劼
李宁洲
张晓娟
卫晓娟
ZHANG Jie;LI Ningzhou;ZHANG Xiaojuan;WEI Xiaojuan(College of Science,Xijing University,Xi’an 710123,China;Shanghai Institute of Technology,Shanghai 201418,China)
出处
《火力与指挥控制》
CSCD
北大核心
2022年第11期18-23,共6页
Fire Control & Command Control
基金
国家自然科学基金(51665027)
甘肃省自然科学基金(20JR5RA406)
甘肃省高等学校创新能力提升基金资助项目(2019B-059)。
关键词
多无人机
时序协同
航迹协同
变量决策
自适应变异算法
multi-UAV
time sequence coordination
track coordination
variables decision-making
adaptive mutation algorithm
