摘要
战场环境的动态性和不确定性以及任务分配问题的复杂性,使得无人机飞行前预先规划难以满足时间敏感性要求。针对多无人机动态任务分配问题,建立了数学模型,并设计了一种求解该问题的ISODATA约束聚类法。运用分组基础上的任务再分配策略,首先通过ISODATA约束聚类实施任务分组,然后在分组基础上利用免疫粒子群算法进行组内任务重分配。仿真实验和分析表明该方法简单有效,能够应对战场环境中的多种突发情况,具有较好的时间性能。
The dynamics and uncertainty of the battle field,as well as the complexity in task assignment make it difficult for the preplanning of Unmanned Aerial Vehicle（UAV） to satisfy the performance request in time sensitivity.A formulation was proposed for multi-UAV dynamic task assignment,and an ISODATA constrained clustering was designed to solve the problem.Task reassignment based on grouping was used.First,task grouping was implemented according to ISODATA restrained clustering,and then immune particle swarm algorithm was used to make task reassignment inside a group.The results and analysis of simulation indicated that the approach is effective and simple with good time performance,which can deal with many unexpected instances in battle field.
出处
《电光与控制》
北大核心
2010年第5期22-25,34,共5页
Electronics Optics & Control
基金
国家自然科学基金(60675057)
关键词
无人机
动态任务分配
ISODATA算法
约束聚类
免疫粒子群优化
Unmanned Aerial Vehicle（UAV）
dynamic task assignment
ISODATA algorithm
constrained clustering
immune particle swarm optimization
