多任务空地武器多目标协同优化任务规划算法研究

Research on Multi-Mission Airborne Weapon Multi-TargetCooperative Optimization Planning Algorithm

在复杂的战场作战环境中可能存在多个动态威胁,如何快速规划出最优航路是攻击任务顺利执行的关键。本文提出一种在文化算法框架下稀疏A*算法与遗传算法(Genetic Algorithm,GA)相结合的动态航路规划算法,用于多任务空地武器多目标协同任务优化中。算法基于文化算法思想框架,首先利用稀疏A*算法快速获取初始航路及航路点信息,并将获取到的信息作为知识送入信仰空间存储,指导遗传算法对种群空间个体在有效范围内优选可行航路点,从而实现最优化目标任务分配及航路获取。仿真结果表明,算法能够有效避开威胁,减少遗传算法规划整体航路的飞行距离,完成多任务空地武器多目标协同任务规划。

In a complex battlefield environment,there may be multiple dynamic threats,how to quickly plan the optimal flight path is the key to the successful execution of the attack mission.Under the cultural algorithm framework,a kind of dynamic flight path planning algorithm used in the multi-mission airborne weapon multi-target cooperative optimization planning combining sparse A algorithm with Genetic Algorithm(GA)algorithm is proposed.Based on the cultural algorithm framework,the sparse A algorithm is used to quickly obtain information of initial flight path and navigation nodes,and the acquired information is sent to the belief space as knowledge for storage,so as to guide the population space to achieve the optimal target task allocation and route acquisition by GA algorithm within the effective range.Simulation results show that the algorithm can effectively avoid threats,reduce the overall flight distance of flight path,and complete the multi-mission airborne weapon multi-target collaborative planning.