基于遗传算法和分层任务网络的战术任务规划方法

针对给定约束条件下作战任务最优战术动作序列规划问题,提出一种将分层任务网络(hierarchical tasknetwork,HTN)与遗传算法(genetic algorithms,GA)相结合的HTN_GA战术任务规划算法。采用定性与定量相结合的方法,基于分层任务网络对战术知识进行建模,基于遗传算法对战术生成过程进行优化。分析了HTN_GA战术规划算法的可行性、完备性。实现了基于SEAD任务的无人作战飞机(uninhabited combat aerial vehicle,UCAV)战术任务规划,仿真结果证明了该任务规划算法的可行性和有效性。

Tactical Mission Plan Based on Genetic Algorithms and Hierarchical Task Network

Considering the optimal planning of tactical actions sequences,a tactical mission planning algorithm based on hierarchical task network(HTN) and genetic algorithms(GA) is proposed by integrating the qualitative knowledge and quantitative optimization.The HTN is used to model the tactic procedure knowledge on certain task,and then,GA is applied to optimize the tactic procedure knowledge so that the best tactic can be searched and selected.The sound and complete procedure for HTN_GA tactic planning are analyzed.The UCAV tactic planning algorithm for suppress enemy air defense(SEAD) based on the proposed HTN_GA is presented.The simulation results demonstrate feasibility of the proposed tactic mission plan algorithm.