基于改进的粒子群优化算法的无人作战飞机航路规划

UCAV Path Planning Based on Modified PSO Algorithm

事先针对敌方防御区内的威胁部署和目标的分布情况,对无人作战飞机的飞行航路进行整体规划设计,可以综合减小被敌方发现和反击的可能性、降低耗油量,从而显著提高UCAV执行对地攻击(或侦察)任务的成功率。在对粒子群优化技术研究的基础上,将一种用于解决TSP的PSO算法加以改进,引入模拟退火的策略思想,借以克服PSO算法易陷局部最优的早熟现象,并在UCAV航路规划中加以运用。仿真实验表明,该算法简易而有效,用其优化出的航路能够满足UCAV飞行任务规划的需要。

Aiming at the threat deployment and target distribution in the defense areas of enemies in advance, the holistic path planning of unmanned combating air vehicle （UCAV） can minimize the possibility being discovered by enemies and reduce the fuel wastage as a whole. Consequently, the successful probability that UCAV attacks the ground or performs reconnaissance can be markedly improved. Based upon analyses of Particle Swarm Optimization （PSO） Algorithm, an modified PSO algorithm for solving TSP is presented in the paper. The strategy of Simulated annealing （SA） is introduced in order to overcome the prematurity of PSO algorithm, i. e. , the problem which is inclinable to plunging into the local optimum. The improved methods have been used in UCAV path planning. The experimental results indicate that not only the methods are simple and effective but also the programmed path can satisfy the demands of UCAV flight mission planning.