交叉粒群算法在无人机航路规划中的应用

随着现代战场环境的日益复杂和作战范围的不断扩大，给无人机（unmanned aerial vehicle, UAV）执行空中侦察、监视、作战等任务带来了严重挑战。为了提高UAV的作战效率和生存概率，从UAV的威胁空间建模出发，根据战场分布的威胁区域，先利用威胁回避技术快速地给出一条从起始点到目标点的粗选航路；然后在此基础上，应用粒群算法和遗传算法中交叉和变异操作相结合的思想，对粗选规划航路进行全局优化，从而找出一条能确保自身安全并威胁代价最小的最优飞行航路。仿真结果说明，该方法是有效、可行的。

Application of particle swarm algorithm in route planning of UAV

The UAV faces a flinty challenge when executing aerial reconnaissance, surveillance and campaign as the environment of modern battle field is becoming more and more complicated and large. A route planning algorithm is proposed to increase efficiency of campaign and survival of unmanned aerial vehicles (UAV). The algorithm first search a coarse route as fast as possible based on threat avoidance technique according to threat areas in battle field. Then it optimizes the coarse route globally by employing the particle swarm algorithm and the idea of intercross and variation in genetic algorithm. The result of simulating shows the algorithm can find an optimized safe route by consuming less iteration times.