复杂环境下的多无人机协同巡逻航路规划

为提高多架无人作战飞机（UCAV）存复杂环境下的协同巡逻效率，根据先验信息将环境划分为关注程度不等的未知区域、已知区域和禁飞区域，在航路规划算法中引入搜索回报函数和加权平均距离，加强对高关注度区域的巡逻力度，改善UCAV的空间分布。应用粒子群算法对航路规划模型进行了仿真，结果表明提出的协同巡逻航路规划算法有效

Cooperative Patrol and Route Planning of Multi-UCAV Under the Complex Environment

In order to improve the efficiency of cooperative patrol of multi-unmanned combat air vehicles （UCAV） in complex environment, the environment was divided into several small regions that had different attention, such as the unknown regions, the known regions, the no-fly regions and so on. The search reward function and weighted average distance were used to ensure search efficiency and combat efficiency of the air route, and strengthen the patrols to the regions of high attention. The issue with particle swarm optimization algorithm was simulated, and the results showed that the route planning algorithm of cooperative patrol was effective.