针对多无人机的航迹规划问题, 采用一种混合粒子群算法, 将其应用于多无人机的航迹规划, 并保证各个无人机所经路径的代价函数总和最小。对城市环境包括楼房建筑等障碍物以及雷达干扰等禁飞区域进行建模, 通过建立多个航点并插入分割点的方法, 然后使用带有差分进化操作以及自适应调整惯性权重策略的混合粒子群算法进行航迹规划, 最后通过仿真实验验证了该算法的有效性。

To solve the problem in path planning for multiple UAVs, a hybrid Particle Swarm Optimization (PSO) algorithm is adopted, and the sum total of the cost function of each UAV's path is guaranteed to a minimum. The city environment, including buildings and other obstacles and threatening areas with radar interference, is modeled. The method of setting up a number of waypoints and then inserting the split point is used. Then, the hybrid PSO algorithm with differential evolution operations and adaptive inertia weight strategies is used for path planning of multiple UAVs. Finally, the validity of the algorithm is verified by simulations.