采用粒子群优化算法规划无人机侦察航路

Particle swarm optimization based path planning for reconnaissance of unmanned air vehicles

研究了粒子群优化算法,提出了将该算法运用于无人机的航路规划。引入交换指数和变异子的概念,解决了算法的局部极值问题,给出了航路规划的方法和步骤。在Matlab仿真环境下得到了参考航路。结果表明,该算法简单有效,在很大程度上提高了无人机的侦察效率。

We studied Particle Swarm Optimization （PSO） algorithm and used it in path planning of unmanned air vehicles. Two concepts of exchange index and mutation operator are introduced to solve the problem that PSO easily falls into a local extremum. The approach of the scheme is described. A reference path is obtained in Matlab simulation environment. Result shows that the improved PSO is brief and effective, and enhances the ce efficiency of the UAVs greatly.