基于自适应权重鸽群算法的无人机航路规划

摘要

在复杂环境下采用基本鸽群算法进行无人机航迹规划时存在易陷入局部最优、收敛速度较慢且不稳定的问题.提出自适应权重鸽群算法,引入自适应权重系数对种群中个体的速度和位置进行计算,以提升航路规划质量和效率.仿真结果表明,相同任务环境下,自适应权重鸽群算法与PIO算法、PSO算法相比,得出的航线距离、威胁代价消耗与算法运行时间均有所减少.经过样条平滑算法进行平滑处理后的路径可以达到无人机航路可飞.%In complex environment,using traditional Pigeon-inspired Optimization algorithm for the UAV route planning leads local optimum and slow convergence speed and unstable problem.This paper introduces an Adaptive Weighted Pigeon-inspired Optimization algorithm.The adaptive weight coefficient is applied to calculate the speed and position of the individuals in the population which enhances the quality and efficiency of route planning.The simulation results show that the Adaptive Weighted Pigeon-inspired Optimization algorithm provides a shorter route distance,a lower threat cost consumption and shorter algorithm running time while comparing with PIO and PSO.After the spline smoothing,the UAV route is flyable.