基于航线自主规划的变电站无人机巡检

Inspection of substation UAV based on autonomous route planning

针对无人机的全局航迹规划进行专项研究,在分析了细菌觅食算法和粒子群算法的优劣势以后,建立了一种引用细菌觅食算法的趋化及迁徙算子的改进型粒子群算法,用以改进无人机的全局航迹规划问题。围绕研究主题,确定了以下技术路线,首先分析了细菌觅食算法、粒子群算法的实现过程,然后剖析了粒子群算法的缺点,提出了一种应用细菌觅食算法的趋化及迁徙算子的新型粒子群算法。面向无人机航迹规划的需求,分析了三维粒子群航迹规划模型、适应度函数、航迹平滑方法、算法早熟收敛判断等,最后利用Matlab软件进行仿真分析。通过与传统粒子群算法作对比,验证了改进后算法在稳定性和寻优能力方面具有明显优势。

The global route planning of UAV is studied.After analyzing the advantages and disadvantages of bacterial foraging algorithm and particle swarm algorithm,a improved particle swarm algorithm using chemotaxis and migration operator of bacterial foraging algorithm is established to improve the global route planning of UAV.The following technical routes are determined:firstly,the implementation process of bacterial foraging algorithm and particle swarm algorithm is analyzed;and then the shortcomings of the particle swarm algorithm are analyzed;thus a new particle swarm algorithm using chemotaxis and migration operators of the bacterial foraging algorithm is proposed.For the requirements of UAV route planning,the three-dimensional particle swarm route planning model,fitness function,route smoothing method,algorithm precocious convergence judgment,etc.are analyzed,and finally the Matlab software is used for simulation analysis.By comparing with the traditional particle swarm algorithm,it is verified that the improved algorithm has obvious advantages in stability and optimization ability.