摘要
针对粒子群算法在用于电力巡检无人机航迹规划时,存在的全局寻优能力不足、收敛速度慢和稳定性不佳的问题,提出了一种改进粒子群航迹规划方法。该方法通过自适应调整粒子的位置和速度,在搜索初期和搜索后期设定不同的更新策略,提升航迹规划算法的全局寻优性能和收敛稳定性;然后引入logistic混沌映射,对粒子群的惯性权重进行优化,提高算法的收敛速度和局部寻优性能。仿真实验结果表明,改进后的粒子群算法能够有效完成电力巡检无人机航迹规划,且在航迹距离、收敛速度和稳定性方面优于现有方法。
Based on the problems of insufficient global optimization ability,slow convergence speed and poor stability of particle swarm optimization algorithm in path planning of electric inspection UAV,an improved particle swarm optimization method is proposed.This method adaptively adjusts the position and velocity of particles.Different new strategies are set at the beginning and the end of the search,which effectively improves the global optimization ability and convergence stability of the track planning algorithm.Then,logistic chaotic mapping is introduced to optimize the inertia weight of particle swarm optimization to improve the convergence speed and local optimization performance of the algorithm.The simulation results show that the improved particle swarm optimization algorithm can effectively complete the track planning of electric inspection UAV.It is superior to the existing methods in terms of track distance,convergence speed and stability.
作者
罗达
廖仕源
姚文浩
周波
曾嘉
徐千雅
LUO Da;LIAO Shi-yuan;YAO Wen-hao;ZHOU Bo;ZENG Jia;XU Qian-ya(State Grid Sichuan Electric Power Transmission and Transformation Construction Co.,Ltd.,Chengdu 610051,China)
出处
《信息技术》
2022年第12期166-171,共6页
Information Technology
关键词
电力巡检
航迹规划
粒子群
自适应
混沌映射
electric inspection
track planning
particle swarm
adaptive
chaotic mapping
