摘要
结合城市无人机复杂运行环境特点,针对三维空间路径规划问题,提出基于混沌粒子群算法的城市无人机路径规划方案。首先利用地理信息系统GIS获取城市相关信息构建三维城市环境模型,然后针对PSO粒子群算法中rand()函数伪随机的问题,引入Zaslavskii混沌序列生成随机数,构建城市无人机路径规划经济效益、飞行高度和障碍物规避3个适应度函数,并对PSO粒子群算法和函数的参数进行调整。选取天津某区域进行仿真,结果表明本文所提方法比传统PSO粒子群算法更具有优越性。
According to the characteristics of complex operating environment of urban UAV,aiming at the problem of three-dimensional space path planning,a path planning scheme of urban UAV is proposed based on chaotic particle swarm optimization algorithm.Firstly,a three-dimensional urban environment model was constructed by using GIS to obtain relevant urban information.Then,as for the pseudo random-ness problem of PSO particle swarm optimization algorithm,the Zaslavskii chaotic sequence was utilized to generate random numbers.By constructing three fitness functions for the economic benefits of urban drone path planning,flight altitude,and obstacle avoidance,the parameters of the PSO particle swarm optimiza-tion algorithm and function were adjusted.Simulation application research were carried out in Tianjin.The results show that the proposed method has superiority over traditional PSO particle swarm optimization algorithm.
作者
耿增显
广鑫
陈俊宇
郭悦翔
GENG Zengxian;GUANG Xin;CHEN Junyu;GUO Yuexiang(Air Traffic Management College,Civil Aviation University of China,Tianjin 300300 China)
出处
《西华大学学报（自然科学版）》
CAS
2024年第6期1-7,共7页
Journal of Xihua University:Natural Science Edition
基金
国家外国专家项目城市无人机运行及安全管控研究(DL2022202002L)
国家重点研发计划有人无人驾驶航空器融合运行安全风险监测技术(2022YFB4300904)
研究生科研创新项目基于数据融合的无人机运行异常航迹识别与分析平台研究(2022YJS089)。
