1673-159X

CN 51-1686/N

基于混沌粒子群算法的城市无人机路径规划

Urban UA Route Planning Based on Chaotic PSO Algorithm

  • 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。

     

    Abstract: 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 randomness 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 optimization 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.

     

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