基于改进粒子群算法的移动机器人路径规划

为了提高复杂环境下移动机器人的精准导航作用,提出了移动机器人路径规划的改进粒子群优化(PSO)算法,即利用粒子个体极值的加权平均值,同时加入惯性权重.建立了移动机器人工作环境的栅格模型,利用Matlab软件进行移动机器人路径规划仿真分析.仿真结果表明:改进后的粒子群算法容易使粒子移动到最佳位置,加强了全局寻优能力,在复杂环境中搜索路径性能优于传统算法.

Mobile robot path planning based on modified particle swarm optimization algorithm

In order to improve precise navigation function of mobile robot in complex environment,mobile robot path planning based on improved particle swarm optimization (PSO) algorithm is proposed,namely using particle individual extremum weighted average value,while adding inertia weight.Establish grid model for working environment of the robot,and simulation analysis on path planning of mobile robot based on Matlab software.Simulation results show that the improved PSO algorithm is easy to move the particle to the best position,and enhance the ability of global optimization,and the performance of path searching is better than the traditional algorithm in complex environment.