基于改进粒子群算法的智能机器人路径规划

Path planning for intelligent robots based on improved particle swarm optimization algorithm

针对粒子群算法局部寻优能力差的缺点,提出一种非线性动态调整惯性权重的改进粒子群路径规划算法。该算法将栅格法与粒子群算法进行有效结合,在路径长度的基础上引入安全度和平滑度概念,建立动态调整路径长度的适应度函数。与传统的粒子群算法相比,实验结果表明,改进算法具有较强的安全性、实时性及寻优能力。

As regards the poor local optimization ability of Particle Swarm Optimization （ PSO）, a nonlinear dynamic adjusting inertia weight was put forward to improve the particle swarm path planning algorithm. This algorithm combined the grid method and particle swarm algorithm, introduced the two concepts of safety and smoothness based on path length, and established dynamic adjustment path length of the fitness function. Compared with the traditional PSO. The experimental results show that the improved algorithm has stronger security, real-time and optimization ability.