Path planning for intelligent robots based on improved particle swarm optimization algorithm
Zhang Wanx · Journal of Computer Applications · 2014
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.