In order to solve the problem of particle swarm optimization algorithm easily falling into local optimum in global path planning

according to the related theories of particle swarm optimization algorithm and pigeon-inspired optimization algorithm

the influencing factors that lead to particle swarm optimization algorithm falling into local optimum are analyzed

and the size of raster in two-dimensional raster map is determined according to the minimum turning radius of automatic guided vehicle.The particle swarm optimization is improved and combined with the fast convergence ability of pigeon-inspired optimization algorithm

and a two-stage hybrid optimization algorithm based on improved particle swarm optimization and pigeon-inspired optimization algorithm is proposed for global path planning. Compared with the traditional particle swarm optimization algorithm

the new method can effectively avoid falling into local optimum. The global optimal path length is shortened by about 3.8% and the path planning time is reduced.