Aiming at the problems of many iterations

slow convergence rate and many times of generating path turning while particle swarm optimization（ PSO） algorithm is applied to AGV path planning

which affects production efficiency

an improved PSO algorithm with genetic factor is proposed. In the algorithm

the adaptive inertia weight is introduced to improve the convergence of the algorithm. The idea of crossover and mutation of the genetic algorithm is used to cross and the mutation operation on the particles to increase the diversity of the population

effectively reduce the number of iterations of the algorithm and improve the convergence speed

so that the algorithm can quickly generate the global optimal solution. The turning number factor is introduced in the algorithm fitness function to reduce the number of path turning and reduce the path complexity. MATLAB simulation results show that the optimized PSO algorithm can effectively reduce the number of iterations

improve the convergence speed

simplify the path complexity

and obtain the global optimal path.