The walking path of AGV used to carry vehicles in the garage has not been reasonably optimized,resul-ting in long storage time and low utilization efficiency of planar movable stereo garage.On the basis of the tradition-al Grey Wolf Algorithm,an improved Grey Wolf Algorithm,was proposed.Firstly,the reverse learning strategy was used to initialize the population,which ensured the quality of the initial solution.Secondly,a two-stage nonlinear convergence factor was added to balance the global search and local development ability of the algorithm.Finally,the crossover and mutation search strategies based on genetic algorithm were used to improve the late convergence speed of the algorithm.The improved Grey Wolf Optimization Algorithm was used to solve AGV path planning prob-lem in planar mobile stereo garage.Through MATLAB simulation experiment,it was compared with the path planned by genetic algorithm and traditional Grey Wolf Algorithm.Experimental results show that the improved Grey Wolf Algorithm is superior to the above two algorithms in convergence speed,path length and turning times,which proves the feasibility and effectiveness of the improved Grey Wolf Algorithm in solving the path planning problem and improves the overall operation efficiency of the garage.
维普
万方数据