| In recent years,the application of automatic guided vehicles(AGVs)in the field of intelligent manufacturing has become more and more extensive,due to its intelligent ability to avoid obstacles and efficiently transfer goods,it is widely used in CNC workshops,which can significantly improve the overall efficiency of the material distribution process.In addition,it can supplement a large number of work stations with essential materials.In addition,the application of this technology can reduce investment in human resources,effectively reduce costs,and increase overall efficiency.This overview focuses on two levels,first,conventional path planning,and second,conflicts encountered in the multi AGV distribution process.How to plan its most ideal path is the key to improving the efficiency of workshop distribution.Therefore,this article focuses on exploring its planning methods,and explores various effective measures for resolving AGV path conflicts.The research content mainly includes:(1)A class of path planning problems for improving the gray wolf algorithm to deal with single AGV is proposed,which is aimed at the shortcomings of the traditional gray wolf algorithm such as poor population diversity and slow convergence speed in the later stage.Specific optimization points include: first,in the population initialization stage combined with the tent mapping initialization strategy to form a chaotic initial population,the chaotic initial population is reverse-learned to form a reverse learning population,the individual fitness of gray wolves in each position in the chaotic initial population and the reverse learning population is compared,and individuals with high fitness are selected to form the final initial population of the gray wolf algorithm,which enriches the initial population of gray wolves and improves the convergence speed;The second is to modify the position information equation of the gray wolf algorithm,combine the optimal individual particle idea of the particle swarm algorithm,so that the improved gray wolf algorithm can remember the historical optimal position of the individual,strengthen the development ability and convergence ability of the algorithm,combine the mutation vector idea of the differential evolution algorithm,randomly select the gray wolf individual to join the guided search,enhance the global exploration ability,and avoid falling into the local optimum.The above two improvements are integrated,and based on the simulation software,according to the actual workshop environment,a map is established for simulation experiments;In the same map environment,the GA genetic algorithm,DE differential evolution algorithm,PSO particle swarm algorithm,GGO traditional gray wolf algorithm and IGWO improved gray wolf algorithm were simulated and tested,and the path planning time and the advantages and disadvantages of the planned paths of the five algorithms were evaluated.The final fitness values of the fitness curves of each algorithm were compared to verify the effectiveness of the improvement strategy.Finally,compared with the A-star algorithm based on geometric model,it is verified that the improved gray wolf algorithm has good feasibility and application prospect for AGV path planning in CNC workshop.(2)Aiming at the conflict between multi-AGV task allocation and multi-AGV path planning in multi-AGV path planning,the rules of task allocation priority and AGV priority are designed,and the conflict resolution method combining priority and time window method is used to realize the conflict-free path planning of multiple AGVs.Firstly,the tasks of the CNC workshop are assigned through the AGV task assignment priority;Then,through the improved gray wolf algorithm,the initial global optimal operation path of each AGV to perform specific tasks is planned.Finally,aiming at the potential conflict problem between the initial paths,the local path planning is completed by the conflict resolution method,and then the extruded path is used to extract the conflict zone,combined with the distance between the conflict point and the next target work point of the AGV,and the applicable time window arrangement method is selected according to the priority level and conflict type of the AGV,so as to generate the global conflict-free optimal running path of multiple AGVs.Using the same task allocation and conflict resolution methods,the GA genetic algorithm,DE differential evolution algorithm,PSO particle swarm algorithm,GGO traditional gray wolf algorithm and IGWO improved gray wolf algorithm were simulated to verify the efficiency of the improved gray wolf algorithm compared with the traditional heuristic algorithm in multipath programming. |
