Research On AGV Path Planning Of Automated Warehouse

In recent years,the fields of computer,Internet and artificial intelligence have sprung up.With the proposal of the "Industry 2025" plan,the upgrading of production technology has accelerated,which has promoted the rapid development of e-commerce,manufacturing services and other industries.Bringing new pressure to these industries will inevitably require downstream industries such as manufacturing,logistics and warehousing to have efficient and efficient operations and operating mechanisms.Under this background,the automated three-dimensional warehouse came into being,and the use of AGV can greatly improve the competitiveness of enterprises.In this paper,the main problems encountered during the operation of AGV in the automated three-dimensional warehouse,combined with the typical AGV operating characteristics and actual work scenarios,are proposed to the AGV operation path planning in the automated three-dimensional warehouse,and the obstacles during the operation of the AGV are proposed.The corresponding dynamic monitoring mechanism ensures the safety of AGV operation,and through certain condition assumptions,the AGV path planning problem in the automated three-dimensional warehouse is simplified accordingly.This paper takes the shortest total duration of all AGV operations as the objective function,uses the two-stage method of destruction-reconstruction on the basis of the AGV path planning model,and integrates the dynamic obstacle avoidance problem during the operation of the AGV to establish an automated warehouse AGV picking The AGV path planning model under the situation of cargo storage,and a multi-group hybrid intelligent algorithm based on ant colony algorithm and genetic algorithm is proposed to solve the above model.The multi-group hybrid intelligent algorithm is that the ant colony algorithm and the genetic algorithm independently pass a certain number of iterations or operation cycles,and transfer the information of their respective optimal solutions to each other,and then perform certain The results are corrected,the worst solution is eliminated from the solution results,and each step is integrated into each other's algorithm,so that the algorithm can obtain the optimal solution more quickly and accurately.This not only ensures the feasibility of a multi-group hybrid intelligent algorithm,but also avoids the defects of prematurity and local optimization caused by a single heuristic algorithm.Combined with actual cases,by comparing and analyzing the results of ant colony algorithm,genetic algorithm and hybrid intelligent algorithm to solve the model,the hybrid intelligent algorithm fully absorbs the advantages of ant colony algorithm and genetic algorithm,and has greater speed and accuracy The advantages of this method indicate that the hybrid intelligent algorithm has a good application prospect in solving the AGV path planning problem of the automated three-dimensional warehouse.