Research On Path Planning And Scheduling Algorithm For AGV Cooperative Operation System

In the field of intelligent warehousing,efficient freight transportation between pickup points is a hot topic for researchers.Automatic Guided Vehicle(AGV)cooperative operation system is widely used in the field of intelligent storage because of its multi-objective and multi task processing.Among them,AGV quantity determination,order task scheduling and path planning which are the key problems to restrict the further development of AGV system.This paper improves the existing probabilistic road marking algorithm of path planning and simulated annealing algorithm of order task scheduling to improve the overall operation efficiency of AGV cooperative operation system.1)An improved probabilistic road marking algorithm which based on edge set optimization and peak node extraction that is proposed for path planning,which improves the efficiency of the algorithm and reduces the number of path nodes.In the learning stage of probabilistic road map,the edge set constraint method is used to optimize the edge set of the road map,which improves the real-time performance of the algorithm;by optimizing the quality of query path nodes and edge set,the problem of more complex edge set E in the road map R(N,E)and more turns of the path in the query stage are solved.The experimental results show that compared with the traditional probabilistic road marking algorithm,the running time of the improved probabilistic road marking algorithm on simple map and complex map with 100 road signs is reduced by 10.64% and 6.75% respectively,and the turning times are reduced by 42.86% and 41.18% respectively.In view of the possible conflict in multi AGV path planning,the time window algorithm and waiting method are used as the first level preventive measures,and the re planning path is used as the second level preventive measures of the two-level preventive strategy.Aiming at the problems that may occur in the process of operation,the dynamic path adjustment strategy based on on-line detection strategy is designed,which improves the ability of the system to deal with emergencies.2)An improved simulated annealing task scheduling algorithm based on memory and hybrid operator is proposed to solve the combination problem of multi vehicle and multi task,which effectively optimizes the average path cost and the standard deviation of path cost.After the initial solution of the multi vehicle multi task model is obtained by greedy algorithm,in view of the problem that the traditional simulated annealing algorithm is easy to lose the optimal solution in the search process when optimizing the initial solution,the memory function is added every time the temperature is cooled to avoid losing the optimal solution in the cooling process,and the quality of the optimal solution finally obtained by the algorithm is improved.;By adding the mixed operator method,a certain operator of the mixed operator is randomly selected by the roulette method to construct the neighborhood structure,which solves the problem of poor molecular exchange sufficiency in the annealing process..Ten simulation results show that compared with the traditional simulated annealing algorithm,the average path cost and the standard deviation of path cost of the improved simulated annealing algorithm are reduced by 2.73% and 30.06%respectively,and the average cooling times and average iteration times are effectively improved.