Research On Multi-AGV Path Planning Optimization Algorithm And Scheduling System

At present,multi-AGV(Automated Guided Vehicle)intelligent warehouse has become the object of vigorous development in the logistics industry.At the same time,the AGV scheduling system based on intelligent warehouse is a hot spot for scholars at home and abroad.This paper studies AGV scheduling system based on intelligent warehouse.Firstly,the AGV scheduling system based on intelligent warehouse is analyzed,and the two key links that affect the efficiency of the AGV scheduling system are multi-AGV path planning and task scheduling.Secondly,the topology-grid method is used to establish a map model of the warehouse environment,and the A* algorithm is analyzed.Aiming at the problem of multi-AGV path planning,a time-space map model is added to the topology-grid map to construct a spatio-temporal map model,and a multi-AGV path planning method based on spatiotemporal conflict constraint A* algorithm is proposed.This method integrates the conflict resolution strategy into the node expansion process and considers the turning cost.Finally,an experimental simulation is conducted on MATLAB,which proves that the method can comprehensively consider the travel time,waiting time and turning time of the AGV,and plan a path with no collision and minimum time cost for multiple AGVs.Thirdly,according to AGV's "single multitasking" operation mode in the intelligent warehouse,the task scheduling problem is decomposed into task combination and dispatch problems.The improved ant colony algorithm is used to solve the task combination problem,and for the task group's dispatch sequence problem,a dispatch strategy based on task group priority is proposed.Finally,the task scheduling algorithm is simulated on MATLAB,and the results show that the improved algorithm can improve the overall efficiency of the system.Finally,AGV was independently designed and developed,and a set of AGV scheduling software was developed using MATLAB.The basic functions of a single AGV and the functions of the scheduling software were tested to verify the feasibility of the algorithm in this paper.