Research On The Service Efficiency Of AGV In Unmanned Warehouse And The Algorithm Of Goods And Shelf Placement

Since the beginning of this century,the rapid growth of e-commerce business and express delivery business has injected huge power into the research and application of the unmanned warehouse management system.For the large-scale task and shelf intensive storage environment,the optimization of goods placement and the path planning algorithm of the automatic guided vehicle(AGV)play a key role in improving the overall performance of the unmanned warehouse management system,reducing all aspects of operating costs and improving enterprise profits.At present,the research on improving the service efficiency of AGV mainly focuses on the path planning algorithm of AGV.However,the unmanned warehouse management system is a huge system closely related to various jobs,and the optimization of other jobs will also greatly affect the service efficiency of AGV.At present,there are relatively few researches on the optimization of goods and shelf placement algorithm in the unmanned warehouse.Different goods and shelf placement strategies in the unmanned warehouse will require different picking task scheduling strategies.Therefore,how to improve the placement algorithm and the picking task scheduling algorithm of goods and shelves to affect the service efficiency of AGV has become the focus of this study.For the above problems,the main research work of this paper is as follows:First of all,the environment of the unmanned warehouse management system is modeled.According to the real working environment of the unmanned warehouse,the key equipment in the unmanned warehouse is selected,and the equipment irrelevant to the research content is removed.The function of the selected equipment and its working mode in the unmanned warehouse are abstracted.Then,a placement algorithm based on co-occurrence matrix and depth first search is proposed for the placement of items and shelves in unmanned warehouses.There is always a certain relevance between the various items stored in the warehouse.By making this association mining algorithm,we first find the association between the items in the unmanned warehouse,and then according to this association,we put forward the placement algorithm of items in the shelf considering the storage conditions of the shelf in the unmanned warehouse,so that the items stored in the same shelf have the maximum Association.Secondly,for the problem of picking task assignment,we design a picking task scheduling algorithm which considers "single task" mode and "multi task" mode.After optimizing the algorithm of goods and shelf placement,the scheduling of pickingtasks is no longer to assign only one picking task to each shelf at a time.Because there is a certain correlation between the items in the shelf,there will be a large probability that the adjacent pick-up tasks will appear in the same shelf.Therefore,the scheduling algorithm of picking tasks in the unmanned warehouse is optimized to allocate more picking tasks to a shelf.Finally,an experimental platform is built for the environment modeling of unmanned warehouse.The feasibility and optimization effect of the algorithm proposed in this paper are proved by building the experimental scene based on the data set obtained and using the primary verification and simulation experimental platform.After the operation and test of the experiment,the final experimental results show that the efficiency of the system is improved significantly,and the system's compression resistance is also improved.