Research On Storage Location Allocation And Path Optimization Of Multi-position Robotic Storage And Retrieval System

With the development of science and economy and the concept of "Industry 4.0",the manufacturing industry has begun to develop in the direction of "intelligence and intelligence".The AGV-based "parts-to-picker" picking system is the mainstream choice in the current logistics industry.In this system,Automated Guided Vehicle(AGV)is used as a handling tool.The multi-position Robotic Storage and Retrieval System(MP-RSRS)studied in this article is a new type of "parts-to-picker" picking system.In most of the traditional"parts-to-picker" picking modes,mobile shelves are often arranged in the warehouse.Since an AGV can only carry one shelf at a time,then when the order requires a large number of goods and they are stored separately When it is on different shelves,the AGV needs to be transported multiple times,which greatly increases the total distance and energy consumption of the AGV,and also makes the operation time longer.The MP-RSRS system studied in this article can solve this problem.The MP-RSRS system is used by the AGV to reach the designated shelf and take out the turnover box.Moreover,the AGV itself has multiple positions and can carry multiple turnover boxes at a time,so it can meet the same requirements.The multiple demands of orders eliminate the need to repeatedly carry shelves back and forth,which further improves the system's picking efficiency,reduces the total travel distance and operation time of the AGV,and at the same time effectively reduces the energy consumption of the AGV.The article introduces the warehouse layout and main equipment of the multi-position robot storage and retrieval system,and gives the main equipment diagram and operation flow chart of the system.The two important indicators of the evaluation system,the total driving distance of the AGV and the time required for the AGV to complete each batch of order operations,are mainly introduced,the calculation formula is given,and then the storage allocation strategy and considerations considering the shelf similarity are proposed.The storage allocation strategy for the frequency of shelf visits.The mathematical model is established with the time required for the AGV to complete each batch of orders as the objective function,and the partheno evolution genetic algorithm is designed to solve the problem.The problem of cargo path planning has been studied,and a path planning strategy that also considers access operations is proposed.A mathematical model is established that takes the time required for the AGV to complete each batch of orders as the objective function.The ant colony algorithm is used to solve the experimental results.It shows that compared with the traditional random layout strategy,the shelf layout strategy proposed in this paper has shortened the operation time by 2.22%,and the total distance of the AGV exercised by 15.65%;the path planning method proposed in this paper and the sequential access operation phase Compared with that,the work time has been shortened by 4.05%,and the total distance of the AGV has been reduced by 23.52%.