Task Scheduling And Path Planning In Intelligent Warehouse System

With the development of information technology in China,the rapid rise of e-commerce has brought enormous challenges to traditional logistics.Although the logistics industry in China has also been greatly developed in recent years,compared with developed countries,the logistics costs in China are still account for a relatively large proportion of GDP,and the overall level of intelligence is still low.At present,smart logistics is vigorously developed at home and abroad,and intelligent warehousing,as an extremely important part of the entire intelligent logistics system,has already attracted the attention of many researchers around the world.The intelligent warehousing system,Kiva,based on multi-mobile robots represented by Amazon,has aroused widespread concern of the society.The system frees the sorter from the mechanical process of moving and searching for items,which greatly improves the sorting efficiency.However,the intelligent warehouse system using the sorting mode still has many problems,which cannot meet the requirements of mass commodities,and the system efficiency needs to be further improved.Two key issues that are important and limit the overall efficiency of the system are order scheduling problems and path planning for mobile robots.Therefore,this thesis focuses on these two problems,and proposes an order task scheduling algorithm based on order fitness and discrete particle swarm optimization.Then improves the traditional A* algorithm for path planning problem in warehouse system and designs path planning algorithm based on space-time map,which improve the overall efficiency of the system.Firstly,in order to study the specific scheduling problems and path planning problems in the storage system,it is necessary to have a holistic understanding of the storage system.Therefore,this thesis investigates the development process of the warehousing system,and introduces the particle swarm algorithm and path planning algorithm applied in the research process,which lays a foundation for the subsequent research work.Secondly,in order to study the order scheduling problem,this thesis first analyzes the order characteristics in the e-commerce warehousing system,analyzes the problems of the current order scheduling,and then proposes the concept of order matching degree and the order allocation strategy.Then the overall process of order task scheduling is designed,and the key steps in the process,namely the discrete particle swarm-based picking task scheduling algorithm,are described in detail.Finally,the order task scheduling algorithm is verified by the simulation platform.The scheduling algorithm can effectively improve the overall efficiency of the system to process orders.Finally,this thesis constructs a suitable road network map based on the scene of e-commerce warehouse,then studies the traditional path planning algorithm based on two-dimensional plane map,designs the collision avoidance rules,improves the algorithm,and analyzes its limitations.Then,the path planning algorithm based on space-time map is designed and implemented.The algorithm also considers the costs of turning and loading shelves in the actual operation process of the robot,so that the system is more in line with the actual scene.Finally,the self-written simulation platform proves that the designed algorithm can effectively reduce the conflict between mobile robots,shorten the time for robots to transport shelves,and improve the operating efficiency of the system.