Research On Multi-AGV Path Planning For Smart Warehousing

The digital economy has seen a remarkable surge in recent years,with a broad spectrum of radiation and a more profound effect than ever before.This is a strategic decision to take advantage of the new technological revolution and the fresh possibilities for industrial transformation.As one of the iconic tools of the digital economy,intelligent storage robots have far-reaching application scenarios in the logistics industry and manufacturing industry.In the intelligent storage system,AGV(Automated Guided Vehicle),as the main transport carrier in intelligent storage,is one of the important technologies for its path planning.This thesis utilizes the Dynamic window approach(DWA)as its foundation to enhance AGV transportation efficiency,resolving deadlock and collision conflict between AGV and obstacles by augmenting the optimal evaluation function and amplifying the impediments.However,in order to achieve efficient transport in intelligent storage systems,multiple AGVs are often required to work simultaneously.They both work together and operate independently of each other.To improve the safety and efficiency of the overall transportation,this thesis is based on the dynamic window method for multi-AGV task allocation and forward prediction strategy.This thesis offers a comprehensive look into the research background and importance of intelligent storage systems,summarizes the current AGV path planning research both domestically and internationally,and outlines the environment modelling and task modelling approach employed in this thesis;research,based on the issues and necessities that arise from intelligent storage systems.The currently commonly used algorithms are analysed and introduced,and it is determined that the dynamic window method will be optimised and improved in this thesis.Theoretically analyzing the traditional dynamic window method and constructing a mathematical model of motion are the initial steps in path planning research of single AGV.For the collision conflict that easily occurs between the planned route of AGV and obstacles too close,the safe speed range of the nearest obstacles in the speed sampling window is restricted and improved to achieve the effect of obstacle expansion.For the phenomenon that AGVs are prone to deadlock,a sub-target function is introduced into the optimal evaluation function,and after determining that AGVs enter the deadlock region,temporary sub-target points are set for escape,and after planning to the sub-target points,the task target points are planned again.Simulation experiments finally confirm the practicality of the upgraded algorithm.As the number of AGVs increases,the probability of collision and conflict also increases,and the application scenario transitions from static and single to dynamic and complex situations.In this thesis,the task assignment of multi-AGVs is first carried out,and as the starting points and task target points of multi-AGV operations are different,the principle of proximity assignment is adopted,and each AGV transports to its nearest task target point,so as to achieve the shortest total planning path for multi-AGVs.In the dynamic and complex storage environment,a forward prediction strategy is carried out for AGVs,and the AGVs appearing within the range of conflict determination are predicted for the next moment of position,so as to carry out obstacle avoidance.The MATLAB simulation platform was utilized to simulate and validate the path planning of multi-AGVs.The simulation outcomes demonstrated that task allocation can simplify dynamic paths,and the upgraded dynamic window algorithm,based on this improved dynamic window,can have a successful obstacle avoidance effect for both static and dynamic obstacles.The research content of this thesis has certain reference significance for the technical development of the intelligent storage industry.