Research On Global Path Planning Of Mobile Robot In Intelligent Factory Logistics System

With the "Made in China 2025" development strategy,many manufacturing companies are choosing to build smart factories by using robots to complete tasks in their factories to save labour costs,improve production efficiency and reduce carbon emissions.In a smart factory,logistics systems play a key role,and the application of intelligent mobile robots in logistics systems provides a new direction for the construction of smart factories.In this paper,for different application environments of mobile robots in smart factories,this paper establishes different environment models respectively and designs suitable algorithms to validate the models,the main research includes the following three aspects:(1)Analysis of the application environment of mobile robots in smart factory logistics system.Firstly,the characteristics of the environment in the intelligent production workshop and the intelligent warehouse are elaborated respectively,and the problems and technical difficulties of the current mobile robot path planning in the intelligent production workshop and the intelligent warehouse are summarized by combining the characteristics of mobile robots applied in different environments.The path planning problems of mobile robots in smart factory logistics system are analyzed from the perspectives of single robot and multiple robots respectively,which provide the basis for the subsequent model building.(2)Modeling and solving the path planning problem of mobile robots in intelligent production workshop.Taking the intelligent production workshop as the application environment,we analyze and model the single-robot and multi-robot applications respectively considering three factors: workshop environment characteristics,robot path length,and whether the path is safe and reliable,design the improved fused genetic grey wolf optimization algorithm,and verify the effectiveness and superiority of the algorithm and the applicability of the constructed model for path planning of different numbers of mobile robots through the analysis of arithmetic cases.The experimental results show that the improved algorithm improves the accuracy of the path planning problem by 20%-50% compared to the original algorithm,and the larger the amount of data,the more obvious the improvement is,which is suitable for the path planning problem of mobile robots in intelligent production workshops.(3)Modeling and solving mobile robot path planning problems in smart warehouses.The differences between the separated smart warehouse and smart production floor environments and the mobile robots applied in the respective environments are analyzed.In the separated smart warehouse the shelves are movable units,so there are state transitions during the movement of the mobile robots in the separated smart warehouse.Focusing on four factors: path length of mobile robots,safety obstacle avoidance,state transition,and environment characteristics,we analyze and model the single-robot and multi-robot applications respectively,design an improved chimpanzee optimization algorithm solution model,and verify the effectiveness and superiority of the algorithm and the applicability of the constructed model to path planning of different numbers of mobile robots through arithmetic case analysis.Experimental results show that the improved chimpanzee optimization algorithm has higher accuracy and faster speed than other similar algorithms in solving path planning problems,with an improvement of 12%-30% in accuracy compared to the original algorithm,and that the improved algorithm is applicable to the path planning problem of mobile robots in the construction model.