Research And Implementation Of Intelligent Sorting Transport Trolley In Factory Environment

In recent years,with the improvement of China’s economy,China’s manufacturing industry has developed rapidly.Whether it is labor-based manufacturing or technology-based manufacturing,the proportion of China’s manufactured products to global exports has been continuously increasing,ranking first for 11 consecutive years.However,the intelligent power needed by China’s manufacturing industry is still lacking.This paper hopes to improve the efficiency of the manufacturing industry,further reduce the manufacturing cost and make the made in China more competitive through the identification and transportation of the workpiece by the intelligent car.Intelligent car is an integrated control system which integrates many sensors and can sense the surrounding environment and make relevant decisions.In the factory environment,a series of operations,such as picking,assembling and processing,need to be carried out on the workpiece,and then the workpiece needs to be transported.Obviously,this is a boring and heavy work.Compared with the manual way to complete these tasks,the intelligent car can completely replace the manual way to complete the required operation of these workpieces,which not only improves the efficiency,but also greatly liberates the labor force,which is also needed for China’s intelligent transformation.Based on this,the main research work of this paper is as follows:First of all,starting from the research status of intelligent vehicles at home and abroad,to understand the current progress of research on intelligent vehicles in relevant countries,and then determine the technology required for intelligent sorting transport vehicles in the factory environment,and the development status of the required technology was fully studied and analyzed.Secondly,the required target detection technology is studied and the selected YOLOv4-Tiny is improved.First of all,the process of traditional target detection is analyzed and the relative experiments are compared.Then,R-CNN series and YOLO series based on deep learning are studied.After comparing the advantages and disadvantages of the two algorithms and combining with the required conditions in the factory background,YOLOv4-Tiny is selected,and then the algorithm is improved.After training with a self-built dataset of artifacts,the mAP improved by 3.22%.Thirdly,the current mainstream communication technology is analyzed.By comparing the advantages and disadvantages of various communication technologies,ZigBee is chosen in combination with the requirements of communication technology in the factory environment.Then,it makes an in-depth study of ZigBee,from the node types,network topology and the composition of the protocol stack.Finally,the selected ZigBee device experiments,the experiment shows that the data can be successfully transmitted through the ZigBee network.Finally,the comprehensive test of multi-intelligent car is carried out.All the modules are loaded onto the car for experiments.The experiments show that the intelligent car can detect the type of workpiece,and then transport the corresponding station points according to the type of workpiece.When a workpiece needs to be further processed and transported,the location information of the positioning equipment carried to the car is shared through ZigBee,and then the intelligent car nearest to the workpiece to be processed is selected for the transportation task.