Machine Learning Optimizes the Efficiency of Picking and Packing in Automated Warehouse Robot Systems

Abstract

In order to improve the efficiency of automated warehouse robots in picking and packaging operations and enhance the control precision of their motion execution, this study incorporated machine learning algorithms into the design of the control system of automated warehouse robots. Mechanical learning and inductive learning were used to design machine learning methods applicable to these robots. Meanwhile, the learning process was optimized with the help of PID feedback adjustment, thus constructing an efficient execution system for the picking and packaging operations of automated warehouse robots. Taking the quantity of goods picked and packaged within the same period of time as the task indicator for the operations, simulation was carried out on the operation situations of robots adopting different machine learning methods. The results show that by using the combination of mechanical and inductive learning methods and the PID feedback adjustment approach, automated warehouse robots can handle more goods within the same period of time, significantly improving their motion execution efficiency in picking and packaging operations and providing strong support for the efficient operation of automated warehouses.