A smart logistics environment robot loading method and device

Abstract

The invention discloses a loading method and device of an intelligent logistics environment robot. The method comprises the following steps that firstly, cargo information is acquired in real time; secondly, the coordinates of the placement positions of cargoes in to-be-loaded areas corresponding to conveying addresses are calculated; thirdly, a classification robot is utilized for clamping the cargoes to the corresponding to-be-loaded areas used for temporary storage from a conveying belt; fourthly, the coordinates of the final positions of the cargoes in loading areas of an AGV intelligent carrier; and fifthly, the cargoes are carried to the loading areas of the AGV intelligent carrier according to a control matrix obtained by an intelligent carrying control model. The cargo sizes, the cargo conveying addresses, the cargo position coordinates and other information are automatically acquired by combining machine vision, the classification robot and the intelligent carrier are utilizedfor automatically and intelligently classifying and loading the cargoes by setting up a neural network model, the loading error rate is greatly reduced, and the loading efficiency is improved.

Description

technical field [0001] The invention belongs to the field of robot cargo loading, and in particular relates to a robot loading method and device in an intelligent logistics environment. Background technique [0002] In recent years, the logistics industry has developed rapidly, and "smart logistics" has attracted more and more attention. In the "smart logistics" environment, cargo loading is generally completed manually, resulting in high labor costs and low cargo loading efficiency. The combination of smart logistics and robots to realize automatic and intelligent loading of goods is the core direction of the development of the logistics industry. [0003] The loading of goods in the smart logistics environment is an important link in the logistics management process, which includes steps such as goods storage, inventory management, replenishment picking, distribution processing, shipping operations, and distribution operations. At present, each step is poorly connected, t...

AI Technical Summary

Problems solved by technology

[0004] The traditional manual loading method is no longer suitable for the logistics industry due to the disadvantages of high cost

Workers manipulating robotic arms or palletizing robots to load goods will lead to wrong loading of goods, low practicability and low efficiency of loading various goods

Nowadays, there are automatic loading robots that use the method of arm joint dynamics equations to obtain training samples, thereby using machine learning methods to load goods. Facts have proved that the method of solving dynamic equations has poor stability and is not very practical. disadvantages

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