Weight learning based stack shape planning method

A stack-shaped and weighted technology, applied in the field of logistics, can solve the problems of low efficiency, lack of macro planning, low degree of automation, etc., and achieve the effect of simple use and clear parameters.

Active Publication Date: 2018-11-09
江苏楚门机器人科技有限公司
View PDF4 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional manual palletizing process is divided into two types: the first one, manual palletizing: through human participation, the position where the container should be stacked is continuously and dynamically determined. The disadvantages are low efficiency, long palletizing cycle, and lack of macroscopic Planning, when there are many boxes to be stacked, the dynamic artificial stack shape planning may fail, and it needs repeated trials, which greatly depends on the experience of the workers to get a better stack shap

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Weight learning based stack shape planning method
  • Weight learning based stack shape planning method
  • Weight learning based stack shape planning method

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0019] The technical solution of this patent will be described in further detail below in conjunction with specific embodiments.

[0020] Cut Figure 1-3 , A stacking planning method based on weight learning, the specific steps are as follows:

[0021] Step one, automatic stack shape planning: calculate the priority rate of stacking for all containers to be stacked {R i }, R i =P v V i +P s S i +P w W i , Where P V Is the volume weight, V i Is the volume of the container, P S Is the area weight, S i Is the area of ​​the cargo box, P w Is the quality weight, W i Is the quality of the container, where i∈[1, n] represents the number of the container;

[0022] Step two, set R i Arrange from largest to smallest to get the stacking order of the boxes;

[0023] Step three, take the current {R i }max cargo box, try to put it to L in turn 1 To L k Until you find a stackable layer L m , L m Is the number of layers of the container stack, m∈[1,k], which represents the number of layers that can b...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a weight learning based stack shape planning method. The concrete steps include: step 1, calculating stacking priority [Ri] for all to-be-stacked cargo tanks, wherein Ri=PvVi+PsSi+PwWi; step 2, ranking Ri in sequence from large to small; step 3, taking the current [Ri]max cargo container and trying to stack it on L1 to Lk in sequence until a stackable stack shape layer Lm is found; step 4, update the current stack shape state; step 5, removing the cargo containers counted to the stack shape from Ri; step 6, checking whether residual cargo containers exist or not, if residual cargo containers exist, repeating steps from step 3 to step 5; step 6, implementing stack shape planning. The method is simple in use and parameters are clear and easy to understand. Automatic stack shape planning can be implemented in a comparatively short period and dynamic planning can even be made for each stack order; high efficiency is achieved. The method provided by the invention isfully automatic and no manual intervention is required. The method provided by the invention has a self learning function and the quality of planned stack shapes is improved continuously towards the optimum.

Description

technical field [0001] The invention relates to the field of logistics, in particular to a stack shape planning method based on weight learning. Background technique [0002] The application of automation equipment in the logistics industry, such as express packing, palletizing, etc., has replaced the role of handling by humans, and new application requirements have also put forward new requirements for the degree of intelligence of automation equipment. The traditional manual palletizing process is divided into two types: the first one, manual palletizing: through human participation, the position where the container should be stacked is continuously and dynamically determined. The disadvantages are low efficiency, long palletizing cycle, and lack of macroscopic Planning, when there are many boxes to be stacked, the dynamic artificial stack shape planning may fail, and it needs repeated trials, which greatly depends on the experience of the workers to get a better stack sha...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/08G06Q10/06
CPCG06Q10/0631G06Q10/083
Inventor 裴磊
Owner 江苏楚门机器人科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap