Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Mechanical-arm hand object grabbing method based on depth learning

A deep learning, robotic arm technology, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve the problems of falling objects to be grasped and insufficient grasping stability of different objects, achieving stable grasping and solving grasping problems. The effect of unbalanced or knocked over grasping objects, improving accuracy and stability

Active Publication Date: 2018-05-29
SHANDONG UNIV OF SCI & TECH
View PDF3 Cites 26 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The joint motor rotates at a certain angle, finds a reasonable path through path planning, turns to the target, and grasps by hand, but has the following disadvantages: the grasping stability of different objects is insufficient, and most robotic arms only grasp a single specific structure, and it is easy to knock down the object to be grasped

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
  • Mechanical-arm hand object grabbing method based on depth learning
  • Mechanical-arm hand object grabbing method based on depth learning
  • Mechanical-arm hand object grabbing method based on depth learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0060]Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0061] A method for grabbing objects with a robotic arm based on deep learning, the process is as follows figure 1 As shown in Fig. 1, the binocular camera, workstation and multi-degree-of-freedom robotic arm are used to realize the voice control of the robotic arm to grasp the object, and the object to be grasped is ideally grasped, and the angles of the motors of each joint of the robotic arm are recorded at this time, and the mapping is done. relationship, an object corresponds to a set of theoretical angle values ​​of the manipulator motor; the specific steps include:

[0062] Step 1: Specific person speech training; specifically includes the following steps:

[0063] Step 1.1: Preprocess the speech signal sequence X(n) to obtain the sequence X m After (n), perform Fourier transform:

[0064] X(i,k)=FFT[X m (n)];

[0065] Ordinary line e...

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 mechanical-arm hand object grabbing method based on depth learning, and belongs to the technical field of multi-degree-of-freedom manipulator control. Before object grabbing,labels are made in advance by adopting a depth learning network structure, training classification is conducted, and a database is made with one label corresponding to one grabbing angle; and when auser says an instruction, a to-be-grabbed object is determined through speech recognition, then the object is found through image recognition and positioning, an image coordinate and the mechanical-arm hand grabbing angle are returned, a BP neural network subjected to particle swarm optimization corrects the image coordinate, finally, the rotating angles required by all motors are resolved reversely through a GRNN network, and grabbing of a manipulator is finished after rotating to the target. According to the mechanical-arm hand object grabbing method based on depth learning, grabbing of theselected target can be achieved, and meanwhile, the problem of instable grabbing is avoided.

Description

technical field [0001] The invention belongs to the technical field of multi-degree-of-freedom manipulator control, and in particular relates to a method for grasping an object by a manipulator hand based on deep learning. Background technique [0002] With the continuous development of society, people's demand for social services will also increase, and the elderly and disabled people have also become the focus of attention. The rapid growth of the elderly population has led to the seriousness of aging in our country. According to statistics, the population over the age of 60 has reached more than 230 million in 2016, but there are not so many nursing staff to take care of these elderly people. Not only that, a large number of disabled people also need a large number of nursing staff. Traditional nursing methods can no longer meet the needs of the current social situation, and advanced nursing robots will improve the lives of the elderly and the disabled. As nursing robo...

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
Patent Type & Authority Applications(China)
IPC IPC(8): B25J9/16B25J9/22
CPCB25J9/0081B25J9/16B25J9/1679
Inventor 王传江侯鹏亮王栋朱坤怀张远来袁振孙秀娟
Owner SHANDONG UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com