Mobile robot repositioning method based on deep learning

A mobile robot and deep learning technology, applied in the field of robotics, can solve problems that need to be improved, and achieve the effect of promoting detection data, improving accuracy, and improving positioning accuracy

Pending Publication Date: 2022-08-05
河南省吉立达机器人有限公司
View PDF1 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Patent application 202210165624.3 discloses a positioning method for autonomous mobile robots based on deep learning, which corrects position information and detection data with the help of pre-trained deep learning neural networks, thereby improving the positioning accuracy of autonomous mobile robots, corresponding to existing mobile The requirements for robot accuracy still need to be improved. For this reason, this application proposes a mobile robot relocation method based on deep learning

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
  • Mobile robot repositioning method based on deep learning
  • Mobile robot repositioning method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments.

[0023] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inside", " The orientation or positional relationship indicated by "outside" is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation, so as to The specific orientation configuration and operation are therefore not to be construed as limitations of...

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 mobile robot repositioning method based on deep learning, a mobile robot comprises a robot attitude control system, a real object data acquisition system and a map model analysis and strategy experience system, and the real object data acquisition system comprises a visual camera, a laser ranging sensor and an anchor frame illumination device. The repositioning method comprises the following specific steps: S1, a real object data acquisition system of a mobile robot; s2, generating a moving path; s3, performing feature evaluation on the strategy experience system; s4, adjusting and controlling the posture of the mobile robot; and S5, action execution. According to the repositioning method, a real object data acquisition system, map model analysis and generation of a moving path are utilized to match a navigation path of an offline map model, the mobile robot is repositioned, the situation that the robot cannot be positioned due to data loss is avoided, the accuracy of tableware surrounding environment data of a visual camera is improved by arranging an anchor frame for illumination, and the service life of the mobile robot is prolonged. Therefore, the effect of improving the positioning precision is achieved.

Description

technical field [0001] The invention relates to the field of robotics technology, in particular to a deep learning-based mobile robot relocation method. Background technique [0002] With the maturity of artificial intelligence technology, robots have gradually developed from industrial robots to intelligent mobile robots that are convenient for people's lives. When the robot fails to track or is powered on and restarts, it must use relocation technology to restore the current pose of the robot, otherwise the robot Continued mapping or precise localization of the environment is not possible, so relocalization is important for mobile robots. [0003] Patent application 202210165624.3 discloses a deep learning-based positioning method for an autonomous mobile robot. With the help of a pre-trained deep learning neural network, the position information and detection data are corrected and processed, thereby improving the positioning accuracy of the autonomous mobile robot, corre...

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): G01C21/00G01C21/20G06N3/04G06N3/08
CPCG01C21/005G01C21/20G06N3/08G06N3/045
Inventor 邓坤霞刘大同刘铭
Owner 河南省吉立达机器人有限公司
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