Online self-learning multi-joint motion planning method based on neural network

A technology of motion planning and neural network, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of difficult manipulator self-learning real-time motion planning, cumbersome solution process, low efficiency, etc., to achieve automatic Learning and control, high computing efficiency, and the effect of time delay

Active Publication Date: 2019-07-23
BEIJING RES INST OF PRECISE MECHATRONICS CONTROLS
View PDF8 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the solution of the working space of the manipulator in the three-dimensional space involves a variety of sampling numerical methods, multi-joint synchronous trajectory planning control, and its complex nonl

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
  • Online self-learning multi-joint motion planning method based on neural network
  • Online self-learning multi-joint motion planning method based on neural network
  • Online self-learning multi-joint motion planning method based on neural network

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0036] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0037] The neural network method can ignore the specific physical parameters of the process or system. Through the learning of training samples, it can realize the complex nonlinear mapping between input and output, and has good generalization ability. It has the best global approximation performance and the training method is fast It is easy to do, and there is no local optimal problem. The neural network is used to establish the functional relationship between the trajectory-related information such as joint position and angle and the trajectory-related information at the previous N moments to predict the trajectory-related information.

[0038] Specifically, such as figure 1 As shown, the online self-learning multi-joint motion planning method of the present invention is implemented through the following steps:

[0039] (1) Collect trajectory-relate...

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 an online self-learning multi-joint motion planning method based on a neural network. According to the method, real-time self-learning and control of a nonlinear complex path are achieved by utilizing the global optimal approximation performance of the neural network. Due to the fact that trajectory planning control is synchronously carried out by multiple joints and has acomplex nonlinear relation with time, the online self-learning of the multiple joints is achieved by establishing a neural network model of a time correlation sequence of each joint position, and themethod is used for real-time control of an intelligent mechanical arm; and the difficulty of numerical solution is greatly reduced, the operation efficiency is improved, and the real-time self-learning capability is achieved.

Description

Technical field [0001] The invention relates to an online self-learning multi-joint motion planning method based on a neural network, which is used for online self-learning to realize the motion planning and control of a multi-joint mechanical arm, belongs to the field of intelligent robot trajectory planning and control, and is particularly suitable for realizing mechanical arm pairing Discovery, real-time self-learning and control of new trajectory paths. Background technique [0002] With the development of technology, intelligent robots have received extensive attention and applications. They include highly integrated equipment of multiple disciplines such as mechanical structure, drive, and control. The robotic arm of intelligent robots generally contains multiple drive joints, and its trajectory control determines The precision, service and application of the robotic arm. Robotic arm motion trajectory control is mostly used in three-dimensional space. At present, state spa...

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): B25J9/16G06N3/04G06N3/08
CPCB25J9/1664G06N3/08G06N3/045
Inventor 郭雅静朱晓荣赵青陈靓郭喜彬
Owner BEIJING RES INST OF PRECISE MECHATRONICS CONTROLS
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