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

Greedy K-mean self-organizing neural network multi-robot path planning method

A K-means, neural network technology, applied in instruments, adaptive control, control/regulation systems, etc., can solve problems such as inability to solve multi-robot path planning, low operating efficiency of multi-robot systems, and unbalanced multi-robot load, etc. To achieve the effect of improving load imbalance, good scalability, and meeting the needs of engineering applications

Active Publication Date: 2021-08-20
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the technical problem that the existing self-organizing neural network cannot solve the multi-robot path planning. The path planning does not consider whether the load of each robot in the multi-robot system is balanced, which eventually leads to the technical problem that the overall operation efficiency of the multi-robot system is low. A greedy K-means self-organizing neural network multi-robot path planning method is creatively proposed. By improving Self-organizing neural networks, enabling them to be used to solve multi-robot path planning problems
At the same time, the greedy K-means self-organizing neural network path planning algorithm is proposed for the first time, which can effectively solve the load imbalance problem of multiple robots

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
  • Greedy K-mean self-organizing neural network multi-robot path planning method
  • Greedy K-mean self-organizing neural network multi-robot path planning method
  • Greedy K-mean self-organizing neural network multi-robot path planning method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0076] like figure 1 As shown, a greedy K-mean self-organized neural network multi-robot path planning method.

[0077] Take the standard travel provider TSPLIB dataset KRA100 as an example, figure 2 It is the original task data of Kroa100 data set, and the number of robots is 5 sets, using a greed of K-mean self-organizing neural network algorithm for multi-robot path planning.

[0078] Step 1: Use the greed K-mean algorithm to obtain the tasks required to each robot

[0079] Step 1.1: Start iteration, determine the number of clusters K, the maximum number of iterations greedykmeans_max_iter, the bias multiples δ, initialization K poly class c = {c = {C 1 , C 2 , ..., c k }.

[0080] Step 1.2: Calculate all task points g = {g 1 , g 2 , ..., g n } The European distance from the K polylass center, respectively.

[0081]

[0082] Where D ij Indicates task point g i To the cluster center C j European distance, (x i Y i ) Represents task point g i Position coordinates. (x j Y j ) Re...

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 relates to a greedy K-mean self-organizing neural network multi-robot path planning method, and belongs to the technical field of artificial intelligence and robot system control. According to the method, the task allocation problem and the path planning problem are considered together, a two-stage solving mode is adopted, robot task allocation is completed in the first stage, and path planning is conducted in the second stage according to the task allocation result in the first stage. In a K-mean iteration process, a greedy algorithm is used to estimate path cost required for task execution by each robot, the size of an adjustment factor is guided through the path cost, and a task allocation result in a clustering process is adjusted through the adjustment factor. The problem of unbalanced robot load of a K-means clustering algorithm and a self-organizing neural network algorithm is solved, a task execution path scheme of load balancing is efficiently planned for each robot, and the method has the advantages of being high in universality and robustness.

Description

Technical field [0001] The present invention relates to a robot path planning method, and specifically, a plurality of robotic path planning methods of greed K-average self-organizing neural network, belonging to artificial intelligence and robotic system control technology. Background technique [0002] Since the 20th century, industrial robots have gradually entered the universal period, growing in an amazing speed in a global scale and rapidly updating iteration with high speed, high precision, system integration, and intelligence. [0003] The path planning of multi-independent robots is always one of the important issues in the field of robots. The core objective of the mobile robot system path plan is that each robot within the system has a specific discrete start position, task point location, and end point position, which can plan from the starting position from the starting point, and experience each item. After the task, finally reached the path to the end position. [...

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): G05B13/02
CPCG05B13/024
Inventor 赵清杰种领张长春方凯仁陈涌泉
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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