Artificial bee colony-based Monte Carlo localization method

A positioning method and artificial bee colony technology, applied in the field of positioning, can solve problems such as slow convergence speed, noise, and positioning failure, and achieve the effect of improving the exploration ability

Inactive Publication Date: 2018-05-08
JIANGXI HONGDU AVIATION IND GRP
View PDF3 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the constraints of the sensing range of the sensor and the presence of certain noise in the sensing data, it is easy to lead to inaccurate positioning and slow convergence speed; at the same time, the diversity of particle samples decreases in the later stage of the algorithm, and positioning is also prone to occur when the surrounding environment is similar failure problem

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
  • Artificial bee colony-based Monte Carlo localization method
  • Artificial bee colony-based Monte Carlo localization method
  • Artificial bee colony-based Monte Carlo localization method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific illustrations.

[0041] see figure 1 A Monte Carlo positioning method based on artificial bee colony, according to the traditional Monte Carlo positioning method, using N discrete particles with weights to simulate the posterior probability density function of the estimated state,

[0042] Bel(x)={x i ,ω i} i＝1…N

[0043] x i represents a particle, ω i Represents the weight of the particle, the specific steps are as follows:

[0044] 1) Initialize the initial particle sample set S1 of a certain number of particles in a given space

[0045] Select the coordinate value of the robot on the map and the angle with the x-axis direction as the state quantity (x, y, θ), each particle is three-dimensional, and randomly generate N particles according to the formula...

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 artificial bee colony-based Monte Carlo localization method. In the Monte Carlo localization method, a behavior of simulating honey collection of bees is added for realizingthe localization. The method comprises the steps of firstly, initializing an initial particle sample set S1 of a certain quantity of particles in a given space; secondly, building a robot motion model, and forming a primary new particle sample set S2 according to the motion model based on all the particles in the initial particle sample set S1; thirdly, building an observation model, and taking the observation model as a fitness function of an artificial bee colony algorithm; fourthly, taking the primary new particle sample set S2 as an initial honey source position of an artificial bee colony, and simulating the honey collection behavior of the bees for performing global optimization; and finally, updating particle weights, and calculating out a robot pose. The exploration capability ofthe particles is effectively improved to avoid localization failure due to the fact that the particles fall into local optimum in complex environments or environments with similar structures; and while the particle diversity is ensured, the real position of a robot is quickly converged.

Description

technical field [0001] The invention relates to the technical field of positioning, in particular to a Monte Carlo positioning method based on artificial bee colonies. Background technique [0002] Self-localization is an important and basic problem in the field of mobile robots, and it is a prerequisite for path planning, navigation and decision-making of mobile robots; Monte Carlo Localization (MCL) is a probability-based localization method that converts the position As many particle density models, each particle is used as a positioning hypothesis for the robot at this position, and all particles move according to the motion model of the robot; the probability of a particle representing the real position of the robot depends on the readings of the sensor in the perception model, and the particles move toward The higher probability sampled position moves, the probability average distribution is used to represent the best estimate of the current robot position. However, i...

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): G06N3/00G06N7/00G01S5/16
CPCG06N3/006G01S5/16G06N7/01
Inventor 史小露张磊周继强王丽峰郑友胜万贻辉
Owner JIANGXI HONGDU AVIATION IND GRP
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