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

Two-wheel self-balancing robot attitude calculation method based on improved Extended Kalman Filter algorithm

A technique for extending Kalman and filtering algorithms, which is applied in the field of attitude calculation of two-wheel self-balancing robots, and can solve the problems of high filtering accuracy, loss of quadratic term value, and inconvenience.

Active Publication Date: 2015-01-28
南京港能环境科技有限公司
View PDF3 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The attitude calculation method of the Kalman filter method is simple in modeling and has good real-time performance, but it ignores the influence of nonlinear factors, especially the influence of the carrier displacement acceleration on the attitude measurement information; the attitude calculation method of the extended Kalman filter method has better real-time performance, but Linearization loses part of the quadratic term value, and the linearization error is large
Tasteless Kalman filter and particle filter have high precision, poor real-time performance, and are not simple enough

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
  • Two-wheel self-balancing robot attitude calculation method based on improved Extended Kalman Filter algorithm
  • Two-wheel self-balancing robot attitude calculation method based on improved Extended Kalman Filter algorithm
  • Two-wheel self-balancing robot attitude calculation method based on improved Extended Kalman Filter algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0039] The present invention is applicable to the attitude calculation of a two-wheel self-balancing robot, and is also applicable to a two-wheel self-balancing electric vehicle. Different inertial sensors have different random drift characteristics. Therefore, the error model and attitude calculation model of the inertial sensor are determined first, and the improved extended Kalman filter is used to perform data fusion on the three-axis gyroscope acceleration and attitude measurement data obtained by sampling. Get the attitude information of the two-wheeled self-balancing robot. Below is the concrete technical implementation scheme of the present invention, comprises the steps:

[0040] Step 1, use the gyroscope to calculate the attitude of the two-wheeled self-balancing robot, and obtain the roll angle γ, pitch angle θ and three-axis angular velocity ω x , ω y , ω z relation:

[0041] { γ . ...

Embodiment

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0075] (1) Static experiment: place the two-wheeled self-balancing robot vertically, and conduct a static experiment under the condition of zero input. At this time, it is considered that there is no influence of non-gravity displacement acceleration, and the curves shown in Figure 2(a)(b) are obtained.

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 present invention relates to a two-wheel self-balancing robot attitude calculation method based on the improved Extended Kalman Filter algorithm. In the prior art, the existing two-wheel self-balancing robot attitude calculation method can not well meet accuracy, real-time property, simplicity and other requirements. Baed on the problems of the existing two-wheel self-balancing robot attitude calculation method, the method of the present invention utilizes the improved Extended Kalman Filter algorithm so as to effectively combine the inertial sensor attitude measurement data, compensate the gyroscope random drift error and reduce the influence of the displacement acceleration of the two-wheel self-balancing robot on the attitude calculation during moving. The two-wheel self-balancing robot attitude calculation method of the present invention can further be simultaneously applied for the two-wheel self-balancing electric vehicle. The results of static experiments, simulated platform experiments and practical dynamic experiments of the two-wheel self-balancing robot verify that the attitude calculation accuracy of the two-wheel self-balancing robot can be increased with the method of the present invention.

Description

technical field [0001] The invention is a method for calculating the attitude of a two-wheel self-balancing robot based on an improved extended Kalman filter algorithm. The invention is applicable to the attitude calculation of a two-wheel self-balancing robot, and is also applicable to a two-wheel self-balancing electric vehicle. Background technique [0002] Two-wheeled self-balancing robot is a typical nonholonomic, nonlinear and underactuated system. In order to realize the balance control of the two-wheeled self-balancing robot during its motion, it is necessary to have a detection system that can detect its attitude information in real time, and transmit the attitude information to the controller to realize the precise control of the two-wheeled self-balancing robot. Due to the characteristics of the inertial sensors that make up the attitude detection system, they are greatly affected by temperature and noise. When the inertial sensors work for a long time, the error...

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): G01C21/20
CPCG01C21/16G01C21/20
Inventor 周翟和胡佳佳虞波沈超赵庆涛
Owner 南京港能环境科技有限公司
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