Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter

In robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measureme...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mei Liu, Yuanli Cai, Lihao Zhang, Yiqun Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	portable mobile robot quaternion implementation complementary filter extended Kalman filtering attitude estimation Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/b64dbb5552d04fc7afab2a8c576beaf9
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b64dbb5552d04fc7afab2a8c576beaf9
record_format	dspace
spelling	oai:doaj.org-article:b64dbb5552d04fc7afab2a8c576beaf92021-11-25T18:23:29ZAttitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter10.3390/mi121113732072-666Xhttps://doaj.org/article/b64dbb5552d04fc7afab2a8c576beaf92021-11-01T00:00:00Zhttps://www.mdpi.com/2072-666X/12/11/1373https://doaj.org/toc/2072-666XIn robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measurement Unit (IMU) multi-sensor signals, the attitude data are corrected, and the high-precision attitude angles are obtained. In this paper, the quaternion algorithm is used to describe the attitude motion, and the process of attitude estimation is analyzed in detail. Moreover, the models of the sensor and system are given. Ultimately, the attitude angles are estimated by using the quaternion extended Kalman filter, linear complementary filter, and Mahony complementary filter, respectively. The experimental results show that the Mahony complementary filtering algorithm has less computational cost than the extended Kalman filtering algorithm, while the attitude estimation accuracy of these two algorithms is similar, which reveals that Mahony complementary filtering is more suitable for low-cost embedded systems.Mei LiuYuanli CaiLihao ZhangYiqun WangMDPI AGarticleportable mobile robotquaternion implementationcomplementary filterextended Kalman filteringattitude estimationMechanical engineering and machineryTJ1-1570ENMicromachines, Vol 12, Iss 1373, p 1373 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	portable mobile robot quaternion implementation complementary filter extended Kalman filtering attitude estimation Mechanical engineering and machinery TJ1-1570
spellingShingle	portable mobile robot quaternion implementation complementary filter extended Kalman filtering attitude estimation Mechanical engineering and machinery TJ1-1570 Mei Liu Yuanli Cai Lihao Zhang Yiqun Wang Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter
description	In robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measurement Unit (IMU) multi-sensor signals, the attitude data are corrected, and the high-precision attitude angles are obtained. In this paper, the quaternion algorithm is used to describe the attitude motion, and the process of attitude estimation is analyzed in detail. Moreover, the models of the sensor and system are given. Ultimately, the attitude angles are estimated by using the quaternion extended Kalman filter, linear complementary filter, and Mahony complementary filter, respectively. The experimental results show that the Mahony complementary filtering algorithm has less computational cost than the extended Kalman filtering algorithm, while the attitude estimation accuracy of these two algorithms is similar, which reveals that Mahony complementary filtering is more suitable for low-cost embedded systems.
format	article
author	Mei Liu Yuanli Cai Lihao Zhang Yiqun Wang
author_facet	Mei Liu Yuanli Cai Lihao Zhang Yiqun Wang
author_sort	Mei Liu
title	Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter
title_short	Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter
title_full	Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter
title_fullStr	Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter
title_full_unstemmed	Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter
title_sort	attitude estimation algorithm of portable mobile robot based on complementary filter
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b64dbb5552d04fc7afab2a8c576beaf9
work_keys_str_mv	AT meiliu attitudeestimationalgorithmofportablemobilerobotbasedoncomplementaryfilter AT yuanlicai attitudeestimationalgorithmofportablemobilerobotbasedoncomplementaryfilter AT lihaozhang attitudeestimationalgorithmofportablemobilerobotbasedoncomplementaryfilter AT yiqunwang attitudeestimationalgorithmofportablemobilerobotbasedoncomplementaryfilter
_version_	1718411220319469568

Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter

Ejemplares similares