A localization method using a dynamical model and an extended Kalman filtering for X4-AUV

Keigo Watanabe, Takanori Yamaguchi, Isaku Nagai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The self-position estimation problem of X4-AUV, which is an autonomous underwater vehicle (AUV) driven by four thrusters, is considered. Since a self-position cannot be underwater measured directly using GPS etc., we have to consider any method for realizing it by an indirect method. The AUV treated by this research has a mechanical structure that a self-position is controlled by changing the attitude from the feature of drive mechanism, and it can observe an attitude angle from an internal sensor, so that based on the dynamical model of the present AUV, a method for estimating the self-position is proposed by applying an extended Kalman filter. The usefulness of this technique is demonstrated by checking the feasibility in the simulation of the position control that used the position estimate.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings
PublisherSpringer Verlag
Pages834-845
Number of pages12
Volume10462 LNAI
ISBN (Print)9783319652887
DOIs
Publication statusPublished - 2017
Event10th International Conference on Intelligent Robotics and Applications, ICIRA 2017 - Wuhan, China
Duration: Aug 16 2017Aug 18 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10462 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Intelligent Robotics and Applications, ICIRA 2017
CountryChina
CityWuhan
Period8/16/178/18/17

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Keywords

  • Extended kalman filter
  • Localization
  • Nonholonomic control
  • X4-AUV

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Watanabe, K., Yamaguchi, T., & Nagai, I. (2017). A localization method using a dynamical model and an extended Kalman filtering for X4-AUV. In Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings (Vol. 10462 LNAI, pp. 834-845). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10462 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-65289-4_77