A Model-Aided Inertial Navigation System for the Remus 100 AUV

Document Type : Dynamics, Vibrations, and Control

Authors

1 Department of Electrical and Computer Engineering, Malek-Ashtar University of Technology, Tehran, Iran

2 Department of Electrical and Computer Engineering, Malek Ashtar University of Technology

3 Electrical and Computer Engineering, Malek-Ashtar University of Technology, Tehran, Iran

Abstract

The use of autonomous underwater vehicles (AUV) for the exploration and oceanology science has been a field of interest of several research centers around the world in the last decade. The inertial navigation system (INS) has commonly been used as the principal means of localization for autonomous underwater vehicles. The main drawback of using only the inertia navigation system is the escalating error in the estimated position and attitude due to the error in the output of the inertia measurement unit and its integration. Traditionally external sensors such as: GPS, acoustic transponders, Doppler velocity logs (DVL), or cameras have been used to aid the solution of the inertial navigator by constraining the errors in the estimated positions. These external sensors have several practical disadvantages, basically related to their reliance on external information. One source of information that can assist in the localization of the vehicle, without the need for extra additional external sensing, is the vehicle’s dynamic model. The model of the vehicle is capable of representing the attitude of the system according to the control inputs and the external forces acting on it. In this work, we follow a model-aided inertial navigation system (MA-INS) for Remus 100 AUV based on a 6-DOF non-linear dynamic model. The proposed navigation system utilizes the knowledge of the device dynamics through an experimentally validated mathematical model. The results show that the proposed navigation system improves underwater navigation capabilities for the systems that lack conventional aiding equipment.

Keywords

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History

  • Receive Date: 19 January 2021
  • Revise Date: 08 May 2021
  • Accept Date: 18 December 2021
  • Publish Date: 23 October 2021

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