Online fault detection and model adaptation for Underwater Vehicles in the case of thruster failures

Georgios Fagogenis, Valerio De Carolis, David Michael Lane

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

13 Citations (Scopus)

Abstract

Autonomous Underwater Vehicles (AUVs) are required to carry out a mission with minimum supervision. Often, the AUV's hardware integrity is compromised amidst operation; thus, jeopardising the mission's success. Thruster failures, for example, may affect AUVs locomotion. Following a thruster failure, the plan may require changes to compensate, if possible, for the loss of mobility. In this paper, we present an algorithm that identifies thruster failures in run-time. Moreover, the algorithm corrects the vehicle's dynamical model to incorporate the defective thruster. The algorithm uses a Mixture of Gaussians representation for the vehicle's state. Variational Bayes Approximation has been utilised to yield the filtering equations. As indicated by experimental evaluation, the algorithm detects thruster-failure events correctly; and, in turn, learns an accurate dynamical model of the vehicle at its current state. Experiments were carried out on a real platform in a wave tank at Heriot-Watt University.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages2625-2630
Number of pages6
ISBN (Electronic)9781467380263
DOIs
Publication statusPublished - 9 Jun 2016
Event2016 IEEE International Conference on Robotics and Automation 2016 - Stockholm, Sweden
Duration: 16 May 201621 May 2016

Conference

Conference2016 IEEE International Conference on Robotics and Automation 2016
CountrySweden
CityStockholm
Period16/05/1621/05/16

Keywords

  • Bayesian inference
  • fault detection
  • Gaussian mixtures
  • model adaptation
  • underwater navigation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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