Robust Magnetic Tracking of Subsea Cable by AUV in the Presence of Sensor Noise and Ocean Currents - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles IEEE Journal of Oceanic Engineering Year : 2018

Robust Magnetic Tracking of Subsea Cable by AUV in the Presence of Sensor Noise and Ocean Currents

Abstract

To inspect subsea cables, an autonomous underwater vehicle (AUV) with two triaxial magnetometers is usually assigned to track the cable route. In this paper, a novel two-layer framework synthesizing antinoise cable localization and robust tracking algorithm is proposed to guide an AUV to track subsea cables in the presence of sensor noise and ocean currents. First, an analytic formulation for the cable localization using two magnetometers is derived, and then a dedicated magnetic line-of-sight (LOS) guidance is built based on the horizontal offset between the AUV and the cable. Second, a novel antinoise method by estimating the horizontal offset is integrated into the LOS guidance law to reduce the negative effects of magnetic noise in the kinematic layer. Subsequently, in the dynamic layer, a simplified yet robust feedback controller with reduced implementation complexity is designed to track the desired guidance profiles, such that the AUV is able to track subsea cables in the presence of sensor noise and ocean currents. In addition, the capability of dynamic control laws accounting for ocean currents is analyzed in the amplitude–frequency domain. Finally, numerical studies illustrate the antinoise and robust performance of the proposed two-layer framework for subsea cable tracking.
Fichier principal
Vignette du fichier
08126782.pdf (1.35 Mo) Télécharger le fichier
Loading...

Dates and versions

lirmm-01904345 , version 1 (24-10-2018)

Identifiers

Cite

Caoyang Yu, Xianbo Xiang, Lionel Lapierre, Qin Zhang. Robust Magnetic Tracking of Subsea Cable by AUV in the Presence of Sensor Noise and Ocean Currents. IEEE Journal of Oceanic Engineering, 2018, 43 (2), pp.311-322. ⟨10.1109/JOE.2017.2768105⟩. ⟨lirmm-01904345⟩
181 View
724 Download

Altmetric

Share

Gmail Facebook X LinkedIn More