A magnetic beacon and MEMS-IMUs array cable fused positioning method for subsea stratum drilling robots

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Authors: Peng Zhou, Jiawang Chen, Peihao Zhang, Yongqiang Ge, Xueyu Ren, Bo Han, Xingshuang Lin, Liwen Nan
Cited by: 1
References: 32

Abstract

Abstract Accurate and robust positioning of drilling robots within subsea strata is a critical prerequisite for deep-sea resource exploration and geological monitoring. However, conventional underwater and underground positioning methods are often ineffective due to severe signal attenuation in sediment. This paper proposes a novel fused positioning method specifically for subsea stratum drilling robots, which integrates a magnetic beacon system with a MEMS-IMUs array cable. A piecewise constant curvature kinematic model is established to reconstruct the spatial morphology of the array cable, providing a continuous position estimate free from temporal integration drift. This estimate is then fused with absolute position and orientation data derived from a magnetic dipole model of the beacon. To handle the high nonlinearity of the magnetic measurements and the complementary error characteristics of the two subsystems, we develop a tightly-coupled sensor fusion framework based on an iterated extended Kalman filter with Levenberg–Marquardt optimization (IEKF-LM). Simulation and experimental results demonstrate that the proposed system effectively overcomes the limitations of individual sensors. The array cable excels in near-field accuracy, while the magnetic beacon provides superior far-field performance. The IEKF-LM fusion algorithm successfully combines their strengths, achieving high-precision and robust localization in both static and dynamic scenarios simulating in-stratum conditions. The results indicate that the proposed fusion method effectively mitigates the limitations of individual sensor modalities, offering a viable positioning solution for subsea exploration robots.

Fields of Study & Subjects

Underwater Vehicles and Communication Systems Inertial Sensor and Navigation Target Tracking and Data Fusion in Sensor Networks

Publication details

Journal / source
Measurement Science and Technology
Publisher
IOP Publishing
Volume
37
Issue
11
Pages
116303–116303
Publication date
3 March 2026