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Research on Navigation Method for Subsea Drilling Robot Based on Inertial Navigation and Odometry.

Yingjie Liu1,2, Peng Zhou3, Feng Xiao3

  • 1Hangzhou Universal Control Mechatronical Engineering LLC, Hangzhou 310005, China.

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|May 4, 2026
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Summary
This summary is machine-generated.

This study introduces a robust navigation method for seafloor robots, enhancing accuracy in soft seabed conditions by mitigating slippage errors with a novel Kalman filter. The method significantly reduces positioning errors, improving deep-sea drilling robot navigation.

Keywords:
integrated navigationnon-Gaussian noiserobust square-root cubature Kalman filterslippage compensationsubsea drilling robot

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Area of Science:

  • Robotics
  • Geotechnical Engineering
  • Navigation Systems

Background:

  • Seafloor drilling robots operating in soft sedimentary layers face navigation accuracy divergence.
  • Challenges include drilling-induced slippage and sensor mismatches, particularly in deep-sea environments.
  • Existing navigation systems struggle with the large-deformation characteristics of the seabed under drilling loads.

Purpose of the Study:

  • To propose a robust navigation method for seafloor drilling robots.
  • To address accuracy divergence caused by slippage and sensor errors in soft seabed conditions.
  • To enhance the reliability and applicability of integrated navigation systems in complex subsea environments.

Main Methods:

  • Developed a unified state-space model incorporating time-varying odometer scale-factor error for seabed mechanical characteristics.
  • Employed a square-root cubature Kalman filter (SRCKF) framework to handle nonlinear systems and non-Gaussian noise, preserving positive definiteness via QR decomposition.
  • Implemented an online fault detection mechanism with a modified chi-square test and a two-segment IGG weighting function for adaptive variance inflation, identifying and down-weighting slippage-induced abnormal observations.

Main Results:

  • The proposed robust square-root cubature Kalman filter (RSRCKF) method effectively mitigates 'false displacement' disturbances from soft clay slippage.
  • Field experiments on tidal mudflats demonstrated enhanced statistical robustness against slippage conditions.
  • Achieved an approximate 82.4% reduction in positioning error compared to conventional SINS/OD integration schemes.

Conclusions:

  • The proposed navigation method significantly improves positioning accuracy for seafloor drilling robots in challenging soft seabed environments.
  • The RSRCKF demonstrates robustness and engineering applicability, overcoming limitations of traditional navigation systems.
  • Validated effectiveness in mitigating disturbances caused by seabed slippage, crucial for deep-sea operations.