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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.
Sensors (Basel, Switzerland)
|May 4, 2026
Summary
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.
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.
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