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Bio-inspired machine-learning aided geo-magnetic field based AUV navigation system.
Ananda Ramadass Gidugu1, Bala Naga Jyothi Vandavasi2, Vedachalam Narayanaswamy1
1Ministry of Earth Sciences, National Institute of Ocean Technology, Chennai, India.
Scientific Reports
|August 2, 2024
Summary
Sea animals inspire new navigation for autonomous underwater vehicles (AUVs). Using Earth's geo-magnetic field (GMF) and machine learning, AUVs can determine their position without acoustic systems, achieving high accuracy.
Area of Science:
- Robotics and Navigation
- Geophysics
- Machine Learning
Background:
- Animal navigation inspires new technologies.
- Autonomous underwater vehicles (AUVs) require precise positioning.
- Geo-magnetic field (GMF) offers a potential navigation source.
Purpose of the Study:
- To explore GMF-based navigation for AUVs.
- To achieve absolute positioning without acoustic systems.
- To assess machine learning's role in GMF navigation.
Main Methods:
- Applied supervised machine learning algorithms (Random Forest, Decision Tree) to GMF intensity data.
- Utilized NOAA World Magnetic Model for a 900 km² area in the Central Indian Ocean.
- Considered magnetometers with 0.1 nT sensitivity for data acquisition.
Main Results:
- Achieved absolute mean position accuracy in the 2D plane for AUVs.
- Demonstrated Circular Error Probable (CEP 50) of 53 m and 56 m.
- Validated GMF anomaly navigation as a viable GPS-alternative.
Conclusions:
- Scalar GMF anomaly navigation is a feasible GPS-alternative for AUVs.
- This method enables autonomous navigation without traditional acoustic systems.
- The system can be expanded to larger areas using inclination and declination vectors.
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