Autonomous Navigation Systems for Underwater Vehicles
Summary
Autonomous navigation systems for underwater vehicles integrate a suite of sensors, algorithms and control strategies to enable self-sufficient operation in complex marine environments. Key challenges include the absence of satellite signals, strong currents, variable sound-speed profiles and limited visibility. Modern systems combine inertial measurement units, acoustic positioning, Doppler velocimetry, vision-based methods and advanced filtering techniques to estimate vehicle position and orientation in real time. Research has progressively shifted from standalone sensor solutions to tightly coupled, multi-sensor fusion frameworks incorporating machine learning and adaptive filters. These advances are unlocking longer missions, higher accuracy and collaborative behaviours in tasks ranging from seabed mapping and ecological monitoring to offshore infrastructure inspection and deep-sea exploration.
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Autonomous Navigation Systems for Underwater Vehicles publication trend
The graph below shows the total number of articles in autonomous navigation systems for underwater vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Autonomous Underwater Vehicle (AUV): A self-propelled submersible platform capable of performing missions without real-time human control.
Inertial Navigation System (INS): A navigation technique that uses accelerometers and gyroscopes to estimate position and orientation by dead reckoning.
Doppler Velocity Log (DVL): An acoustic sensor that measures relative velocity between a vehicle and the seabed or water column using Doppler shift.
Long Baseline (LBL): An underwater acoustic positioning system that uses fixed transponders on the seafloor to triangulate vehicle location.
References
- Underwater Robots and Key Technologies for Operation Control. Cyborg and Bionic Systems (2024).
- Improving the underwater navigation performance of an IMU with acoustic long baseline calibration. Satellite Navigation (2024).
- Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter. Sensors (2021).
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