DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation
Autonomous Underwater Vehicles (AUVs) rely heavily on the fusion of inertial sensors and Doppler velocity logs (DVLs) for navigation. In standard autonomous navigation systems, the DVL measures four beam velocities, thereby enabling the estimation of the AUV velocity vector. However, during real-world missions, the DVL may receive noisy or incomplete beam measurements due to marine obstacles, seabed reflections, or environmental disturbances. Furthermore, some low-cost underwater platforms operate without inertial sensors to reduce system complexity and cost. In such cases, reliable estimation of the AUV velocity vector in real-world missing beam scenarios becomes challenging, leading to ...
| Authors | Arup Kumar Sahoo, Itzik Klein |
|---|---|
| Primary category | cs.RO |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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