DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation
2026-06-22 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors address the problem of underwater robots (AUVs) struggling to accurately figure out their speed when their sensors give incomplete or noisy data. They created DVL-DeepONet, a special deep learning model guided by physics to estimate the robot's velocity even when some sensor beams are missing or when only certain types of sensors are available. Their approach uses real-world data and consistently follows known measurement rules, leading to more reliable navigation. Tests show their method is about 40% better than existing techniques.
Autonomous Underwater Vehicles (AUVs)Doppler Velocity Log (DVL)Inertial sensorsDeep neural operatorVelocity estimationSensor noiseMachine learningPhysics-guided learningBeam measurement recoveryNavigation systems
Authors
Arup Kumar Sahoo, Itzik Klein
Abstract
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 degraded navigation solutions. To circumvent these challenges and enable resilient underwater navigation, we propose DVL-DeepONet, a physics-guided deep neural operator framework along with three variants. The proposed models are designed to estimate DVL-based velocity information under multiple operational scenarios, including (i) noise-resilient estimation in coupled inertial/DVL measurements, (ii) DVL-only learning, and (iii) beam measurement recovery. By learning a nonlinear operator that maps temporal inertial/DVL observations directly to vehicle velocity while enforcing DVL measurement physics through a consistency constraint, the proposed approach enables robust velocity estimation even under degraded sensing conditions. The proposed framework is validated using real-world AUV experiments, comprising a cumulative path length of approximately 10,000 m. Experimental results demonstrate that the proposed DVL-DeepONet architectures outperform baseline model-based approaches and learning-based algorithms by 40%.