A geometry-aware per-surface neural network surrogate predicts real-time hydrodynamic forces for amphibious AGVs from SDF submergence data, achieving low sMAPE on held-out CFD and reproducing quadratic drag and linear buoyancy scaling in full-scale vehicle trials without explicit encoding in theloss
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BIND-USBL bounds AUV IMU drift via intermittent USBL fixes from ASV teams, with performance governed by survey scale, acoustic coverage, team geometry, and a TDMA scheduler that improves fix rates without collisions.
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Geometry-Aware Surrogate for Real-Time Hydrodynamics Estimation of Autonomous Ground Vehicles in Amphibious Environments
A geometry-aware per-surface neural network surrogate predicts real-time hydrodynamic forces for amphibious AGVs from SDF submergence data, achieving low sMAPE on held-out CFD and reproducing quadratic drag and linear buoyancy scaling in full-scale vehicle trials without explicit encoding in theloss
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BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams
BIND-USBL bounds AUV IMU drift via intermittent USBL fixes from ASV teams, with performance governed by survey scale, acoustic coverage, team geometry, and a TDMA scheduler that improves fix rates without collisions.