A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data
arXiv:2606.19711v1 Announce Type: cross Abstract: Field-based modeling from onboard measurements can produce autonomous underwater vehicle (AUV) maneuvering models that reflect real operating characteristics. From an approximation perspective, conventional maneuvering models use predefined constraint polynomial bases, whereas data-driven models use data-adaptive bases. Motivated by this basis-function view, this paper presents a differentiable composite-approximation formulation, in which the polynomial-basis component and the data-adaptive basis component are treated as differentiable parts o
This research is emerging as autonomous systems, particularly AUVs, become critical for both commercial and defence applications, driving the need for more accurate and data-driven modeling.
Improved AUV maneuvering models, derived from real-world data and advanced AI techniques, directly enhance the reliability, autonomy, and operational capabilities of underwater systems.
The ability to accurately model AUV behavior using a hybrid approach of conventional and data-driven methods will lead to more robust and adaptable autonomous underwater vehicles.
- · Defence contractors
- · Marine robotics companies
- · Oceanographic research institutions
- · AI/ML model developers
- · Traditional model-based control systems
- · Human-operated submersibles
More efficient and reliable autonomous underwater vehicle (AUV) operations in diverse and challenging environments.
Accelerated development and adoption of AUVs for applications like seabed mapping, infrastructure inspection, and naval reconnaissance.
Strategic advantage for nations and corporations deploying highly autonomous and performable underwater fleets, potentially shifting geopolitical balances in maritime domains.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG