A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data

2026-06-18 · Source: Machine Learning · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new differentiable composite-approximation framework has been developed for autonomous underwater vehicle (AUV) maneuvering modeling, utilizing sea-trial data. This framework integrates both predefined polynomial-basis components and data-adaptive basis components into a single, jointly calibrated predictor. A gradient-based co-calibration method is employed, featuring a sensitivity-aware mechanism for bounded polynomial updates and a neural residual to capture nonlinear discrepancies. To address ocean-current effects in field data, the framework includes a turning-motion-based current estimation and compensation procedure, creating current-compensated learning targets. Evaluation using sea-trial data from a 7-meter AUV across multiple maneuvering conditions demonstrates improved recursive trajectory and velocity prediction compared to polynomial-only, neural-only, and frozen-prior hybrid baselines, confirming its applicability for field-data-based AUV modeling.

Key takeaway

For Robotics Engineers developing autonomous underwater vehicle (AUV) maneuvering models from sea-trial data, you should consider adopting a composite approximation framework. This approach, which jointly calibrates polynomial and data-adaptive bases, significantly improves recursive trajectory and velocity prediction. Integrating current estimation and compensation directly into your training pipeline will enhance model robustness and accuracy in dynamic ocean environments, leading to more reliable AUV operations.

Key insights

The framework combines polynomial and data-adaptive bases for AUV maneuvering, jointly calibrated with gradient-based methods and current compensation for improved prediction.

Principles

• Combining basis functions improves model accuracy.
• Joint calibration of components enhances prediction.
• Account for environmental factors like ocean currents.

Method

A gradient-based co-calibration method jointly optimizes polynomial and data-adaptive bases. It uses a sensitivity-aware mechanism and neural residual, plus turning-motion-based current estimation for data compensation.

In practice

• Apply composite models for AUV trajectory prediction.
• Integrate current compensation in field data training.
• Use gradient co-calibration for hybrid model optimization.

Topics

• Autonomous Underwater Vehicles
• Maneuvering Modeling
• Sea-Trial Data
• Differentiable Approximation
• Gradient-Based Calibration
• Ocean Current Compensation

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.