Uncertainty-Aware Adaptive Dynamics For Underwater Vehicle-Manipulator Robots

2026-03-06

AI Summary

This work addresses the challenge of accurately modeling the time-varying dynamics of underwater robots, which are significantly influenced by hydrodynamics and thus resist precise adaptive representation. The authors propose an uncertainty-aware adaptive dynamics modeling framework that employs moving-horizon estimation to online identify lumped parameters of both the vehicle and its manipulator. While preserving parameter linearity, the approach embeds convex physics-based consistency constraints on inertia, damping, friction, and hydrostatic forces, and simultaneously quantifies the uncertainty in parameter evolution. Implemented on a BlueROV2 Heavy platform, the method achieves rapid convergence with a median solve time of 0.023 seconds, yields manipulator model fits with R² values between 0.88 and 0.98, enables high-fidelity motion reproduction, substantially reduces MAE and RMSE, and produces parameter confidence intervals with near-100% coverage.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Accurate and adaptive dynamic models are critical for underwater vehicle-manipulator systems where hydrodynamic effects induce time-varying parameters. This paper introduces a novel uncertainty-aware adaptive dynamics model framework that remains linear in lumped vehicle and manipulator parameters, and embeds convex physical consistency constraints during online estimation. Moving horizon estimation is used to stack horizon regressors, enforce realizable inertia, damping, friction, and hydrostatics, and quantify uncertainty from parameter evolution. Experiments on a BlueROV2 Heavy with a 4-DOF manipulator demonstrate rapid convergence and calibrated predictions. Manipulator fits achieve R2 = 0.88 to 0.98 with slopes near unity, while vehicle surge, heave, and roll are reproduced with good fidelity under stronger coupling and noise. Median solver time is approximately 0.023 s per update, confirming online feasibility. A comparison against a fixed parameter model shows consistent reductions in MAE and RMSE across degrees of freedom. Results indicate physically plausible parameters and confidence intervals with near 100% coverage, enabling reliable feedforward control and simulation in underwater environments.

Problem

• underwater vehicle-manipulator systems
• hydrodynamic effects
• time-varying parameters
• uncertainty-aware dynamics
• adaptive modeling

Innovation

• uncertainty-aware dynamics
• adaptive parameter estimation
• physical consistency constraints
• moving horizon estimation
• underwater vehicle-manipulator system