Uncertainty-Aware Adaptive Dynamics For Underwater Vehicle-Manipulator Robots

TL;DR

This paper presents an uncertainty-aware adaptive dynamics model for underwater vehicle-manipulator robots that ensures physical consistency, rapid online estimation, and improved predictive accuracy under hydrodynamic effects.

Contribution

It introduces a novel linear, uncertainty-aware adaptive model with convex physical constraints and moving horizon estimation for real-time underwater robot dynamics.

Findings

Achieved high R2 scores (0.88-0.98) in manipulator fitting.

Demonstrated rapid convergence with median update time ~0.023 s.

Significantly reduced prediction errors compared to fixed parameter models.

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.