Uncertainty-Aware Adaptive Dynamics For Underwater Vehicle-Manipulator Robots
2026-03-06 • Robotics
Robotics
AI summaryⓘ
The authors developed a new way to better model underwater robots with arms, accounting for changing water effects that make things harder to predict. Their method uses a smart math approach to keep the model realistic and updates it in real-time while figuring out how certain the predictions are. They tested it on a real robot and showed it quickly learned accurate parameters, improving control and simulation performance compared to fixed models. This helps underwater robots work more reliably despite complex conditions.
underwater vehicle-manipulator systemadaptive dynamics modelhydrodynamic effectsmoving horizon estimationparameter estimationconvex physical constraintsBlueROV2feedforward controlmodel uncertaintyonline estimation
Authors
Edward Morgan, Nenyi K Dadson, Corina Barbalata
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.