Gaussian Process-Augmented Model Predictive Contouring Control for 6-DoF Trajectory Tracking with a Holonomic AUV
Gaussian Process-Augmented Model Predictive Contouring Control for 6-DoF Trajectory Tracking with a Holonomic AUV
This research presents a nonlinear Model Predictive Contouring Control (MPCC) approach for holonomic Autonomous Underwater Vehicles (AUVs) that improves trajectory tracking accuracy by augmenting the nominal dynamics model with residual dynamics learned via Gaussian Process regression. The method integrates the learned residuals into an optimal control problem using zero-order robust optimization, maintaining real-time performance at 25 Hz. Validation includes simulation ablations and in-water experiments on the Multimodality, Observing, Low-cost, Agile (MOLA) AUV, comparing the proposed controller against a nominal MPCC and a standard PID controller.
The paper proposes a nonlinear Model Predictive Contouring Control (MPCC) framework augmented with Gaussian Process regression to handle dynamics inaccuracies in holonomic Autonomous Underwater Vehicles (AUVs), validated through simulations and in-water experiments on the MOLA AUV platform.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Control
- Reported autonomousOpen source S2
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
arXiv:2611.14373v1 Announce Type: new Abstract: Small, holonomic Autonomous Underwater Vehicles (AUVs) enable close-range inspection of underwater structures from arbitrary viewpoints, requiring accurate position and attitude tracking under state and input constraints. Model Predictive Contouring Control (MPCC) naturally handles these requirements, but relies on an accurate dynamics model, which is difficult to obtai
Open source S2
n underwater. In this paper, we present a nonlinear MPCC in all six Degrees of Freedom (DoF) for a holonomic AUV and alleviate the inaccuracies in the dynamics by augmenting the nominal model employed with residual dynamics learned via Gaussian Process (GP) regression. The learned residual is embedded in the optimal control problem through a zero-order robust optimization scheme, which keeps the controller real-time
Open source S3
capable at 25 Hz. A simulation ablation shows that residual selection is essential when designing the controller, and simulation and in-water experiments with the Multimodality, Observing, Low-cost, Agile (MOLA) AUV validate the resulting controllers against a nominal MPCC and a field proven Proportional-Integral-Derivative (PID) controller.
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org