Gaussian Process-Augmented Model Predictive Contouring Control for 6-DoF Trajectory Tracking with a Holonomic AUV
Overview
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.
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“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”
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- arXiv Robotics — research abstractsSignalGaussian 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.
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