Underwater docking enhances the operational capabilities of Autonomous Underwater Vehicles (AUVs) by facilitating energy and data transfer. Optical beacons serve as the primary guidance method for AUVs to localize and track docking stations. This paper presents VIP-Dock, a novel optical beacon tracking algorithm for robust underwater docking of AUVs. VIP-Dock addresses the challenge of maintaining accurate beacon tracking under visual interference by integrating visual, inertial, and pressure perception. Employing an unscented Kalman filter framework, the VIP-Dock algorithm provides continuous optimal estimation of beacon positions. Experimental results demonstrated VIP-Dock's real-time tracking performance in actual docking scenarios and its ability to maintain accuracy during visual input failure. Implementation in a simulation platform for an underwater vertical shuttle showed significant improvement, increasing docking success rates from 62% to 84% across 100 trials under simulated current disturbances.

Learning from Demonstration (LfD) provides an efficient approach to acquiring diverse underwater skills, with task-parameterized learning enhancing the generalization of policies. However, collecting comprehensive underwater demonstrations across various conditions remains a significant challenge. In this work, we propose an adversarial trajectory augmentation method for Task Parameterized Hidden Semi-Markov Models (TP-HSMM) based on digital twins, inspired by adversarial example generation. Our method aims to improve the performance of motion policies by utilizing adversarial trajectory generation and retraining, leveraging low-cost demonstrations from digital twins. We evaluate the proposed adversarial trajectory augmentation method on two datasets. Comparative experiments demonstrate its effectiveness in reducing trajectory generation errors in new scenarios. Finally, we validate the method through an underwater humanoid plugging experiment, showing that it achieves similar performance to the baseline with fewer demonstrations.

Autonomous Underwater Vehicles (AUVs) require energy-efficient and responsive attitude control for underwater operations. We present UVS, a novel underwater vehicle that combines Variable Center of Mass System (VCMS) and thrusters for hybrid attitude regulation. Through multi-objective optimization of the VCMS structure, we achieved a 5.19% larger pitch angle range while reducing space occupation by 15.72%. Pool experiments demonstrated near-linear pitch control from 17.5° to 172.5° with stable horizontal-vertical mode transitions. Our proposed collaborative control method integrates VCMS and thruster advantages, enabling rapid convergence to target attitudes with long-term stability. The results show UVS’s potential for energy-efficient, wide-range attitude control in mobile ocean sensing applications.