Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering control policy using a pre-collected dataset of USV path-following trials. A Markov decision process (MDP) tailored for path following is formulated. The proposed offline RL steering controller, trained on static datasets, demonstrates improved sample efficiency and asymptotic performance due to an expanded ensemble of Q-networks. The accuracy and adaptive learning capabilities of the RL controller are validated through simulations and free-running tests.

Bottom detection and tracking from multibeam echosounder water-column data constitute a fundamental step to 3D seabed mapping. Traditional methods based on amplitude thresholding and phase detection rely heavily on heuristic assumptions and are prone to failure in the presence of interference in water-column data. To address these limitations, we propose a novel deep convolutional architecture that models full-swath spatial context, and couple it with a parallelized sliding-window pipeline for end-to-end inference on high-resolution backscatter signals. The proposed model was trained and evaluated on a combined dataset of public deep-water multibeam water-column data acquired by Kongsberg EM302 and EM710 systems. The bottom detection results demonstrate that in deep-water scenarios, our method obtained the correct bottom position, whereas the traditional methods yielded inaccurate or no detection results. Furthermore, applying knowledge distillation yields a lightweight model that achieves a high inference speed on an embedded edge device. The proposed method substantially suppresses interference-induced false detections and provides a robust, real-time solution for marine bathymetry.