An accelerometer-based gyro drift compensation approach in a dual-axial stabilization platform is introduced in this paper. The stabilization platform consists of platform framework, drive motor, gyro and accelerometer module and contorl board. Gyro is an angular rate detecting element to achieve angular rate and rotation angle of the dynamic platform system. However, the platform system has an unstable factor because of the drift of gyro. The main contribution of this paper is to implement a convenient gyro drift compensation approach by using the accelerometer. In contrast to a kalman filtering method, this approach is simpler and practical due to the high-precision characteristic of the accelerometer. Data filtering algorithm and limit of threshold setting of total acceleration values are applied in this approach. The validity and feasibility of the proposed approach are evaluated by four tests under various conditions.

The violent swaying of gangway when maintenance vessel dock at wind turbine tower poses a severe threaten to the safety of maintenance personnel. Therefore, ensuring the stable docking of gangway is of vital importance for safeguarding the maintenance personnel. However, marine-surface vehicles often experience large-scale irregular movements due to the coupling effects of wind, waves, surge and other disturbances, posing unique challenges. Real-world factors such as parallax and depth variations, the breakdown of the brightness constancy assumption, and texture absence constrained the accuracy of current optical flow estimation methods. To solve this issue, an offset-field-based unsupervised optical flow learning framework called R-OFFlow algorithm is proposed. Specifically, the motion field module called offset field is formed by integrating the gyroscope data and heave information at first. Secondly, the self-guided offset fusion module is designed, which fuses the background motion extracted from the offset field with the optical flow. Thirdly, the feature extraction and optical flow refinement methods by Recurrent All-Pairs Field Transforms (RAFT) are integrated to enhance the feature extraction. The combination of the above modules guides the network to focus on motion details, thus achieving high-precision unsupervised optical flow learning. Furthermore, a dedicated dataset called Wind Power Optical Flow (WPOF) is constructed. Finally, the comparative analysis experimental results demonstrated that the proposed method improves by at least 12.12% compared to other algorithms in various complex offshore scenarios, outperforming state-of-the-art methods. Ablation studies further validate the effectiveness of the modules in the proposed algorithm.

Offshore wind power generation is recognized as an indispensable component of renewable energy systems. However, growing concerns regarding operational safety have emerged with the increasing deployment of offshore wind turbine foundations. Current maintenance methodologies predominantly rely on vessel-based approaches, where technicians access turbine equipment via gangways, rendering the secure transfer of personnel onto turbine structures a critical operational requirement. Conventional stabilization platforms for offshore wind operation-and-maintenance vessels predominantly employ six-degrees-of-freedom platforms controlled by classical control strategies, yet these mechanical systems frequently encounter limitations in response speed, positioning accuracy, and cost-effectiveness. To address these challenges, a novel Propagation-aware Residual-corrected Adaptive Flow Estimation (PRA-Flow) optical flow estimation method based on the GMFlow algorithmic framework is proposed. The methodology leverages the inherent advantages of feature matching combined with computer vision techniques, where global image correspondence is achieved through Transformer architectures. Three core enhancements are implemented: dynamic window partitioning, occlusion-aware propagation, and recurrent iterative refinement of the baseline algorithm. These innovations not only improve estimation accuracy but also maintain computational efficiency. For comprehensive evaluation, the WPOF+ dataset is constructed, encompassing four complex operational scenarios (offshore maintenance, base commissioning and maintenance, gangway maintenance, and nearshore maintenance) and five evaluation conditions (reference maritime scenes and four synthetic conditions: Dark, Snow, Fog, and Rain). Comparative experiments and ablation studies demonstrate that the proposed method achieves an overall mean EPE of 1.878, compared with 1.891 for RAFT, corresponding to a 0.70% reduction, while ranking second in the runtime comparison. These results demonstrate the effectiveness of PRA-Flow in optical flow estimation under complex offshore environments.