# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 2 Development of Marine LNG Loading and Unloading Arms
### Marine Loading Arm Actuation and Its Intelligence and Automation
To address the challenge of precise positioning and docking for LNG loading and unloading arms in complex environments, [PERSON] [15] puts forward an automatic docking system integrating vision and multisensor fusion. By combining the YOLOv8 deep-learning model and geometric-constraint positioning method with distance feedback from ultrasonic sensors, the system enables a quick approach to the target flange in the coarse-docking stage and conducts high-precision attitude adjustment and docking in the fine-docking stage. This ensures a docking accuracy of 3 mm regardless of rainy or sunny conditions. Currently, a common automatic positioning system on the market is the IN-SIGHT series vision system of the American company COGNEX [37]. However, this system has high requirements for the loading operation environment. Factors such as humidity, light intensity, and stains on the equipment will interfere with the image recognition efficiency. The intelligence of loading and unloading arms should also be able to achieve remote monitoring and control of loading and unloading arms, as well as adaptively adjust their own parameters according to the specific requirements of on-site operations. The 716 Research Institute has developed a 3D real-time intelligent management and control system. Through digital twin technology, it can achieve real-time linkage between the loading equipment and the 3D model in the terminal control room, which can intuitively and clearly display the working status of the loading and unloading arms. In addition, by applying spatial visual positioning to the LNG loading field, a vision-based automatic docking system has been designed. It has successfully achieved accurate recognition of multiple target flanges in large-scale scenes, over long distances, and under complex and changeable working conditions. Through continuous iterative updates of deep learning, it can adapt to various ship-shore loading conditions. Without human intervention, it can automatically achieve precise docking between the QC/DC of the unloading arm and the manifold flange on the tanker. It is the world's first large-scale LNG unloading arm with an automatic docking function. At present, the automation and intelligence technologies of large-scale LNG loading and unloading arms are developing rapidly. Functions such as fast automatic dockingbased on visual recognition, 3D real-time intelligent visual remote management and control [38], and a safety operation and maintenance prediction system [39] based on big data have been successively applied to LNG loading and unloading arms, enabling the automatic docking of ship-shore LNG [40]. To further promote the intelligent and automated development of loading and unloading arms, efforts need to be made in multiple aspects. This includes optimizing adaptive control algorithms such as visual recognition algorithms and deep learning to ensure the stable operation of loading and unloading arms in complex environments and improve the accuracy of positioning target flanges [41; 42; 43]. Additionally, efforts should be made toward integrating the control systems and detection systems of loading and unloading arms, and leveraging big-data analysis [44] and digital twin technologies [45; 46] to achieve human-like functions such as one-click operation, automatic intelligent docking, remote status monitoring, and fault handling.