# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 1 Introduction
By capturing real-time images in complex environments with cameras and processing them through trained models, the method achieves coordinate positioning, providing critical technical support for automated installations in industrial production. [PERSON] et al. [18] developed a target recognition and positioning system based on a monocular PTZ camera. By integrating color and edge detection methods, and employing [PERSON]'s calibration method and the principle of similar triangles, the system achieves high-precision target positioning, offering valuable insights for addressing monocular vision measurement errors. [PERSON] et al. [19] designed an automatic aerial docking system based on the YOLOv4-tiny model and an RGB-D camera. The system's effectiveness in complex environments was validated through target detection and three-dimensional coordinate measurement. [PERSON] and [PERSON] [20] integrated the YOLOv8 algorithm with the PnP technique to address the automatic docking problem of a six-degree-of-freedom refueling robotic arm. While visual positioning technologies have shown significant advantages in dynamic target detection and long-distance recognition, their applicability for identifying and localizing large targets at close range is considerably reduced due to limitations such as viewing angles and lighting conditions. This is particularly problematic for precise docking tasks, such as the LNG loading-arm docking process, where the target flange needs to be detected and tracked in close proximity. To address these challenges, recent studies have shown that integrating additional sensors can significantly compensate for the limitations of visual positioning. For example, research combining laser rangefinders with binocular cameras has improved the localization accuracy of underwater large targets [21]. Another study demonstrated that fusing ultrasonic sensors with visual data significantly enhanced the stability and robustness of target recognition in indoor navigation and positioning [22]. Building on these insights, this research introduces a fusion approach of ultrasonic sensors and visual technology in the LNG loading-arm automatic docking process. This integration compensates for the limitations of visual positioning at close range, ensuring accurate identification and docking of the flange target, particularly in dynamic environments. The primary challenge of LNG loading-arm automatic docking lies in ensuring both docking accuracy and speed. To address these challenges, this paper proposes an automated docking system, which consists of two core components: (1) a phased automatic docking scheme and its motion-control logic; and (2) a deep learning-based target detection and tracking system utilizing machine vision. The proposed design aims to enhance the operational efficiency and reliability of the LNG loading arm through the integration of effective control logic and intelligent visual systems. The main innovations of this study are as follows:  
* A phased design for the LNG loading-arm docking process is proposed, which is divided into \"coarse docking\" and \"fine docking\", with corresponding control logic developed for each phase;
* The preliminary identification of the target flange is achieved using a monocular camera combined with the YOLOv8 model, providing fast and stable positioning capabilities;
* A multi-level positioning technique combining monocular cameras and ultrasonic sensors is introduced to further enhance docking accuracy and operational reliability. The remainder of this study is organized as follows: Section 2 introduces the overall architecture of the LNG loading-arm automatic docking system and the segmentation of docking stages. Section 3 proposes a positioning method based on the combination of image features and geometric constraints, and investigates its adaptability under complex viewing angles. Section 4 presents the performance validation of the system through simulation experiments. Section 5 concludes the paper and outlines future research directions.