# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 5 Conclusions  
This paper proposes an automatic docking system for LNG loading arms based on vision and multisensor fusion to solve the challenge of precise positioning and docking of loading arms in complex environments. By introducing the YOLOv8 deep learning model and geometric-constraint positioning method, combined with the distance feedback information of the ultrasonic sensor, the system achieves rapid approach to the target flange in the coarse docking stage, and high-precision attitude adjustment and docking in the fine docking stage. In order to verify the performance of the system, we built an experimental platform in a dock environment and conducted multiple experimental tests on the automatic docking system. Experimental results show that, under the influence of sea winds in the dock environment, the docking system achieves a docking accuracy of 3 mm in both rainy and sunny conditions. While the current experiments primarily focus on a static dock environment, we have taken into account external factors like wind and weather conditions. However, due to experimental limitations, dynamic conditions such as waves and tides have not been fully addressed. Future research will focus on exploring the impact of these dynamic conditions, particularly waves and tides, on the system's performance. Additionally, we plan to optimize anti-interference algorithms to improve robustness and introduce predictive control algorithms to better adapt to nonlinear disturbances. In the event of a sensor failure, the system is designed to stop and revert to manual control, ensuring that the docking process can still be safely completed by the operator. This approach provides an additional layer of safety, ensuring the system's reliability even in unforeseen circumstances. For future improvements, we are considering the use of redundant sensors. Specifically, we plan to replace the current set of four ultrasonic probes with multiple probes, enhancing fault tolerance and ensuring more reliable data collection. This modification will improve the system's operational continuity, even if individual sensors fail. While the system currently meets docking accuracy requirements, we recognize the potential to further enhance its robustness. In future work, we aim to implement anti-interference algorithms, such as Kalman filters or neural networks, to improve the system's ability to handle sensor noise and environmental disturbances more effectively. Moreover, we will investigate how the installation position and calibration of ultrasonic sensors affect docking accuracy, particularly with different pipe sizes. Optimizing sensor placement and calibration could further increase the system's precision and reliability under diverse environmental conditions and for pipelines of varying dimensions. Conceptualization, R.X. and W.F. ; methodology, R.X. and S.S.; software, R.X. and S.S.; validation, R.X., W.F. and S.S.; formal Analysis, R.X. ; investigation, R.X. and H.Z. ; resources, W.F. and H.Z. ; data curation, R.X. and H.Z. ; writing--original draft preparation, R.X. ; writing--review and editing, W.F. and S.S.; supervision, W.F. ; project administration, W.F. All authors have read and agreed to the published version of the manuscript. This research was funded by Dinghai District Science and Technology Bureau under the project(2024) Intelligent Low-Temperature Vacuum Fish Powder Drying Equipment Key Technology Research, by the Oil Transfer Arm Automatic Docking Technology Research and Development Project between Shanghai Guan Zhuo Enterprise Development Co., Ltd., and Zhejiang Ocean University. Not applicable. All data used to support the findings of this study are included within the article. The authors gratefully acknowledge the support provided by Shanghai Eminent Enterprise Development Co., Ltd., and Zhejiang Ocean University. The authors declare that this study received funding from Shanghai Guan Zhuo Enterprise Development Co., Ltd.