Application of AUV Cluster Control in Deep-Sea Pipeline Inspection

Abstract

With the rapid development of offshore oil and gas resources exploitation, deep-sea pipelines, as the core of marine energy transportation, face severe challenges in regular inspection and maintenance. Autonomous Underwater Vehicle (AUV) cluster technology has become a promising solution to address the limitations of single AUV in efficiency, coverage, and reliability during deep-sea operations. This paper focuses on the application of AUV cluster control in deep-sea pipeline inspection. First, it analyzes the technical difficulties of deep-sea pipeline inspection and the advantages of AUV cluster systems. Then, it proposes a leader-follower formation control strategy based on backstepping neural network to solve the problems of underactuation, nonlinear dynamics, and communication constraints of AUVs in marine environments. The kinematic and dynamic models of AUV clusters are established, and the stability of the control system is verified through theoretical derivation. Finally, simulation experiments and sea trial data are presented to demonstrate the effectiveness of the proposed control strategy in improving inspection efficiency and data accuracy. The results show that the AUV cluster system can complete pipeline inspection tasks with an average error of less than 5% under complex deep-sea conditions, which significantly outperforms the single AUV operation mode. This research provides a reliable technical support for the intelligent and efficient inspection of deep-sea pipelines.