This study investigates the tracking of underwater cables using autonomous underwater vehicles (AUVs) equipped with side-scan sonar (SSS). AUV motion stability is crucial for effective SSS imaging, which is essential for continuous cable tracking. Traditional methods that derive AUV guidance rates directly from measured cable states often cause unnecessary jitter when imaging, complicating accurate detection. To address this, we propose a non-myopic receding-horizon optimization (RHO) strategy designed to maximize cable imaging quality while considering AUV maneuvering constraints. This strategy identifies the optimal heading decision sequence over a future horizon, ensuring stable and efficient cable tracking. We also employ a long short-term memory (LSTM) network to predict future cable states, further minimizing AUV motion instability during abrupt path changes. Given the computational limitations of AUVs, we have developed an efficient decision-making framework that can execute resource-intensive algorithms in real time. Finally, the robustness and effectiveness of the proposed algorithm were validated through comparative experiments. The results demonstrate that the proposed method outperforms existing methods in key metrics such as cable-tracking accuracy and AUV motion stability. This ensures that the AUV can acquire high-quality acoustic images of the submarine cable in an optimal state, enhancing the continuity and reliability of cable-tracking tasks.

With advancements in Autonomous Underwater Vehicle (AUV) and sensor technologies, the operational paradigms for seabed survey are undergoing significant transformation. Compared to traditional towed or remotely operated platforms, AUV-based seabed survey systems demonstrate superior capabilities in data resolution, operational efficiency and stealth. Furthermore, propelled by progress in artificial intelligence, the technical approaches of AUV-based seabed exploration systems are also experiencing disruptive changes. Based on our observations, existing review articles predominantly focus on individual technologies within seabed survey operations, failing to reflect the systemic constraints and interdependencies among these discrete technological components. This review focuses on the scenario of seabed target detection within seabed survey operations, summarizing research progress aimed at enhancing the effectiveness of such systems across three key technical areas: image processing of side-scan sonar (SSS) systems, intelligent detection of seabed targets and autonomous path planning for survey missions, which is based on a representative system—AUV-mounted SSS system. Given the multi-faceted challenges still present in seabed exploration technology, this paper aims to provide directional guidance for new researchers entering this field.