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.

Underwater target detection via side-scan sonar is vital for defense and economy but hindered by sparse targets, high data costs, and feature extraction difficulties due to textureless acoustic data and limited samples. To overcome these limitations, particularly for few-shot, small-object detection, we propose a Spatial Distribution Probability-Guided Detection Framework to aid Unmanned Underwater Vehicles (UUVs) in precise localization and clustering. The framework features a novel module that leverages a pre-trained Vision Foundation Model (DINOv3) to generate spatial distribution probability maps, guiding a Transformer-based network for accurate detection with scarce data. Additionally, it incorporates a Target Position Calculation Module and a DBSCAN-based post-processing module to determine global geographic coordinates and cluster discrete points, respectively. Experiments were conducted on both a Public Mine Detection Dataset and a self-collected dataset containing simulated mines and buoys. Ablation studies and comparison experiments demonstrated that the proposed guidance mechanism significantly improves detection performance. Furthermore, two comb-search missions verified that the system could accurately locate and cluster targets, distinguishing real targets from false detections (noise). These results confirm the framework’s efficacy in enabling high-precision perception and autonomous operations for complex underwater inspection tasks.

During seafloor terrain mapping missions conducted by AUVs, positioning error accumulation occurs inevitably over long distances due to the unavailability of global satellite navigation signals underwater. Moreover, the alternating distribution of flat and undulating regions on the seafloor renders single-constraint-based bathymetric SLAM methods prone to performance degradation in complex environments. To address these challenges, this paper proposes a multi-constraint hybrid strategy SLAM framework for AUV-based seafloor terrain mapping, grounded in an analysis of error accumulation mechanisms and constraint failure characteristics. The framework establishes a hierarchical and progressive constraint architecture to enable collaborative optimization across different spatial scales and topographic conditions. At the foundational pose estimation stage, multi-source trajectory information is fused to ensure continuity and stability in pose computation. In the local consistency constraint stage, an improved point cloud registration method combined with a neighborhood survey-line constraint mechanism is introduced to enhance geometric consistency among survey lines in feature-sparse regions. At the global optimization stage, a loop closure detection strategy is designed based on topographic statistical features, incorporating adaptive thresholds and correlation metrics to achieve robust introduction of global constraints. By flexibly integrating direct registration and feature-matching strategies according to topographic characteristics, the framework fully leverages the advantages of multi-constraint cooperative optimization. The proposed method is validated by the field data. Experimental results on real lake-trial data show that, relative to the baseline configurations evaluated under identical noise-injection conditions, the MCHS-SLAM framework yields more concentrated consistency-error distributions with markedly shorter large-error tails, and exhibits improved error suppression relative to the reference trajectory. This work presents a methodological framework for high-quality seafloor terrain mapping under heterogeneous terrain conditions, providing a basis for future extensions toward onboard real-time deployment.