Deep learning-based intelligent path planning system design for subsea pipeline laying

Subsea pipeline route planning is one of the core challenges in marine engineering. Traditional methods, which rely on manual expertise and static algorithms, struggle to adapt to complex and dynamic seabed terrains, leading to prolonged decision-making cycles and high construction costs. This paper proposes an intelligent path planning system based on deep learning and multi-objective optimization. By leveraging transfer learning to extract common features from historical seabed terrain data and integrating a modified sparrow search algorithm (MSSA) with gradient constraints, the system achieves dynamic multi-objective optimization balancing route length, installation cost, and safety. Experimental results demonstrate that the proposed system significantly outperforms conventional methods in planning efficiency, path quality, and cost control under complex seabed conditions. One of the core innovations of the system is the adoption of a transfer learning-based multi-source heterogeneous terrain feature alignment method. By extracting universal gradient features (such as continental slope distribution patterns) from South China Sea seabed topography data, the system substantially enhances the path planning generalization to new marine areas. Furthermore, a digital twin-based real-time feedback mechanism is established, which dynamically adjusts route planning schemes using sonar and remote sensing data. This enables online re-planning in response to sudden obstacles (e.g., shipwrecks) with the response time less than 4.2 seconds. This study provides an efficient and low-risk solution for intelligent path planning in deep-sea pipeline installation projects.