Path Planning Techniques for Underwater Robots Based on Obstacles in Subsea Oil Pipelines

With the continuous growth of global energy demand, the safety and maintenance of underwater oil pipelines have become a focal point of international concern. Autonomous Underwater Vehicles (AUVs) are increasingly pivotal in inspection, maintenance, and repair of oil pipelines. This paper explores path planning techniques for underwater robots based on obstacles in subsea oil pipelines to enhance operational efficiency and safety. Employing theoretical analysis and simulation, the study systematically addresses key issues in path planning, including environmental modeling, obstacle recognition, path generation, and optimization. By constructing complex underwater environment models and integrating advanced algorithms such as improved ant colony optimization, genetic algorithms, and particle swarm optimization, the research investigates path planning strategies for AUVs under various obstacle distributions. The results demonstrate that the proposed path planning techniques effectively handle obstacles in subsea oil pipelines, significantly improving planning efficiency and robustness. Additionally, the feasibility of multi-robot collaborative path planning is discussed, providing theoretical support for future advancements in underwater robotics. Overall, this study offers new technological insights for the maintenance of underwater oil pipelines and is crucial for enhancing the intelligence level of underwater operations.