Autonomous underwater vehicles (AUVs) are seeing increasingly widespread adoption in marine exploration, search and rescue, and military applications. As a core enabling technology, underwater path planning faces significant challenges, in this article an intelligent path planning algorithm named A*-MPPO-DWA is proposed to enhance the efficiency and accuracy of path planning for AUV in complex dynamic environments. The proposed hierarchical framework operates as follows: firstly, the A* algorithm performs global path search and preliminary planning to ensure a feasible route from the start to the goal point. Secondly, the MPPO (multiphase path optimization) strategy refines the path through multiphase decision making. Different from conventional path smoothing and single-stage optimization methods, MPPO integrates dynamic obstacle detection and ocean current compensation into a three-stage progressive optimization pipeline, which realizes global topology preservation, redundant node elimination, and adaptive smooth correction simultaneously, rather than simple geometric smoothing. It can effectively handle complex dynamic environment. Finally, the DWA (Dynamic Window Approach) algorithm is employed for local path smoothing and real-time obstacle avoidance by integrating adaptive velocity control, enabling the AUV to avoid collisions and excessive steering during mission execution. Experimental results demonstrate that the proposed algorithm achieves superior stability and accuracy. Against baseline approaches using A* or DWA, the A*-MPPO-DWA algorithm shows significant advantages in key metrics, including path length, number of path turns, obstacle avoidance success rate, and computational time.
Chen et al. (Fri,) studied this question.