The Hybrid Path Planning Algorithm Based on the Dynamic Characteristics of Autonomous Underwater Vehicles Integrating A* and PCHIP
Bo Liu, Haichuan Zhang, Muxin Nian, Xiangkun Wang, Ping Zheng, Tengfei Liu, Jingyang Liao, Jianhao Ye · 2025
With advancing technology and interdisciplinary integration, Autonomous Underwater Vehicles (AUVs) are increasingly used in underwater construction, marine resource exploration, and deep-sea monitoring. However, traditional path planning methods struggle in complex, dynamic environments. While the A* algorithm is efficient in pathfinding, it often generates paths with sharp turns, affecting navigation stability and efficiency. This paper proposes an enhanced A* algorithm to address this issue. By adjusting obstacle density and introducing adaptive weighting coefficients, search efficiency is improved. A refined neighborhood search strategy reduces unnecessary node traversal, further accelerating computation. The algorithm also mitigates sharp turns to accommodate AUV steering constraints, enhancing navigability. Additionally, the segmented cubic Hermite interpolating polynomial (PCHIP) is applied to smooth the path, reducing abrupt turns and energy consumption. Experimental results show that the proposed algorithm outperforms the conventional A* algorithm in terms of path length, smoothness, and computational efficiency, demonstrating its feasibility for complex underwater environments. This approach provides a robust solution for optimizing AUV navigation in dynamic, constrained conditions.