Global Path Planning for UUVs in Nearshore Environments Using an IAPF-RRT* Method

Nearshore environments, characterized by complex obstacle distributions and dynamic disturbances, pose significant challenges to global path planning for unmanned underwater vehicles (UUVs). To address these challenges, this paper proposes an improved artificial potential field-guided RRT* (IAPF-RRT*) method for efficient and robust path planning in coastal environments. The proposed approach integrates an improved artificial potential field into the sampling and node expansion processes to enhance goal-directed exploration and obstacle avoidance capability. In addition, a target-biased sampling strategy and an adaptive attraction mechanism are introduced to accelerate convergence, while a NURBS-based refinement scheme is employed to improve trajectory continuity and smoothness. Extensive simulations in representative scenarios demonstrate that the proposed method significantly improves planning efficiency, reducing planning time by up to 80% compared with conventional RRT-based methods, while substantially decreasing redundant node expansion and improving trajectory smoothness. A consistently high success rate is maintained across all scenarios. Field experiments in nearshore environments further validate the robustness and practical applicability of the proposed method. These results indicate that the proposed IAPF-RRT* method achieves a favorable balance between efficiency, robustness, and path quality, making it well-suited for real-world UUV operations in complex nearshore environments.