Abstract

Autonomous underwater vehicles (AUVs) are increasingly used in seabed inspection, underwater search, ocean observation, and infrastructure maintenance. However, feasible and safe trajectory planning in dense three-dimensional underwater environments remains challenging because AUVs must avoid multiple irregular static obstacles, react to moving obstacles, and maintain robust navigation performance under ocean-current disturbances. Traditional path planning methods can suffer from high computational cost or poor trajectory smoothness in dense 3-D environments. The interfered fluid dynamical system (IFDS) provides a promising flow-field-based planning mechanism by treating obstacles as disturbance sources in a virtual fluid field. Nevertheless, the performance of IFDS strongly depends on the repulsive and tangential parameters, which are usually selected empirically and may not provide an optimal trade-off among path length, smoothness, safety margin, and energy consumption. To address these issues, this paper proposes a grey wolf optimization-enhanced IFDS method, termed GWO-IFDS. First, static and dynamic underwater obstacles are modeled using unified super ellipsoid implicit functions, allowing spheres, cylinders, ellipsoids, reefs, and seabed mounds to be described in a common mathematical form. Second, a 3-D IFDS planner is developed to generate collision-free streamlines by combining the attractive flow toward the target and obstacle-induced modulation matrices. Third, a grey wolf optimization algorithm is introduced to optimize the IFDS repulsive and tangential parameters by minimizing a scalarized multi-criteria fitness function that considers path length, terminal error, trajectory smoothness, energy proxy, and minimum obstacle clearance. Finally, simulation studies are conducted under four representative scenarios: multiple static obstacles, mixed static and dynamic obstacles, mixed obstacles with ocean-current disturbances, and parameter-optimization comparison among GWO, PSO, DE, BO, GA, and fixed-parameter IFDS. The results demonstrate that the proposed GWO-IFDS method can generate smoother and safer trajectories with lower steering-effort-related cost than fixed-parameter IFDS. Additional tests under sonar-like perception uncertainty, bounded steering constraints, and different weight settings further verify the robustness and feasibility of the proposed framework.

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