Abstract
Safe navigation in coastal waters remains a persistent challenge for conventional grid-based path planning methods, which prioritize shortest-distance optimization while neglecting spatial risk distribution and kinematic trackability. This study proposes a local path planning framework that integrates an integral image-based risk field with kinematic constraints for small vessel navigation in high-resolution coastal environments. The proposed method evaluates local obstacle density through an area-based spatial risk model and employs an integral image structure to reduce risk computation complexity from O(W2) to O(1). A 16-direction node expansion strategy incorporating kinematic filtering and cubic B-spline smoothing was applied to improve maneuvering feasibility and trajectory continuity. Simulation results across two topologically distinct coastal environments, Mokpo–Sinan and Myeongnyang Strait, demonstrated that the proposed framework increased the minimum clearance distance from 20 m to 238.32 m and the average clearance distance from 884.2 m to 1038.3 m relative to the conventional A* algorithm, and consistently outperformed a static buffer-based baseline. The 16-direction search reduced the maximum course change angle by up to 39% and the average course change angle by up to 55% relative to the 8-direction configuration across both environments while maintaining practical computational efficiency. Kinematic feasibility was further verified through curvature-based analysis of the final smoothed trajectories, confirming that the minimum turning radius consistently exceeded the vessel’s theoretical minimum turning radius across all tested configurations. The results demonstrate that the proposed framework can generate risk-aware and kinematically feasible navigation routes using coastline-based occupancy information alone, suggesting potential applicability to coastal autonomous navigation in environments with limited bathymetric data.