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This paper introduces an A*-Fuzzy hybrid path planning algorithm for Unmanned Surface Vehicles (USVs) that integrates fuzzy collision-avoidance reasoning. The approach uses preprocessed satellite imagery to create a grid-based navigable map, then employs A* for initial path search, followed by a Takagi-Sugeno (T-S) fuzzy inference model to refine node selection near obstacles. Simulation results demonstrate improved redundancy and safety in USV navigation, high computational efficiency, and reduced data storage requirements.
USVs can now navigate more safely and efficiently thanks to a novel A*-Fuzzy path planning algorithm that intelligently avoids obstacles using fuzzy logic.
: This paper presents a global path planning algorithm for unmanned surface vehicle (USV) based on an A* – fuzzy hybrid approach that integrates fuzzy collision-avoidance reasoning. To address path planning challenges in real-world scenarios, the proposed method first processes actual satellite imagery through image preprocessing and binarization to generate a grid-based navigable map. The grid map is then scaled to physical dimensions, and obstacle boundaries are dilated according to the USV minimum safety radius to ensure navigational clearance. Subsequently, the A* algorithm is employed for initial path search, while a Takagi-Sugeno (T-S) fuzzy inference model is applied to refine node selection near obstacles, enhancing local decision-making under uncertainty. Finally, the generated trajectory is simplified by retaining only critical waypoints, significantly reducing data storage requirements without compromising path quality. Simulation results demonstrate that the proposed algorithm improves both redundancy and safety in USV navigation, maintains high computational efficiency, and offers a practical solution for autonomous maritime decision-making. The approach effectively balances path optimality, obstacle avoidance capability, and memory efficiency, providing valuable support for safe USV operations.
