Enhanced Hybrid A* Path Planning for Unmanned Surface vessels Using Integrated Kalman Filtering

Based on the underactuated unmanned vessel model, a mobile robot path planning method is proposed that combines a Kalman filter with an enhanced Hybrid A* algorithm to address efficiency and safety concerns. By enhancing the heuristic function in the Hybrid A* algorithm and introducing a distance penalty function, the number of node searches is reduced. Additionally, penalty terms for node advancement and direction change are included in the cost function to ensure the feasibility and safety of the planned path. By obtaining trust values of grid cells and calculating vector magnitudes and vector angles for obstacle grid cells, we construct a safe path within a threshold range. Furthermore, we integrate the advantages of Kalman filtering for prediction updates to achieve dynamic path planning for obstacle avoidance in unmanned boats. Simulation results show that our path planning approach, which integrates the enhanced Hybrid A* algorithm with Kalman filtering, is particularly suitable for underactuated unmanned vessels.