Path planning of unmanned ships based on A* and dynamic window approach

The coastline raid task requires an unmanned surface ship to carry out precise, fixed-point raids in a complex coastal environment. The terrain of the coastal area is highly varied, with static obstacles such as reefs and shoals, as well as moving obstacles like floating objects at sea. Moreover, the task must be completed within a strict time frame. Therefore, real-time, safe, and accurate path planning for the unmanned surface ship is crucial. To address the challenge of balancing global optimization, efficiency, and safety in path planning, a method that integrates global search and local optimization was proposed, based on the A* algorithm and the dynamic window approach (DWA). The A* algorithm computed the global shortest path in a static environment, while the improved DWA optimized local path obstacle avoidance between path nodes. The A* algorithm was enhanced by incorporating a heuristic function with dynamic exponential decay weighting, which reduced the running time and exploration space for global path planning. Additionally, the DWA was improved by introducing an evaluation function based on the angle between the heading of dynamic obstacles and the predicted trajectory of the unmanned surface ship, as well as a distance evaluation function based on a logistic curve, which enhanced the real-time responsiveness of local obstacle avoidance. Simulation results demonstrate that the proposed hybrid algorithm can search for a near-optimal global solution in real-time, effectively handle both static and dynamic obstacles, and significantly improve the task completion efficiency and stability of the unmanned surface ship in complex coastal environments.