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The A* algorithm is widely used in path planning for Automated Guided Vehicles (AGVs), but it lacks real-time obstacle avoidance capability. To address this limitation, this paper proposes a hybrid algorithm that integrates an improved A* with the Dynamic Window Approach (DWA). First, a global key-point extraction strategy is introduced: redundant and turning points are removed using Bresenham’s line algorithm to enhance path smoothness and continuity, while child nodes of the current position are redefined to improve search efficiency. Second, to strengthen obstacle avoidance in complex environments, turning points in the global path are designated as sub-targets for DWA, enabling path segmentation and local dynamic planning. This integration allows the system to react in real time to dynamic obstacles. Simulation results demonstrate that, compared to the traditional A* algorithm, the proposed method reduces planning time by 24.19%, the number of inflection points by 40.00%, and path length by 1.49%. In environments with random obstacles, the fused approach generates smoother trajectories and significantly enhances local obstacle avoidance and overall path-planning safety.