This work explores an autonomous navigation strategy for mobile robots that combines the coordinated integration of two complementary approaches: the A* algorithm, renowned for its efficiency in global path planning, and the Dynamic Window Approach (DWA), which is well-suited for reactive local control. The aim is to ensure robust navigation in semi-structured environments by combining long-term planning with real-time adaptability. The performance of the method was evaluated in a simulated environment with fixed obstacles, through two representative scenarios. The first scenario, characterized by a high risk of blockage (dead ends, narrow passages), revealed the limitations of using DWA alone, which often becomes trapped in complex configurations. In contrast, the combined approach leverages the predictive capabilities of A* to effectively bypass problematic areas. The second, less constrained scenario aimed to compare trajectory quality under favorable conditions. Results demonstrate that the proposed integration not only enables the robot to reach the goal but also improves overall performance, as evidenced by a 1.76% reduction in average distance traveled and a 1.89% decrease in navigation time compared to DWA alone. The presented approach stands out due to its ability to dynamically adjust the robot's behavior while maintaining a global view of the environment. This contributes to increased reliability and efficiency of autonomous navigation, especially in complex or cluttered contexts.