As a classic path-planning algorithm, the dynamic window approach (DWA) is still widely used in the field of path planning, especially in robotic path planning. After many years of iterations, the DWA algorithm has evolved with many innovative versions. Among them, the combination of DWA and A-STAR algorithms is widely used. The A* algorithm, serving as a global path planner, provides global path guidance to DWA to avoid local optima issues. Meanwhile, DWA acts as a local path planner, dynamically responding to obstacles in the environment and providing local paths for robots. This combination is known for its simplicity and efficiency. However, in complex environments, this traditional algorithm cannot timely respond to dynamic changes, especially when navigating around moving obstacles. Therefore, this paper proposes a fuzzy control-based DWA local controller combined with an adaptive A* algorithm to enhance the efficiency and adaptability of robot navigation in dynamic environments. For the A* algorithm, this study introduces an adaptive coefficient to refine the heuristic function, enhancing path-planning efficiency. Concurrently, within the DWA framework, a fuzzy controller is implemented to timely modulate the coefficients of the evaluation functions. While ensuring pathfinding efficiency, it enhances its adaptability in complex environments, thereby improving the robustness of robot path planning. In multiple experiments, this algorithm has enabled robots to safely reach their targets in various dynamic environments, which proves the algorithm's high performance and practicality.