In outdoor parking lots, due to the uncertainty and dynamic interaction of vehicle spatiotemporal distribution and the limitations of real-time computing resources, the real-time performance and efficiency of omnidirectional mobile robots in dynamic parking lot path planning need improvement. Current path planning algorithms struggle to meet the demands of such real-time dynamic environments. This paper proposes an improved A*-DWA algorithm incorporating fuzzy control, which combines fuzzy global planning driven by environmental obstacle factors with local dynamic trajectory optimization using the dynamic window approach to address the path planning challenges of omnidirectional mobile robots in real-time dynamic environments. First, an expanded 20-neighborhood search strategy is adopted to optimize global path generation. Second, a fuzzy adaptive evaluation mechanism based on multi-parameter weighting is designed to enhance the algorithm′s adaptability to dynamic environments. Then, cubic spline interpolation is employed to achieve path smoothing. Finally, DWA is integrated for local dynamic obstacle avoidance and trajectory optimization. By constructing a simulated outdoor parking lot environment, the results demonstrate that compared to the algorithms proposed in the literature, the FCA*-DWA algorithm improves path length, search efficiency, and node optimization by 13.30%, 21.16% and 45.45%, respectively, providing methodological guidance for autonomous navigation of mobile robots in complex dynamic scenarios.