In this study, we aim to achieve path-planning for firefighter robots in large petrochemical complexes. In large environments, path-planning (e.g., Hybrid A∗) requires a large computation memory and a long execution time. These constrains are not feasible for firefighter robots. In order to overcome these two challenges, we propose a two-stage hybrid A∗ path-planning. For the first stage we use a global pathplanner that makes a path using a low-resolution grid map of 2 m. The global path-planner generates a path for an area of approx. 500 m×1000 m in 10 seconds . In the second stage, we refine the path by using a local-planner that uses a local-map of 100 m×100 m size around the robot with a high resolution grid of 1 m. The local planner receives its sub-goal from the global planner and recalculates a local path at a high speed of a few hundred milliseconds. Therefore, the local-planner can react to changes of the map due to obstacles in real-Time. We evaluated our proposed method by comparing with conventional hybrid A∗ in simulated as well as real experimental data of petrochemical complexes. By employing the local-planner our method could drastically reduce the used memory and execution time for the re-planning. For a trajectory of 600 m, our method reduces the execution time by 99:2% for real data and by 94:34% for simulated data. The memory usage was likewise drastically reduced by 97:45% for real data and by 97:91% for simulated data.