<![CDATA[The use of drones is expanding rapidly, but a major operational challenge is navigation in dynamic and obstacle-filled environments. This research aims to develop a drone pathfinding simulator capable of optimizing flight paths while avoiding moving obstacles in real-time. The method involves modifying the A* algorithm by integrating a local grid mapping-based Dynamic Obstacle Avoidance mechanism. The simulator was built to test route efficiency, computation time, and collision avoidance success rates. The results indicate that the simulator successfully found the shortest path with a 98% success rate in avoiding dynamic obstacles, while maintaining an average computation time of under 50 milliseconds per grid update. The practical implication of this research is providing a reliable and cost-effective simulation tool for testing drone navigation algorithms before physical hardware implementation. In conclusion, integrating dynamic obstacle avoidance into the A* algorithm proved effective in optimizing flight paths, but further research is still needed to test scalability in more complex three-dimensional environments. Furthermore, the validation was extended by comparing the proposed method with several modern path planning algorithms, namely D* Lite, Theta*, RRT*, and Dynamic Window Approach (DWA), in order to evaluate its effectiveness more comprehensively in dynamic environments.]]>
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