Efficient Grid-Based Parking Navigation Using Heuristic Path Planning

In modern urban environments, the efficient utilization of parking spaces has become a critical challenge due to rapid vehicle growth and limited infrastructure. This paper presents an intelligent path planning approach for grid-based parking lots using the A* (A-star) algorithm to determine the optimal path between a designated source and destination point. The parking lot is modelled as a two-dimensional grid where certain blocks represent obstacles corresponding to occupied or restricted spaces. The proposed system, implemented in Python, dynamically computes the shortest feasible path while avoiding obstacles, ensuring minimal traversal cost and time. The A* algorithm integrates heuristic estimation with actual movement cost to efficiently explore possible routes and identify the most cost-effective path. Simulation results demonstrate accurate and reliable path generation in complex grid configurations, validating the algorithm’s capability to enhance autonomous navigation and smart parking management. This research contributes toward the development of intelligent parking systems that can be extended to autonomous vehicles and real-time traffic control applications.