A Study of Heuristic-based Path Planning in Complex Maze Using the A* Algorithm with Cost Optimization and Visualization

Route planning is crucial in areas such as robotics, autonomous driving, and drone navigation. Especially in earthquake and other disaster scenarios, route planning in emergency rescue is more difficult. In this paper, A route planning method combining the A star algorithm and multi-cost optimization is proposed to consider road deterioration, risk areas, and different crossing costs in the complex post-earthquake environment. By simulating a 20×20 post-disaster maze, the crossing costs are randomly assigned. Algorithm A is used to determine the cheapest path from the starting point to the destination point. The results show that the optimized A* algorithm has excellent performance in terms of path length, search efficiency, and computational overload, especially in a complex disaster environment. Dynamic visualization technology shows the process of finding nodes and the final path, which helps to improve the decision-making efficiency of emergency response and provides strong technical support for disaster relief.