Reading Digital Map Files, Adding Map Obstacles, and Solving 3D Path Planning Using A* Algorithm (A-Star)

Path planning in three-dimensional space presents a challenging task, particularly when obstacles are present. This article demonstrates how to solve this problem using the A* algorithm.

The first step involves reading environmental data from digital map files. These files typically store 3D grid information in specific formats, including passability status for each coordinate point. In code implementation, this can be achieved using file reading functions (like fread or specialized map parsers) to load grid data into a 3D matrix structure. After loading, obstacles can be dynamically added to the existing map by modifying corresponding grid point properties (e.g., setting obstacle flags to 1) to simulate impassable areas in real environments.

The core advantage of the A* algorithm lies in combining Dijkstra's algorithm's completeness with heuristic search efficiency. When implementing in 3D environments, several key issues must be addressed: Definition of 3D neighborhood nodes -需要考虑上下左右前后六个基本方向，以及对角线方向共26个相邻节点 Design of heuristic function - commonly using 3D Euclidean distance or Manhattan distance for heuristic evaluation Cost calculation - setting different movement costs based on terrain elevation differences, which can be implemented through a cost function that considers slope gradients

Implementation requires handling optimization aspects such as using priority queues (often implemented with min-heaps) to store nodes to be checked, and establishing effective node visitation recording mechanisms (like closed lists using hash tables) to avoid redundant calculations. For large-scale 3D maps, optimization strategies like hierarchical path planning can be considered, where high-level planning uses coarse grids while detailed planning uses finer resolutions.

By appropriately setting heuristic weights and cost functions, the A* algorithm can quickly find optimal or near-optimal obstacle-avoiding paths in 3D space. This technology has broad application prospects in areas such as UAV route planning and robot navigation, where the implementation typically involves defining node expansion rules, heuristic functions, and path reconstruction algorithms.