用八叉树优化3D路径规划，节省内存还更快。
Ground-Aware Octree-A* Hybrid Path Planning for Memory-Efficient 3D Navigation of Ground Vehicles
- 结合八叉树与A*算法，动态利用可通行障碍物
- 压缩空旷区域节点，探索节点数减少40%以上
- 适合实时导航的地面机器人，尤其看重内存效率
本文提出一种融合八叉树结构与A*算法的3D路径规划方法。无人地面车辆（UGVs）和足式机器人已在多种地形中实现运动能力。随着移动性提升，障碍物不再仅被视为需避开的阻碍，也可作为辅助行进的路径资源。通过在代价函数中引入高度相关惩罚项，改进后的3D A*算法能有效利用可通行障碍物，同时避开不可逾越的障碍，生成更高效、更真实的路径。八叉树构建的3D网格地图通过将高分辨率节点合并为更大块，在无障碍或稀疏区域实现数据压缩，显著减少A*算法需探索的节点数量，从而提升计算效率并降低内存占用，支持实际环境中的实时路径规划。基准测试表明，该方法在保证路径最优的同时，大幅降低内存使用量与计算时间。
原文摘要 · Abstract (English)
In this paper, we propose a 3D path planning method that integrates the A* algorithm with the octree structure. Unmanned Ground Vehicles (UGVs) and legged robots have been extensively studied, enabling locomotion across a variety of terrains. Advances in mobility have enabled obstacles to be regarded not only as hindrances to be avoided, but also as navigational aids when beneficial. A modified 3D A* algorithm generates an optimal path by leveraging obstacles during the planning process. By incorporating a height-based penalty into the cost function, the algorithm enables the use of traversable obstacles to aid locomotion while avoiding those that are impassable, resulting in more efficient and realistic path generation. The octree-based 3D grid map achieves compression by merging high-resolution nodes into larger blocks, especially in obstacle-free or sparsely populated areas. This reduces the number of nodes explored by the A* algorithm, thereby improving computational efficiency and memory usage, and supporting real-time path planning in practical environments. Benchmark results demonstrate that the use of octree structure ensures an optimal path while significantly reducing memory usage and computation time.
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