A multi-scale path-planning method for large-scale scenes based on a framed scale-elastic grid map
Autor: | Yuekun Sun, Xiaochong Tong, Yi Lei, Congzhou Guo, Yaxian Lei, Haoshuai Song, Zige An, Jiayi Tang, Yibo Wu |
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Jazyk: | angličtina |
Rok vydání: | 2024 |
Předmět: | |
Zdroj: | International Journal of Digital Earth, Vol 17, Iss 1 (2024) |
Druh dokumentu: | article |
ISSN: | 17538947 1753-8955 1753-8947 |
DOI: | 10.1080/17538947.2024.2383852 |
Popis: | Environment modeling serves as the foundation for path planning in unmanned systems. Single-scale maps have many nodes and impose large memory requirements; tree-based multi-scale grid maps used for representing large-scale urban scenes have limited aggregation ability in the presence of dimensional anisotropy. This study proposes a novel multi-scale map-construction method based on a scale-elastic discrete grid structure. The method provides more flexible node aggregation in grid-based representations, reducing the number of grid and border-grid nodes while maintaining the same modeling accuracy. Furthermore, a novel multi-scale A* path-planning algorithm that modifies the neighborhood-expansion phase of A* is proposed to reduce the number of algorithm search nodes in framed multi-scale maps while ensuring optimal path planning. The experimental results demonstrate that the proposed map-construction method requires 71.5% fewer grids and 10.8% fewer border grids than framed-octree grid maps in three-dimensional scenarios with the same modeling accuracy. Consequently, memory requirements are smaller, making this method more efficient on devices with limited performance. The multi-scale A* algorithm also improves path-planning efficiency by reducing the number of search nodes. The proposed method is suitable for path planning in large-scale and complex urban scenes. |
Databáze: | Directory of Open Access Journals |
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