A Fast Large-Scale Path Planning Method on Lunar DEM Using Distributed Tile Pyramid Strategy
Zhonghua Hong, Bin Tu, Xiaohua Tong, Haiyan Pan, Ruyan Zhou, Yun Zhang, Yanling Han, Jing Wang, Shuhu Yang, Zhenling Ma · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2022
In lunar exploration missions, path planning for lunar rovers using digital elevation models (DEMs) is currently a hot topic in academic research. However, research on path planning using large-scale DEMs has rarely been discussed, owing to the low time efficiency of existing algorithms. Therefore, in this article, we propose a fast path-planning method using a distributed tile pyramid strategy and an improved A*algorithm. The proposed method consists of three main steps. First, the tile pyramid is generated for the large lunar DEM and stored in Hadoop distributed file system. Second, a distributed path-planning strategy based on tile pyramid (DPPS-TP) is used to accelerate path-planning tasks on large-scale lunar DEMs using Spark and Hadoop. Finally, an improved A*algorithm was proposed to improve the speed of the path-planning task in each tile. The method was tested using lunar DEM images. Experimental results demonstrate that: in a single-machine serial strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed A*algorithm for open list and closed list with random access feature (OC-RA-A*algorithm) is 3.59 times faster than the traditional A*algorithm in long-distance path planning tasks and compared to the distributed parallel computation strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed DPPS-TP based on tile pyramid DEM is 113.66 times faster in the long-range path planning task.