Intelligent autonomous path planning is crucial to improve the exploration
efficiency of planetary rovers. In this paper, we propose a learning-based
method to quickly search for optimal paths in an elevation map, which is called
NNPP. The NNPP model learns semantic information about start and goal
locations, as well as map representations, from numerous pre-annotated optimal
path demonstrations, and produces a probabilistic distribution over each pixel
representing the likelihood of it belonging to an optimal path on the map. More
specifically, the paper computes the traversal cost for each grid cell from the
slope, roughness and elevation difference obtained from the DEM. Subsequently,
the start and goal locations are encoded using a Gaussian distribution and
different location encoding parameters are analyzed for their effect on model
performance. After training, the NNPP model is able to perform path planning on
novel maps. Experiments show that the guidance field generated by the NNPP
model can significantly reduce the search time for optimal paths under the same
hardware conditions, and the advantage of NNPP increases with the scale of the
map.

智能自主路径规划的关键是提高行星探测器的探索效率。本文提出了一种基于学习的方法，用于在高程地图中快速搜索最优路径，称为 NNPP 模型。该模型通过大量预注释的最优路径示范中学习起点和目标位置的语义信息以及地图表示，并生成每个像素的概率分布，表示其属于地图上最优路径的可能性。通过计算从 DEM 中获得的坡度、粗糙度和高度差来计算每个网格单元的遍历成本。随后，使用高斯分布对起点和目标位置进行编码，并分析不同位置编码参数对模型性能的影响。在训练后，NNPP 模型能够在新颖的地图上进行路径规划。实验证明，NNPP 模型生成的导航场能够在相同硬件条件下显著缩短寻找最优路径的时间，并且 NNPP 的优势随着地图规模的增加而增加。