NNPP：一种用于加速非平坦地形最优路径规划的基于学习的启发式模型
NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain
AI总结：
针对非平坦地形移动机器人路径规划效率问题，提出NNPP启发式模型，通过学习最优路径示例生成像素级路径概率分布以缩小搜索空间，可加速Astar等算法在新地图上的最优路径规划。
摘要：
智能自主路径规划对于提升在行星表面、越野环境等非平坦地形中作业的移动机器人的探索效率至关重要。本文提出NNPP模型用于计算启发式区域，使Astar等基础算法仅在这一缩小的搜索空间内寻找最优路径，有效减少搜索时间。NNPP模型从大量预先标注的最优路径示例中学习起点与终点位置的语义信息以及地图表示，并输出每个像素的概率分布，表征其属于地图上最优路径的可能性。具体而言，本文根据数字高程模型得到的坡度、粗糙度和高程差，计算每个栅格单元的通行成本。随后，采用高斯分布对起点和终点位置进行编码，并分析了不同位置编码参数对模型性能的影响。经过训练，NNPP模型能够加速全新地图上的路径规划。
英文摘要：
Intelligent autonomous path planning is essential for enhancing the exploration efficiency of mobile robots operating in uneven terrains like planetary surfaces and off-road environments.In this paper, we propose the NNPP model for computing the heuristic region, enabling foundation algorithms like Astar to find the optimal path solely within this reduced search space, effectively decreasing the search time. 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 digital elevation model. 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 \textcolor{revision}{accelerate} path planning on novel maps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
