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Intelligent autonomous path planning is essential for enhancing theexploration efficiency of mobile robots operating in uneven terrains likeplanetary surfaces and off-road environments.In this paper, we propose the NNPPmodel for computing the heuristic region, enabling foundation algorithms likeAstar to find the optimal path solely within this reduced search space,effectively decreasing the search time. The NNPP model learns semanticinformation about start and goal locations, as well as map representations,from numerous pre-annotated optimal path demonstrations, and produces aprobabilistic distribution over each pixel representing the likelihood of itbelonging to an optimal path on the map. More specifically, the paper computesthe traversal cost for each grid cell from the slope, roughness and elevationdifference obtained from the digital elevation model. Subsequently, the startand goal locations are encoded using a Gaussian distribution and differentlocation encoding parameters are analyzed for their effect on modelperformance. After training, the NNPP model is able to\textcolor{revision}{accelerate} path planning on novel maps.

NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain