NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain
This addresses path planning efficiency for mobile robots in uneven environments like planetary surfaces, but it is incremental as it builds on existing heuristic methods.
The paper tackles the problem of accelerating optimal path planning for mobile robots on uneven terrain by proposing the NNPP model, which learns to predict a heuristic region from demonstrations, reducing search time for algorithms like A*.
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.