A CNN-Based Fast Generalized Voronoi Diagrams Framework for Path Planning

Generalized Voronoi Diagrams (GVD) are extensively utilized in path planning, particularly in algorithms based on RRT*, where they guide the sampling process and thereby enhance the search efficiency of RRT*. Traditional methods for constructing GVD, however, often demand substantial computational resources, especially in complex and large-scale environments. In this article, we propose an efficient GVD generation algorithm leveraging Convolutional Neural Networks (CNN). By harnessing the learning capabilities of CNN, the model can swiftly predict the corresponding GVD probability map from an input image containing obstacle information. The training dataset is derived from traditional GVD computation methods, with GVD pruning applied to complex environmental scenarios. The training outcomes indicate that for simple environments, the CNN-based GVD generation aligns closely with the results obtained via traditional methods. In complex environments, the model is also capable of accurately predicting smooth and efficient GVD, consistent with manually pruned GVD results. Simulation results demonstrate that the model's predictions can be seamlessly integrated into the RRT* algorithm, providing effective sampling heuristics. This integration leads to a significant improvement in GVD computation speed compared to traditional methods and exhibits robust generalization capabilities in complex environments.