Convolutional Neural Network-Based Approximation of Coverage Path Planning Results for Parking Lots

Parking lots have wide variety of shapes because surrounding environment and the objects inside parking lot, such as trees, manholes, etc. In case paving much area possible should be covered by construction vehicle to reduce need for manual workforce. Thus, coverage path planning (CPP) problem is formulated. The CPP a complex with constraints regarding various issues, dimensions data processing time resources. A strategy based on convolutional neural networks (CNNs) fast estimation CPP’s average track length, standard deviation lengths, number tracks was suggested in this article. Two datasets different complexity were generated analyze approach. first represented simple working polygon constructed out several rectangles applied shear rotation transformations. second geometry ellipses, narrow area, obstacles. results compared linear regression models, an input. For both datasets, use approximator estimate outcomes led more accurate respective models. approach enables us rough estimates large geometries short period organize process, example, price, choosing best decomposition polygon,