Minimising the discomfort caused by robots when navigating in social situations is crucial for them to be accepted. The paper presents a machine learning-based framework that bootstraps existing one-dimensional datasets to generate a cost map dataset and a model combining Graph Neural Network and Convolutional Neural Network layers to produce cost maps for human-aware navigation in real-time. The proposed framework is evaluated against the original one-dimensional dataset and in simulated navigation tasks. The results outperform similar state-of-the-art-methods considering the accuracy on the dataset and the navigation metrics used. The applications of the proposed framework are not limited to human-aware navigation, it could be applied to other fields where map generation is needed.
@article{arxiv.2011.05180,
title = {Generation of Human-aware Navigation Maps using Graph Neural Networks},
author = {Daniel Rodriguez-Criado and Pilar Bachiller and Luis J. Manso},
journal= {arXiv preprint arXiv:2011.05180},
year = {2020}
}