Generation of Human-aware Navigation Maps using Graph Neural Networks

2020/11/10 by Rodriguez-Criado, Daniel, Bachiller, Pilar, Manso, Luis J.
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2011.05180

Abstract

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.

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