论文标题
使用图神经网络生成人类意识的导航图
Generation of Human-aware Navigation Maps using Graph Neural Networks
论文作者
论文摘要
在社交情况下导航时机器人引起的不适感至关重要，这对于他们被接受至关重要。本文提出了一个基于机器学习的框架，该框架可以引导现有的一维数据集，以生成成本映射数据集，并结合图形神经网络和卷积神经网络层的模型，以实时生成成本图，以实时实时导航。针对原始的一维数据集和模拟导航任务评估了所提出的框架。考虑到数据集和所使用的导航指标的准确性，结果优于类似的最新方法。提议的框架的应用不仅限于人类意识的导航，还可以应用于需要生成地图的其他领域。
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.
