Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to minimise the disruption caused to humans while moving. This implies predicting how people will move and complying with social conventions. Avoiding disrupting personal spaces, people's paths and interactions are examples of these social conventions. This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms. Along with the model, this paper presents an evolution of the dataset SocNav1 [25] which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using different Graph Neural Network blocks. The model trained achieves close-to-human performance in the dataset. In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. The dataset and the model are available in a public repository (https://github.com/gnns4hri/sngnnv2).

翻译：自动导航是辅助和辅助机器人的关键技能。 机器人要取得成功, 机器人必须最大限度地减少在移动时对人类造成的干扰。 这意味着预测人们如何移动和遵守社会惯例。 避免干扰个人空间、 人们的路径和互动就是这些社会惯例的例子。 本文利用图像神经网络模拟机器人的中断, 以考虑到人类和机器人的移动, 以便模型能够被路径规划算法所使用。 与模型一起, 本文介绍了数据集 SocNav1 [ 25] 的演变情况, 该数据集考虑了机器人和人类的移动, 并更新了假想图转换, 并使用不同的图形神经网络块进行了测试。 所培训的模型在数据集中实现了接近人的性能。 除了其准确性外, 这种方法的主要优势在于其相对于手制模型而言可以考虑的社会因素数量的可缩放性。 数据集和模型可在公共仓库( https://github.com/gnns4hr/sngnnv/2) 。