Many of the tasks that a service robot can perform at home involve navigation skills. In a real world scenario, the navigation system should consider individuals beyond just objects, theses days it is necessary to offer particular and dynamic representation in the scenario in order to enhance the HRI experience. In this paper, we use the proxemic theory to do this representation. The proxemic zones are not static. The culture or the context influences them and, if we have this influence into account, we can increase humans' comfort. Moreover, there are collaborative tasks in which these zones take different shapes to allow the task's best performance. This research develops a layer, the social layer, to represent and distribute the proxemics zones' information in a standard way, through a cost map and using it to perform a social navigate task. We have evaluated these components in a simulated scenario, performing different collaborative and human-robot interaction tasks and reducing the personal area invasion in a 32\%. The material developed during this research can be found in a public repository, as well as instructions to facilitate the reproducibility of the results.

中文翻译：

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为活动感知导航定义自适应邻近区域

服务机器人在家中可以执行的许多任务都涉及导航技能。在现实世界的场景中，导航系统应该考虑的不仅仅是对象，现在有必要在场景中提供特定和动态的表示，以增强 HRI 体验。在本文中，我们使用近位理论来进行这种表示。邻近区域不是静态的。文化或环境会影响他们，如果我们考虑到这种影响，我们可以增加人类的舒适度。此外，还有一些协作任务，其中这些区域采用不同的形状以实现任务的最佳性能。该研究开发了一个层，即社会层，以标准方式表示和分发邻近区域的信息，通过成本地图并使用它来执行社交导航任务。我们在模拟场景中评估了这些组件，执行不同的协作和人机交互任务，并将个人区域入侵减少了 32%。本研究期间开发的材料可以在公共存储库中找到，以及促进结果可重复性的说明。