论文标题
定义自适应邻近区域用于活动感知导航
Defining Adaptive Proxemic Zones for Activity-aware Navigation
论文作者
论文摘要
服务机器人可以在家执行的许多任务都涉及导航技能。在现实世界的情况下,导航系统应考虑个人以外的个人,这是有必要在情况下提供特定而动态的表示,以增强HRI体验。在本文中,我们使用邻近理论来执行此表示。邻近区域不是静态的。文化或环境会影响它们,如果我们考虑到这种影响,我们可以增加人类的舒适感。此外,在某些协作任务中,这些区域采用不同的形状,以允许任务的最佳性能。这项研究开发了一个层,即社交层,以通过成本映射以标准方式代表和分发亲近区域的信息,并使用它来执行社交导航任务。我们已经在模拟场景中评估了这些组件,执行了不同的协作和人类机器人交互任务,并减少了32 \%的个人面积入侵。在这项研究中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。
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.