Socially-aware navigation planner using models of human-human interaction
Socially-aware navigation planner using models of human-human interaction
复制标题
使用人与人交互模型的具有社交意识的导航规划器
DOI：
10.1109/roman.2017.8172334
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
David Feil
中科院分区：
文献类型：
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作者：
Meera Sebastian;S. Banisetty;David Feil
在本文中，我们重新审视了一个实时的社会感知导航规划器，它可以帮助移动的机器人以社会可接受的方式与人类一起导航。这个导航规划器是一个修改的导航核心包的机器人操作系统（ROS），基于早期的工作和进一步修改，只使用自我中心的传感器。该规划器可以用来提供安全以及社会适当的机器人导航。原始特征包括机器人与交互伙伴之间的人际距离和环境特征（例如实时检测的走廊），用于推理交互的当前状态。高斯混合模型（GMM）在这些特征上进行训练，这些特征来自各种交互场景的人与人交互演示。该模型既用于区分与其导航行为相关的不同人类行为，又用于帮助轨迹选择过程，为潜在轨迹提供社交适当性得分。本文提出了一种评估模拟，同时利用数据从真实的人的互动。
In this paper, we revisit a real-time socially-aware navigation planner which helps a mobile robot to navigate alongside humans in a socially acceptable manner. This navigation planner is a modification of nav core package of Robot Operating System (ROS), based upon earlier work and further modified to use only egocentric sensors. The planner can be utilized to provide safe as well as socially appropriate robot navigation. Primitive features including interpersonal distance between the robot and an interaction partner and features of the environment (such as hallways detected in real-time) are used to reason about the current state of an interaction. Gaussian Mixture Models (GMM) are trained over these features from human-human interaction demonstrations of various interaction scenarios. This model is both used to discriminate different human actions related to their navigation behavior and to help in the trajectory selection process to provide a social-appropriateness score for a potential trajectory. This paper presents an evaluation done in simulation while utilizing data from real human interactions.