Navigation Model for a Robot as a Human Group Member to Adapt to Changing Conditions of Personal Space
Navigation Model for a Robot as a Human Group Member to Adapt to Changing Conditions of Personal Space
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机器人作为人类群体成员适应个人空间条件变化的导航模型
DOI：
10.20965/jaciii.2020.p0621
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Masataka Tokumaru
中科院分区：
文献类型：
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作者：
Yotaro Fuse;Masataka Tokumaru
在本文中，我们提出了一种机器人模型，以帮助确定机器人在人机群体中人类个人空间不断变化的条件下的位置。最近，人们做出了一些尝试来开发适合人类社区的个人机器人。确定机器人的位置不仅对于避免与人类发生碰撞很重要，而且对于与人类保持社会可接受的距离也很重要。社区中的人所维持的人际空间取决于特定的背景和情况。因此，机器人需要在确定自己的位置的同时考虑其他人的位置并评估其个人空间的变化。为了解决这个问题，我们提出了一种机器人导航模型，并检查实验参与者是否能够在实验场景中区分机器人的轨迹和人类的轨迹。我们准备了一个场景，群体中的机器人需要在三维空间中保持适当的距离。实验参与者在观看代表场景的记录时提供了他们对机器人动作的印象。结果表明：（1）使用所提出的模型的机器人能够跟随其他组成员；（2）实验参与者不确定机器人的轨迹是否由人类和所提出的模型控制。因此，我们得出的结论是，所提出的模型在机器人群体中生成合适的轨迹。
In the present paper, we propose a robotic model to help determine a robot’s position under the changing conditions of human personal space in a human-robot group. Recently, several attempts have been made to develop personal robots suitable for human communities. Determining a robot’s position is important not only to avoid collisions with humans but also to maintain a socially acceptable distance from them. Interpersonal space maintained by persons in a community depends on the particular context and situations. Therefore, robots need to determine their own positions while considering the positions of other persons and evaluating the changes made in their personal space. To address this problem, we proposed a robot navigation model and examined whether the experiment participants could distinguish the robot’s trajectory from the human’s trajectory in the experimental scenario. We prepared a scenario in which robots in a group needed to keep an appropriate distance in a three-dimensional space. The experiment participants provided their impressions on robot movements while watching the records representing the scenario. The results indicate that (1) a robot using the proposed model is able to follow the other group members and (2) the experiment participants were not sure whether the trajectories of the robots were controlled by humans and by the proposed model. Therefore, we conclude that the proposed model generates suitable trajectories in robot groups.