Navigation Model for a Robot as a Human Group Member to Adapt to Changing Conditions of Personal Space
Author(s) -
Yotaro Fuse,
Masataka Tokumaru
Publication year - 2020
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2020.p0621
Subject(s) - robot , computer science , trajectory , context (archaeology) , artificial intelligence , human–computer interaction , space (punctuation) , personal robot , position (finance) , personal space , mobile robot , human–robot interaction , simulation , motion (physics) , social robot , computer vision , robot control , psychology , social psychology , geography , physics , archaeology , finance , astronomy , economics , operating system
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.
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