{"title":"Human-Robot Interaction Design Based on Specific Person Finding and Localization of a Mobile Robot","authors":"K. Song, Pei-Chun Lu, Shao-Huan Song","doi":"10.1109/CACS47674.2019.9024734","DOIUrl":null,"url":null,"abstract":"In this paper we propose a novel human-robot interaction system to find a specific person in public for providing service. The system combines indoor localization, face recognition and robot navigation. The indoor localization uses deep neural network (DNN) and particle filtering to estimate the user position. A face recognition module provides the user identification to the robot. The robot first uses localization data to navigate to the vicinity of the user and then uses the face recognition to move to the front of the user to provide service. To verify the effectiveness of the design, we implemented the system to a mobile robot and integrated the application through a smart phone. The integrated experiments demonstrated that a user can call the robot to come to his/her front by using the proposed design. One also can order the robot via a smart phone to find a specific person and interact with him/her.","PeriodicalId":247039,"journal":{"name":"2019 International Automatic Control Conference (CACS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Automatic Control Conference (CACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CACS47674.2019.9024734","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper we propose a novel human-robot interaction system to find a specific person in public for providing service. The system combines indoor localization, face recognition and robot navigation. The indoor localization uses deep neural network (DNN) and particle filtering to estimate the user position. A face recognition module provides the user identification to the robot. The robot first uses localization data to navigate to the vicinity of the user and then uses the face recognition to move to the front of the user to provide service. To verify the effectiveness of the design, we implemented the system to a mobile robot and integrated the application through a smart phone. The integrated experiments demonstrated that a user can call the robot to come to his/her front by using the proposed design. One also can order the robot via a smart phone to find a specific person and interact with him/her.