HubotVerse: Towards Internet of Human and Intelligent Robotic Things with a Digital Twin-Based Mixed Reality Framework

Dandan Zhang, Ziniu Wu, Jin Zheng, Yifan Li, Zheng Dong, Jialin Lin

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)
118 Downloads (Pure)

Abstract

Although the Internet of Robotic Things (IoRT) has enhanced the productivity of robotic systems in conjunction with the Internet of Things (IoT), it does not inherently support seamless human–robot collaboration. This article presents HuBotVerse, a unified framework designed to foster the evolution of the Internet of Human and Intelligent Robotic Things (IoHIRT). HuBotVerse is advantageous due to its unique features, including security, user-friendliness, manageability, and its open source nature. Moreover, this framework can seamlessly integrate various human–robot interaction (HRI) interfaces to facilitate collaborative control between humans and robots. Here, we emphasize a digital twin (DT)-based mixed reality (MR) interface, which enhances teleoperation efficiency by offering users an intuitive and immersive way to interact with. To evaluate the effectiveness of HuBotVerse, we conducted user studies based on a pick-and-place task. Feedback was gathered through questionnaires, complemented by a quantitative analysis of key performance metrics, user experience, and the National Aeronautics and Space Administration Task Load Index (NASA-TLX). Results indicate that the fusion of MR and HuBotVerse within a comprehensive framework significantly improves the efficiency and user experience of teleoperation. Moreover, the follow-up questionnaires reflect the advantages of the HuBotVerse framework in terms of evident user-friendliness, manageability, and usability in home-care or health-care applications. For codes, project videos, tutorials, technical details, case studies, and Q&A, please check our website ( https://sites.google.com/view/iohirtplusmr/home ).
Original languageEnglish
Pages (from-to)2-12
Number of pages11
JournalIEEE Robotics and Automation Magazine
Early online date28 Aug 2024
DOIs
Publication statusE-pub ahead of print - 28 Aug 2024

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