Demo: TINGLE: Pushing Edge Intelligence in Synchronization and Useful Data Transfer for Human-Robotic Arm Interactions

被引：10

作者：

Gu, Xinjie ^{[1
]}

Wang, Xiaolong ^{[1
]}

Feng, Yuchen ^{[1
]}

Long, Yuzhu ^{[1
]}

Mukherjee, Mithun ^{[1
]}

Pan, Zhigeng ^{[1
]}

Guo, Mian ^{[2
]}

Zhangz, Qi ^{[3
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Sch Artificial Intelligence, Nanjing, Peoples R China

[2] Guangdong Polytech Normal Univ, Sch Elect & Informat, Guangzhou, Peoples R China

[3] Aarhus Univ, Dept Elect & Comp Engn, DIGIT, Aarhus, Denmark

来源：

IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) | 2022年

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1109/INFOCOMWKSHPS54753.2022.9798353

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This demo presents a lightweight framework for the remote operation of human-robot interactions. As always, proper synchronization between the human (master) and the robot (controlled) is a critical issue during manipulation. In this experiment, we present an end-to-end synchronous system to establish near real-time maneuvering. Moreover, by leveraging the devices' limited yet available computational capabilities in master and controlled domains, we aim to apply edge intelligence to determine the amount of data required for mimicking the human's hand movement before wireless transmission to the controlled domain. We observe from extensive experiment results that our proposed TINGLE demonstrates a noticeable performance with fewer missing movements in the controlled domain than baselines.

引用

页数：2

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