Cloud-based learning for robot control

Shane Harrigan; Sonya Coleman; Dermot Kerr; JP Quinn; Leeanne Lindsay; Kyle Madden

doi:10.1109/ICIT58465.2023.10143103

Cloud-based learning for robot control

Shane Harrigan, Sonya Coleman, Dermot Kerr, JP Quinn, Leeanne Lindsay, Kyle Madden

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Abstract

This paper introduces the Virtual Manufacturing Platform (VMP), a cloud-complete educational platform targeting manufacturing industries providing tutorials and sandboxing. The scope of this paper focuses on the robot control architecture of the platform which allows for seamless control of both virtual and physical robots and end-effectors accompanied by supportive educational materials. The platform seeks to derisk automation investments for small to medium enterprises by offering confirmatory environments and knowledge transfer opportunities.

Original language	English
Title of host publication	Proceedings of 2023 IEEE International Conference on Industrial Technology (ICIT)
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	979-8-3503-3650-4
ISBN (Print)	979-8-3503-3651-1
DOIs	https://doi.org/10.1109/ICIT58465.2023.10143103
Publication status	Published online - 9 Jun 2023
Event	International Conference on Industrial Technology - Florida, Orlando, United States Duration: 4 Apr 2023 → 6 Apr 2023 http://icit2023.ieee-ies.org/

Publication series

Name
ISSN (Print)	2641-0184
ISSN (Electronic)	2643-2978

Conference

Conference	International Conference on Industrial Technology
Abbreviated title	ICIT
Country/Territory	United States
City	Orlando
Period	4/04/23 → 6/04/23
Internet address	http://icit2023.ieee-ies.org/

Bibliographical note

Funding Information:
The authors would like to acknowledge the support of Innovate UK. This work is supported by the Smart Manufacturing Data Hub project (contract no. 10017032) – www.smdh.uk.

Publisher Copyright:
© 2023 IEEE.

Keywords

Robotics
Automation
Control
Manufacturing industries
Cloud computing
Service robots
Robot controls
tutorials
End effectors
automation
robotics
control

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICIT58465.2023.10143103

ICIT23-000067 - Cloud-based Learning for Robot ControlFinal published version, 2.21 MBLicence: Unspecified

Cite this

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title = "Cloud-based learning for robot control",

abstract = "This paper introduces the Virtual Manufacturing Platform (VMP), a cloud-complete educational platform targeting manufacturing industries providing tutorials and sandboxing. The scope of this paper focuses on the robot control architecture of the platform which allows for seamless control of both virtual and physical robots and end-effectors accompanied by supportive educational materials. The platform seeks to derisk automation investments for small to medium enterprises by offering confirmatory environments and knowledge transfer opportunities.",

keywords = "Robotics, Automation, Control, Manufacturing industries, Cloud computing, Service robots, Robot controls, tutorials, End effectors, automation, robotics, control",

author = "Shane Harrigan and Sonya Coleman and Dermot Kerr and JP Quinn and Leeanne Lindsay and Kyle Madden",

note = "Funding Information: The authors would like to acknowledge the support of Innovate UK. This work is supported by the Smart Manufacturing Data Hub project (contract no. 10017032) – www.smdh.uk. Publisher Copyright: {\textcopyright} 2023 IEEE.; International Conference on Industrial Technology , ICIT ; Conference date: 04-04-2023 Through 06-04-2023",

year = "2023",

month = jun,

day = "9",

doi = "10.1109/ICIT58465.2023.10143103",

language = "English",

isbn = "979-8-3503-3651-1",

publisher = "IEEE",

booktitle = "Proceedings of 2023 IEEE International Conference on Industrial Technology (ICIT)",

address = "United States",

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TY - GEN

T1 - Cloud-based learning for robot control

AU - Harrigan, Shane

AU - Coleman, Sonya

AU - Kerr, Dermot

AU - Quinn, JP

AU - Lindsay, Leeanne

AU - Madden, Kyle

N1 - Funding Information: The authors would like to acknowledge the support of Innovate UK. This work is supported by the Smart Manufacturing Data Hub project (contract no. 10017032) – www.smdh.uk. Publisher Copyright: © 2023 IEEE.

PY - 2023/6/9

Y1 - 2023/6/9

N2 - This paper introduces the Virtual Manufacturing Platform (VMP), a cloud-complete educational platform targeting manufacturing industries providing tutorials and sandboxing. The scope of this paper focuses on the robot control architecture of the platform which allows for seamless control of both virtual and physical robots and end-effectors accompanied by supportive educational materials. The platform seeks to derisk automation investments for small to medium enterprises by offering confirmatory environments and knowledge transfer opportunities.

AB - This paper introduces the Virtual Manufacturing Platform (VMP), a cloud-complete educational platform targeting manufacturing industries providing tutorials and sandboxing. The scope of this paper focuses on the robot control architecture of the platform which allows for seamless control of both virtual and physical robots and end-effectors accompanied by supportive educational materials. The platform seeks to derisk automation investments for small to medium enterprises by offering confirmatory environments and knowledge transfer opportunities.

KW - Robotics

KW - Automation

KW - Control

KW - Manufacturing industries

KW - Cloud computing

KW - Service robots

KW - Robot controls

KW - tutorials

KW - End effectors

KW - automation

KW - robotics

KW - control

UR - http://www.scopus.com/inward/record.url?scp=85163343181&partnerID=8YFLogxK

U2 - 10.1109/ICIT58465.2023.10143103

DO - 10.1109/ICIT58465.2023.10143103

M3 - Conference contribution

SN - 979-8-3503-3651-1

BT - Proceedings of 2023 IEEE International Conference on Industrial Technology (ICIT)

PB - IEEE

T2 - International Conference on Industrial Technology

Y2 - 4 April 2023 through 6 April 2023

ER -

Cloud-based learning for robot control

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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Cite this