No Cover Image

Journal article 209 views 32 downloads

An enhanced teaching interface for a robot using DMP and GMR

Chunxu Li Orcid Logo, Chenguang Yang, Zhaojie Ju, Andy S. K. Annamalai

International Journal of Intelligent Robotics and Applications, Volume: 2, Issue: 1, Pages: 110 - 121

Swansea University Author: Chunxu Li Orcid Logo

  • 66016.VoR.pdf

    PDF | Version of Record

    © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.

    Download (1.98MB)

Check full text

DOI (Published version): 10.1007/s41315-018-0046-x

Abstract

This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the...

Full description

Published in: International Journal of Intelligent Robotics and Applications
ISSN: 2366-5971 2366-598X
Published: Springer Science and Business Media LLC 2018
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa66016
first_indexed 2024-06-27T11:38:52Z
last_indexed 2024-11-25T14:17:19Z
id cronfa66016
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2024-06-27T12:40:29.8825699</datestamp><bib-version>v2</bib-version><id>66016</id><entry>2024-04-09</entry><title>An enhanced teaching interface for a robot using DMP and GMR</title><swanseaauthors><author><sid>e6ed70d02c25b05ab52340312559d684</sid><ORCID>0000-0001-7851-0260</ORCID><firstname>Chunxu</firstname><surname>Li</surname><name>Chunxu Li</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-04-09</date><deptcode>ACEM</deptcode><abstract>This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the playback process of the Baxter. The dynamic movement primitive (DMP) is used to model and generalize the movements. In order to learn from multiple demonstrations accurately, dynamic time warping (DTW), is used to pretreat the data recorded by the robot platform and Gaussian mixture model (GMM), aiming to generate multiple patterns after the teaching process, are employed for the calculation of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. This proposed approach is tested by performing two tasks on a KUKA iiwa and a Baxter robot.</abstract><type>Journal Article</type><journal>International Journal of Intelligent Robotics and Applications</journal><volume>2</volume><journalNumber>1</journalNumber><paginationStart>110</paginationStart><paginationEnd>121</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2366-5971</issnPrint><issnElectronic>2366-598X</issnElectronic><keywords>Teaching interface; Teleoperation; Dynamic movement primitive (DMP); Gaussian mixture regression (GMR); Dynamic time warping (DTW)</keywords><publishedDay>8</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-03-08</publishedDate><doi>10.1007/s41315-018-0046-x</doi><url/><notes/><college>COLLEGE NANME</college><department>Aerospace, Civil, Electrical, and Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>ACEM</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This work was partially supported by Royal Society International Exchanges award IE170247, Newton Mobility Grant IE150858, and Changzhou International Collaboration grant CZ20170018.</funders><projectreference/><lastEdited>2024-06-27T12:40:29.8825699</lastEdited><Created>2024-04-09T20:14:37.9084968</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Chunxu</firstname><surname>Li</surname><orcid>0000-0001-7851-0260</orcid><order>1</order></author><author><firstname>Chenguang</firstname><surname>Yang</surname><order>2</order></author><author><firstname>Zhaojie</firstname><surname>Ju</surname><order>3</order></author><author><firstname>Andy S. K.</firstname><surname>Annamalai</surname><order>4</order></author></authors><documents><document><filename>66016__30766__ea56b3305b3347cfa4804b9e521e378d.pdf</filename><originalFilename>66016.VoR.pdf</originalFilename><uploaded>2024-06-27T12:39:15.0163864</uploaded><type>Output</type><contentLength>2074705</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2024-06-27T12:40:29.8825699 v2 66016 2024-04-09 An enhanced teaching interface for a robot using DMP and GMR e6ed70d02c25b05ab52340312559d684 0000-0001-7851-0260 Chunxu Li Chunxu Li true false 2024-04-09 ACEM This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the playback process of the Baxter. The dynamic movement primitive (DMP) is used to model and generalize the movements. In order to learn from multiple demonstrations accurately, dynamic time warping (DTW), is used to pretreat the data recorded by the robot platform and Gaussian mixture model (GMM), aiming to generate multiple patterns after the teaching process, are employed for the calculation of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. This proposed approach is tested by performing two tasks on a KUKA iiwa and a Baxter robot. Journal Article International Journal of Intelligent Robotics and Applications 2 1 110 121 Springer Science and Business Media LLC 2366-5971 2366-598X Teaching interface; Teleoperation; Dynamic movement primitive (DMP); Gaussian mixture regression (GMR); Dynamic time warping (DTW) 8 3 2018 2018-03-08 10.1007/s41315-018-0046-x COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee This work was partially supported by Royal Society International Exchanges award IE170247, Newton Mobility Grant IE150858, and Changzhou International Collaboration grant CZ20170018. 2024-06-27T12:40:29.8825699 2024-04-09T20:14:37.9084968 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Chunxu Li 0000-0001-7851-0260 1 Chenguang Yang 2 Zhaojie Ju 3 Andy S. K. Annamalai 4 66016__30766__ea56b3305b3347cfa4804b9e521e378d.pdf 66016.VoR.pdf 2024-06-27T12:39:15.0163864 Output 2074705 application/pdf Version of Record true © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title An enhanced teaching interface for a robot using DMP and GMR
spellingShingle An enhanced teaching interface for a robot using DMP and GMR
Chunxu Li
title_short An enhanced teaching interface for a robot using DMP and GMR
title_full An enhanced teaching interface for a robot using DMP and GMR
title_fullStr An enhanced teaching interface for a robot using DMP and GMR
title_full_unstemmed An enhanced teaching interface for a robot using DMP and GMR
title_sort An enhanced teaching interface for a robot using DMP and GMR
author_id_str_mv e6ed70d02c25b05ab52340312559d684
author_id_fullname_str_mv e6ed70d02c25b05ab52340312559d684_***_Chunxu Li
author Chunxu Li
author2 Chunxu Li
Chenguang Yang
Zhaojie Ju
Andy S. K. Annamalai
format Journal article
container_title International Journal of Intelligent Robotics and Applications
container_volume 2
container_issue 1
container_start_page 110
publishDate 2018
institution Swansea University
issn 2366-5971
2366-598X
doi_str_mv 10.1007/s41315-018-0046-x
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the playback process of the Baxter. The dynamic movement primitive (DMP) is used to model and generalize the movements. In order to learn from multiple demonstrations accurately, dynamic time warping (DTW), is used to pretreat the data recorded by the robot platform and Gaussian mixture model (GMM), aiming to generate multiple patterns after the teaching process, are employed for the calculation of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. This proposed approach is tested by performing two tasks on a KUKA iiwa and a Baxter robot.
published_date 2018-03-08T08:29:21Z
_version_ 1821393458642812928
score 11.52865

Similar Items