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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

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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...

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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
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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.
Keywords: Teaching interface; Teleoperation; Dynamic movement primitive (DMP); Gaussian mixture regression (GMR); Dynamic time warping (DTW)
College: Faculty of Science and Engineering
Funders: This work was partially supported by Royal Society International Exchanges award IE170247, Newton Mobility Grant IE150858, and Changzhou International Collaboration grant CZ20170018.
Issue: 1
Start Page: 110
End Page: 121

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