Ring Gaussian Mixture Modelling and Regression for Collaborative Robots

Shirine El Zaatari; Weidong Li; Zahid Usman

doi:10.1016/j.robot.2021.103864

Ring Gaussian Mixture Modelling and Regression for Collaborative Robots

Shirine El Zaatari, Weidong Li, Zahid Usman

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

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Abstract

Task Parametrised Gaussian Mixture Modelling and Regression (TP-GMM/R) is an eminent algorithm to enable collaborative robots (cobots) to adapt to new environments intuitively by learning robotic paths demonstrated by humans. Task parameters in the TP-GMM/R algorithm, i.e., frames associated with demonstration paths, are considered to have orientations by default. This requirement, however, limits the range of applications that TP-GMM/R can support. To address the issue, in this paper, a novel ring Gaussian (rGaussian) is defined to cater for orientation-less frames, and an improved TP-GMM/R algorithm based on rGaussians is developed to improve the adaptability and robustness of the algorithm. In the improved algorithm, firstly, kernels are incorporated to enable Gaussians encoding points from all demonstrations, and criteria are devised to judge a frame to be oriented or orientation-less. Then, improved Gaussian mixture regression that caters for rGaussians and orientation-less frames is developed to generate regression paths adaptable to complex environments. Finally, a series of case studies are used to benchmark the improved TP-GMM/R algorithm with the conventional TP-GMM/R algorithm under different conditions. Quantitative analyses are conducted in terms of smoothness, efficiency and reachability. Results show that the improved algorithm outperformed the conventional algorithm on all the cases.

Original language	English
Article number	103864
Journal	Robotics and Autonomous Systems
Volume	145
Issue number	November 2021
Early online date	4 Aug 2021
DOIs	https://doi.org/10.1016/j.robot.2021.103864
Publication status	Published - Nov 2021

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Robotics and Autonomous Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Autonomous Systems, 145 (2021) DOI: 10.1016/j.robot.2021.103864

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Funder

Funding Information:
This research is funded by Coventry University , Unipart Powertrain Application Ltd. (U.K.), Institute of Digital Engineering (U.K.), and the National Natural Science Foundation of China
(Project No. 51975444 ).

Keywords

Collaborative robots (cobots)
Gaussian Mixture Model
Gaussian Mixture Regression
Learning from demonstration

ASJC Scopus subject areas

Software
Control and Systems Engineering
Mathematics(all)
Computer Science Applications

Access to Document

10.1016/j.robot.2021.103864

Post-PrintAccepted author manuscript, 2.21 MBLicence: CC BY-NC-ND

https://www.researchgate.net/publication/353573458_Ring_Gaussian_Mixture_Modelling_and_Regression_for_Collaborative_Robots

Cite this

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title = "Ring Gaussian Mixture Modelling and Regression for Collaborative Robots",

abstract = "Task Parametrised Gaussian Mixture Modelling and Regression (TP-GMM/R) is an eminent algorithm to enable collaborative robots (cobots) to adapt to new environments intuitively by learning robotic paths demonstrated by humans. Task parameters in the TP-GMM/R algorithm, i.e., frames associated with demonstration paths, are considered to have orientations by default. This requirement, however, limits the range of applications that TP-GMM/R can support. To address the issue, in this paper, a novel ring Gaussian (rGaussian) is defined to cater for orientation-less frames, and an improved TP-GMM/R algorithm based on rGaussians is developed to improve the adaptability and robustness of the algorithm. In the improved algorithm, firstly, kernels are incorporated to enable Gaussians encoding points from all demonstrations, and criteria are devised to judge a frame to be oriented or orientation-less. Then, improved Gaussian mixture regression that caters for rGaussians and orientation-less frames is developed to generate regression paths adaptable to complex environments. Finally, a series of case studies are used to benchmark the improved TP-GMM/R algorithm with the conventional TP-GMM/R algorithm under different conditions. Quantitative analyses are conducted in terms of smoothness, efficiency and reachability. Results show that the improved algorithm outperformed the conventional algorithm on all the cases.",

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author = "{El Zaatari}, Shirine and Weidong Li and Zahid Usman",

note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Robotics and Autonomous Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Autonomous Systems, 145 (2021) DOI: 10.1016/j.robot.2021.103864 {\textcopyright} 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ ",

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Ring Gaussian Mixture Modelling and Regression for Collaborative Robots

Abstract

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Keywords

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