Ring Gaussian Mixture Modelling and Regression for Collaborative Robots

Shirine El Zaatari, Weidong Li, Zahid Usman

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    54 Downloads (Pure)


    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 languageEnglish
    Article number103864
    JournalRobotics and Autonomous Systems
    Issue numberNovember 2021
    Early online date4 Aug 2021
    Publication statusPublished - 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/


    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 ).


    • 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


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