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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37279

Title: Trajectory creation towards fast skill deployment in plug-and-produce assembly systems: A Gaussian-Mixture Model approach
Authors: Zimmer, Melanie
Al-Yacoub, Ali
Ferreira, Pedro
Lohse, Niels
Keywords: Plug-and-produce
Learning by demonstration
Fast dynamic time warping
Gaussian mixture regression
Trajectory learning
Issue Date: 2019
Publisher: EPSRC UK-Robotics and Autonomous Systems (UK-RAS) Network
Citation: ZIMMER, M. ... et al, 2019. Trajectory creation towards fast skill deployment in plug-and-produce assembly systems: A Gaussian-Mixture Model approach. Presented at the 2nd UK Robotics and Autonomous Systems Conference (UK-RAS 2019), Loughborough, UK, 24 January 2019, pp.87-90.
Abstract: In this paper, a technique that reduces the changeover time in industrial workstations is presented. A Learning from Demonstration-based algorithm is used to acquire a new skill through a series of real-world human demonstrations in which the human shows the desired task. Initially, the collected data are filtered and aligned applying Fast Dynamic Time Warping (FastDTW). Then the aligned trajectories are modelled with a Gaussian Mixture Model (GMM), which is used as an input to generate a generalisation of the motion through a Gaussian Mixture Regression (GMR). The proposed approach is set into the context of the openMOS framework to efficiently add new skills that can be performed on different workstations. The main benefit of this work in progress is providing an intuitive, simple technique to add new robotics skills to an industrial platform which accelerates the changeover phase in manufacturing scenarios.
Description: This is a conference paper.
Sponsor: The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680735, project openMOS (Open Dynamic Manufacturing Operating System for Smart Plug-and-Produce Automation Components). Funding from the Engineering and Physical Science Research Council Centre for Doctoral Training in Embedded Intelligence (grant no. EP/L014998/1) is also acknowledged.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/37279
Publisher Link: https://www.ukras.org/publications/ras-proceedings/
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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