electronics-10-01509.pdf (1.27 MB)
Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities
journal contributionposted on 2021-06-23, 10:24 authored by Ali Al-Yacoub, Myles Flanagan, Achim Buerkle, Thomas Bamber, Pedro FerreiraPedro Ferreira, Ella-Mae HubbardElla-Mae Hubbard, Niels LohseNiels Lohse
With collaborative robots and the recent developments in manufacturing technologies, physical interactions between humans and robots represent a vital role in performing collaborative tasks. Most previous studies have focused on robot motion planning and control during the execution of the task. However, further research is required for direct physical contact for human-robot or robot-robot interactions, such as co-manipulation. In co-manipulation, a human operator manipulates a shared load with a robot through a semi-structured environment. In such scenarios, a multi-contact point with the environment during the task execution results in a convoluted force/toque signature that is difficult to interpret. Therefore, in this paper, a muscle activity sensor in the form of an electromyograph (EMG) is employed to improve the mapping between force/torque and displacements in co-manipulation tasks. A suitable mapping was identified by comparing the root mean square error amongst data-driven models, mathematical models, and hybrid models. Thus, a robot was shown to effectively and naturally perform the required co-manipulation with a human. This paper’s proposed hypotheses were validated using an unseen test dataset and a simulated co-manipulation experiment, which showed that the EMG and data-driven model improved the mapping of the force/torque features into displacements.
Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)
Engineering and Physical Sciences Research CouncilFind out more...
- Mechanical, Electrical and Manufacturing Engineering
- VoR (Version of Record)
Rights holder© The Authors
Publisher statementThis is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/