Online prediction of robot to human handover events using vibrations

Singh, Harmeet and Controzzi, Marco and Cipriani, Christian and Di Caterina, Gaetano and Petropoulakis, Lykourgos and Soraghan, John; (2018) Online prediction of robot to human handover events using vibrations. In: 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, ITA, pp. 687-691. ISBN 9789082797015 (https://doi.org/10.23919/EUSIPCO.2018.8553474)

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Abstract

One of the main issues for a robotic passer is to detect the onset of a handover, in order to avoid the object from being released when the human partner is not ready or if some impact occurs. This paper presents the methodology for a robotic passer, that is potentially able to estimate the interaction forces by the receiver on the object, thus to achieve fluent and safe handovers. The proposed system uses a vibrator that energizes the object and an accelerometer that monitors vibration propagation through the object during the handover. We focused on the machine-learning technique to classify between four states during object handover. A neural network was trained for these four states and tested online. In experimental trials an accuracy of 85.2% and 93.9% were obtained respectively for four classes and two classes of actions by a neural network classifier.

  • Item type:Book Section
    ID code:64161
    Dates:
    Date
    Event
    3 December 2018
    Published
    18 May 2018
    Accepted
    Notes:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:24 May 2018 14:44
    Last modified:21 Apr 2024 00:10
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/64161
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