During recent decades, strain gauge-based joint torque sensors have been commonly used to provide high-fidelity torque measurements in robotics. Although measurement of joint torque/force is often required in engineering research and development, the gluing and wiring of strain gauges used as torque sensors pose difficulties during integration within the restricted space available in small joints. The problem is compounded by the need for a scalable geometric design to measure joint torque. In this communication, we describe a novel design of a strain gauge-based mono-axial torque sensor referred to as square-cut torque sensor (SCTS), the significant features of which are high degree of linearity, symmetry, and high scalability in terms of both size and measuring range. Most importantly, SCTS provides easy access for gluing and wiring of the strain gauges on sensor surface despite the limited available space. We demonstrated that the SCTS was better in terms of symmetry (clockwise and counterclockwise rotation) and more linear. These capabilities have been shown through finite element modeling (ANSYS) confirmed by observed data obtained by load testing experiments. The high performance of SCTS was confirmed by studies involving changes in size, material and/or wings width and thickness. Finally, we demonstrated that the SCTS can be successfully implementation inside the hip joints of miniaturized hydraulically actuated quadruped robot-MiniHyQ. This communication is based on work presented at the 18th International Conference on Climbing and Walking Robots (CLAWAR).


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    Titel :

    Towards Scalable Strain Gauge-Based Joint Torque Sensors


    Beteiligte:

    Erscheinungsdatum :

    2017-08-18


    Anmerkungen:

    Khan , H , D'Imperio , M , Cannella , F , Caldwell , D G , Cuschieri , A & Semini , C 2017 , ' Towards Scalable Strain Gauge-Based Joint Torque Sensors ' , Sensors , vol. 17 , no. 8 , 1905 , pp. 1-17 . https://doi.org/10.3390/s17081905



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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