This thesis presents a comprehensive study of tactile sensing, particularly on the prob- lem of active texture perception. It includes a brief introduction to tactile sensing technology and the neural basis for tactile perception. It follows the literature review of textural percep- tion with tactile sensing. I propose a decoding and perception pipeline to tackle fine-texture classification/identification problems via active touching. Experiments are conducted using a 7DOF robotic arm with a finger-shaped tactile sensor mounted on the end-effector to per- form sliding/rubbing movements on multiple fabrics. Low-dimensional frequency features are extracted from the raw signals to form a perceptive feature space, where tactile signals are mapped and segregated into fabric classes. Fabric classes can be parameterized and sim- plified in the feature space using elliptical equations. Results from experiments of varied control parameters are compared and visualized to show that different exploratory move- ments have an apparent impact on the perceived tactile information. It implies the possibil- ity of optimising the robotic movements to improve the textural classification/identification performance.


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    Titel :

    Active Tactile Sensing for Texture Perception in Robotic Systems



    Erscheinungsdatum :

    2023-07-27


    Anmerkungen:

    doi:10.15167/wang-si-ao_phd2023-07-27



    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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