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.
Active Tactile Sensing for Texture Perception in Robotic Systems
2023-07-27
doi:10.15167/wang-si-ao_phd2023-07-27
Theses
Electronic Resource
English
DDC: | 629 |
Tactile Sensing for Dexterous Robotic Hands
NTRS | 2000
|Tactile Based Active Sensing for Detecting Stiffness and Contact Point Using Robotic Finger
British Library Online Contents | 1996
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