Artificial Neural Network Based Tactile Sensing Unit for Robotic Hand
Unlike the conventional haptic detection with the tactile sensor or Force Sensing Resistor (FSR) sensor, this paper proposes a new algorithm for tactile sensing unit that air pressure sensors are implemented to represent the tactile degree of the robot hand which can play more accurate haptic feedback. Meanwhile, in order to optimize the performance of the tactile sensing unit, several target objects are trained with the help of Artificial Neural Network (ANN) that gives the linear output values according to the constant mass when the robot hand holds different target objects. In addition, Arrival of Time (A.o.T) algorithm is utilized for recognizing the touch points of the robot hand when the target object is compressed by the tactile sensing device. The optimal output positions can be selected through amounts of tests with various grasp positions in the haptic sensing part for the reason that different pressure-points distribution facilitates the optimization mapping. Experiments show that the proposed method can be applied for Human Robot Interaction (HRI) effectively and efficiently.
KeywordsArtificial neural network Air pressure sensor Tactile sensing unit Grasp positions
This research is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No.10073147.
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