Seung Ho Lee

The presence of a tactile sensor is essential to hold an object and manipulate it without damage. The tactile information helps determine whether an object is stably held. If a tactile sensor is installed at wherever the robot and the object touch, the robot could interact with more objects. In this paper, a skin type slip sensor that can be attached to the surface of a robot with various curvatures is presented. A simple mechanical sensor structure enables the cut and fit of the sensor according to the curvature. The sensor uses a non-array structure and can operate even if a part of the sensor is cut off. The slip was distinguished using a simple vibration signal received from the sensor. The signal is transformed into the time-frequency domain, and the slippage was determined using an artificial neural network. The accuracy of slip detection was compared using four artificial neural network models. In addition, the strengths and weaknesses of each neural network model were analyzed according to the data used for training. As a result, the developed sensor detected slip with an average of 95.73% accuracy at various curvatures and contact points.