DETECTION OF INCIPIENT OBJECT SLIPPAGE BY SKIN-LIKE SENSING AND NEURAL-NETWORK PROCESSING

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-fo rm reconstruction and primitive recognition, it has to be the main fea ture of an artificial tactile system. The system presented here is bas ed on a neural network used to detect incipient slippage and on a skin -like sensor sensible to normal and shear stresses. Normal and shear s tresses components inside the sensor are the input data of the neural net. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being m anipulated is not needed. To validate the method we worked on both sim ulated and experimental data, In the first case, the Finite Element Me thod is used to solve the direct problem of elastic contact in its ful l nonlinearity by resorting to the lowest number of approximations reg arding the real problem. Simulation has shown that the network learns and is robust to noise. Then an experimental test was carried out. Exp erimental results show that, in a simple case, the method is able to d etect the incipiency of slippage between an object and the sensor.