Abstract
Tactile sensors suffer from many types of interference and errors like crosstalk, non-linearity, drift or hysteresis, therefore calibration should be carried out to compensate for these deviations. However, this procedure is difficult in sensors mounted on artificial hands for robots or prosthetics for instance, where the sensor usually bends to cover a curved surface. Moreover, the calibration procedure should be repeated often because the correction parameters are easily altered by time and surrounding conditions. Furthermore, this intensive and complex calibration could be less determinant, or at least simpler. This is because manipulation algorithms do not commonly use the whole data set from the tactile image, but only a few parameters such as the moments of the tactile image. These parameters could be changed less by common errors and interferences, or at least their variations could be in the order of those caused by accepted limitations, like reduced spatial resolution. This paper shows results from experiments to support this idea. The experiments are carried out with a high performance commercial sensor as well as with a low-cost error-prone sensor built with a common procedure in robotics.