The detection of the properties of objects is essential to deal with the manipulation of objects with artificial hands and grippers. In particular, texture detection is a common challenge in robotics. In the quest for smooth and natural manipulation, response times in the order of milliseconds are needed. In a context where the number of sensors and actuators integrated in the system increases, it becomes necessary to pre-process in local electronics. This pre-processing reduces the number of interconnecting wires that hinder movement, and also the computational load and data traffic. This paper proposes the use of the Goertzel algorithm as an alternative to the common use of FFT to obtain the features used to identify a texture. The lower computational cost of the Goertzel algorithm translates into lower resource and power consumption. This lower cost is observed for a limited number of features to be extracted from the raw signal, which can be assumed for an application that seeks to obtain main features of the manipulated object, and not an exhaustive characterisation of the object. This paper shows that a set of 12 textures can be classified with 84.8% accuracy by extracting features that give the signal power for 16 selected frequencies in the spectrum.
