A feature extraction algorithm for texture detection oriented to its implementation on embedded electronics based on a field-programmable gate array (FPGA) is proposed in this article. Local preprocessing with smart tactile sensors can help to improve dexterity in artificial hands. Simplicity is the goal in order to achieve a hardware-friendly strategy that can be replicated and integrated with other circuitry. This is interesting, considering that tactile sensors are arrays and FPGAs are capable of parallel execution. The proposal was tested with a custom smart tactile sensor mounted on a Cartesian robot to explore different textures. A comparison with a common feature extraction approach based on the fast Fourier transform (FFT) computation was also made. In addition, the whole procedure is implemented on a system on chip (SoC) with the feature extraction on the embedded FPGA and a k-means classifier on an advanced RISC machine (ARM) core. The proposed algorithm obtains the spatial frequency components of the tactile signal but not their power. Therefore, some information is lost with respect to that provided by the FFT. Nevertheless, an 89.17% accuracy of the proposed algorithm is obtained versus 91.4% with the FFT when 12 different textures are considered, including complex and fabric textures. There is a noticeable saving in power and hardware resources. In addition, since the size of the feature vector is much smaller, data traffic and memory usage are much lower, and the classifier can be simpler.
