Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga (UMA), 29010 Malaga, Spain.
Instituto Universitario de Investigación en Ingeniería Mecatrónica y Sistemas Ciberfísicos (IMECH.UMA), Universidad de Málaga (UMA), 29017 Malaga, Spain.
Sensors (Basel). 2023 Apr 19;23(8):4120. doi: 10.3390/s23084120.
This paper presents a procedure for classifying objects based on their compliance with information gathered using tactile sensors. Specifically, smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from moment-versus-time graphs are proposed as features, to build the input vector of a classifier. The extraction of these features was implemented in the field programmable gate array (FPGA) of a system on chip (SoC), while the classifier was implemented in its ARM core. Many different options were realized and analyzed, depending on their complexity and performance in terms of resource usage and accuracy of classification. A classification accuracy of over 94% was achieved for a set of 42 different classes. The proposed approach is intended for developing architectures with preprocessing on the embedded FPGA of smart tactile sensors, to obtain high performance in real-time complex robotic systems.
本文提出了一种基于物体对触觉传感器采集到的信息的一致性进行分类的方法。具体来说，智能触觉传感器在物体被挤压和释放时提供触觉图像的原始矩。从力矩-时间图中提出了一组简单的参数作为特征，构建分类器的输入向量。这些特征的提取是在片上系统 (SoC) 的现场可编程门阵列 (FPGA) 中实现的，而分类器是在其 ARM 核中实现的。根据其在资源使用和分类准确性方面的复杂性和性能，实现和分析了许多不同的选项。对于一组 42 个不同的类别，实现了超过 94%的分类准确性。该方法旨在为智能触觉传感器的嵌入式 FPGA 上的预处理开发架构，以在实时复杂机器人系统中获得高性能。
