Alves de Oliveira Thiago Eustaquio, Cretu Ana-Maria, Petriu Emil M
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Department of Computer Science and Engineering, Université du Quebec en Outaouais, Gatineau, QC J8X 3X7, Canada.
Sensors (Basel). 2017 May 23;17(6):1187. doi: 10.3390/s17061187.
Robots are expected to recognize the properties of objects in order to handle them safely and efficiently in a variety of applications, such as health and elder care, manufacturing, or high-risk environments. This paper explores the issue of surface characterization by monitoring the signals acquired by a novel bio-inspired tactile probe in contact with ridged surfaces. The tactile module comprises a nine Degree of Freedom Microelectromechanical Magnetic, Angular Rate, and Gravity system (9-DOF MEMS MARG) and a deep MEMS pressure sensor embedded in a compliant structure that mimics the function and the organization of mechanoreceptors in human skin as well as the hardness of the human skin. When the modules tip slides over a surface, the MARG unit vibrates and the deep pressure sensor captures the overall normal force exerted. The module is evaluated in two experiments. The first experiment compares the frequency content of the data collected in two setups: one when the module is mounted over a linear motion carriage that slides four grating patterns at constant velocities; the second when the module is carried by a robotic finger in contact with the same grating patterns while performing a sliding motion, similar to the exploratory motion employed by humans to detect object roughness. As expected, in the linear setup, the magnitude spectrum of the sensors' output shows that the module can detect the applied stimuli with frequencies ranging from 3.66 Hz to 11.54 Hz with an overall maximum error of ±0.1 Hz. The second experiment shows how localized features extracted from the data collected by the robotic finger setup over seven synthetic shapes can be used to classify them. The classification method consists on applying multiscale principal components analysis prior to the classification with a multilayer neural network. Achieved accuracies from 85.1% to 98.9% for the various sensor types demonstrate the usefulness of traditional MEMS as tactile sensors embedded into flexible substrates.
人们期望机器人能够识别物体的特性，以便在各种应用中安全、高效地处理物体，如健康和老年护理、制造业或高风险环境。本文通过监测一种新型仿生触觉探头与带脊表面接触时采集的信号，探讨了表面特征描述问题。该触觉模块包括一个九自由度微机电磁、角速率和重力系统（9-DOF MEMS MARG）以及一个嵌入柔性结构的深度微机电压力传感器，该结构模仿了人类皮肤中机械感受器的功能和组织以及人类皮肤的硬度。当模块尖端在表面上滑动时，MARG单元振动，深度压力传感器捕获施加的总法向力。该模块在两个实验中进行了评估。第一个实验比较了在两种设置下收集的数据的频率内容：一种是将模块安装在直线运动滑架上，该滑架以恒定速度滑动四种光栅图案；另一种是模块由机器人手指携带，在与相同光栅图案接触的同时进行滑动运动，类似于人类用于检测物体粗糙度的探索性运动。正如预期的那样，在直线设置中，传感器输出的幅度谱表明该模块能够检测频率范围为3.66 Hz至11.54 Hz的施加刺激，总体最大误差为±0.1 Hz。第二个实验展示了如何将从机器人手指设置在七种合成形状上收集的数据中提取的局部特征用于对它们进行分类。分类方法包括在使用多层神经网络进行分类之前应用多尺度主成分分析。各种传感器类型实现的准确率从85.1%到98.9%，证明了传统微机电系统作为嵌入柔性基板的触觉传感器的有用性。
