Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction

Sensors
Juan M GandariasAlfonso J García-Cerezo

Abstract

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was ch...Continue Reading

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Citations

Dec 12, 2019·Frontiers in Neurorobotics·Shaowei JinFuchun Sun
Dec 11, 2019·Sensors·Francisco PastorJesús M Gómez-de-Gabriel
Jun 12, 2021·Frontiers in Robotics and AI·Daniel De BarrieKhaled Elgeneidy

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Methods Mentioned

BETA
features extraction

Software Mentioned

AlexNet
Tekscan
matlab
Neural Network Toolbox
Scan
Smart Materials 3D
Curve Fitting Toolbox

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