scholarly journals Recognition of Contact State of Four Layers Arrayed Type Tactile Sensor by Using Neural Networks

10.5772/6635 ◽  
2008 ◽  
Author(s):  
Seiji Aoyagi
Keyword(s):  
Neural Networks ◽  
Tactile Sensor ◽  
Contact State

scholarly journals Tactile Perception for Teleoperated Robotic Exploration within Granular Media

2021 ◽  
Vol 10 (4) ◽  
pp. 1-27
Author(s):  
Shengxin Jia ◽  
Veronica J. Santos
Keyword(s):  
Granular Media ◽  
Fluid Pressure ◽  
Tactile Sensor ◽  
Tactile Perception ◽  
Test Accuracy ◽  
Contact State ◽  
Buried Objects ◽  
Media Type ◽  
Additional Information ◽  
Internal Fluid

The sense of touch is essential for locating buried objects when vision-based approaches are limited. We present an approach for tactile perception when sensorized robot fingertips are used to directly interact with granular media particles in teleoperated systems. We evaluate the effects of linear and nonlinear classifier model architectures and three tactile sensor modalities (vibration, internal fluid pressure, fingerpad deformation) on the accuracy of estimates of fingertip contact state. We propose an architecture called the Sparse-Fusion Recurrent Neural Network (SF-RNN) in which sparse features are autonomously extracted prior to fusing multimodal tactile data in a fully connected RNN input layer. The multimodal SF-RNN model achieved 98.7% test accuracy and was robust to modest variations in granular media type and particle size, fingertip orientation, fingertip speed, and object location. Fingerpad deformation was the most informative modality for haptic exploration within granular media while vibration and internal fluid pressure provided additional information with appropriate signal processing. We introduce a real-time visualization of tactile percepts for remote exploration by constructing a belief map that combines probabilistic contact state estimates and fingertip location. The belief map visualizes the probability of an object being buried in the search region and could be used for planning.

scholarly journals Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5356 ◽  
Author(s):  
Francisco Pastor ◽  
Juan M. Gandarias ◽  
Alfonso J. García-Cerezo ◽  
Jesús M. Gómez-de-Gabriel
Keyword(s):  
Neural Networks ◽  
Tactile Sensor ◽  
Training Data ◽  
Release Process ◽  
Tactile Information ◽  
Grasping Forces ◽  
Novel Method ◽  
3D Cnn ◽  
Robotic Palpation ◽  
Exploratory Procedure

In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different grasping forces that provide information not only about the external shape of the object, but also about its internal features. The gripper consists of two underactuated fingers with a tactile sensor array in the thumb. A new representation of tactile information as 3D tactile tensors is described. During a squeeze-and-release process, the pressure images read from the tactile sensor are concatenated forming a tensor that contains information about the variation of pressure matrices along with the grasping forces. These tensors are used to feed a 3D Convolutional Neural Network (3D CNN) called 3D TactNet, which is able to classify the grasped object through active interaction. Results show that 3D CNN performs better, and provide better recognition rates with a lower number of training data.

scholarly journals An artificial spiking afferent nerve based on Mott memristors for neurorobotics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xumeng Zhang ◽  
Ye Zhuo ◽  
Qing Luo ◽  
Zuheng Wu ◽  
Rivu Midya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Complementary Metal Oxide Semiconductor ◽  
Tactile Sensor ◽  
Metal Oxide Semiconductor ◽  
Afferent Nerve ◽  
Spiking Neural Networks ◽  
Oxide Semiconductor ◽  
Semiconductor Technology ◽  
Hardware Implementations ◽  
First Time

AbstractNeuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbOx Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

scholarly journals Tactile Sensor with High-Density Microcantilever and Multiple PDMS Bumps for Contact Detection

2020 ◽  
Vol 32 (2) ◽  
pp. 297-304
Author(s):  
Tomoya Fujihashi ◽  
◽  
Fumitoshi Suga ◽  
Ryoma Araki ◽  
Jyun Kido ◽  
...  
Keyword(s):  
Contact Angle ◽  
Detection Method ◽  
Tactile Sensor ◽  
High Density ◽  
Contact Detection ◽  
Uneven Surface ◽  
Resistance Change ◽  
Contact State ◽  
Partial Contact ◽  
Contact Position

In the study, we investigated a detection method of partial contact of an object owing to curved or uneven surface of the contact object by a tactile sensor. The sensor is developed using three microcantilevers embedded in a polydimethylsiloxane (PDMS) bump. First, three bumps were employed to place a bump for each cantilever. It was possible to detect a contact position because the resistance change in the strain gauge on the cantilever under each bump significantly depended on the contact/non-contact state of each bump. Second, a tactile sensor with high-density arrangement of microcantilevers was used to detect partial or tilted contact situations. The results indicated that the output of a tactile sensor with high-density arrangement of microcantilevers reflected partial or tilted contact. It is suggested that a tactile sensor with multiple bumps and high-density microcantilevers allows for more dexterous gripping control based on the shape of the object and contact angle.

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