scholarly journals Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2653 ◽  
Author(s):  
Liang Zou ◽  
Chang Ge ◽  
Z. Wang ◽  
Edmond Cretu ◽  
Xiaoou Li
Keyword(s):  
Tactile Sensor ◽  
Sensor Technology ◽  
Tactile Sensing ◽  
Sensing Systems

scholarly journals Localization of Sliding Movements Using Soft Tactile Sensing Systems with Three-axis Accelerometers

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2036 ◽  
Author(s):  
Trinh ◽  
Iwamoto ◽  
Shibuya
Keyword(s):  
Numerical Simulation ◽  
Tactile Sensor ◽  
Sensor System ◽  
Tactile Sensing ◽  
Dynamic Changes ◽  
Sliding Motion ◽  
Sensing Systems ◽  
Morphological Computation ◽  
The Ideal

This paper presents a soft tactile sensor system for the localization of sliding movements on a large contact surface using an accelerometer. The system consists of a silicone rubber base with a chamber covered by a thin silicone skin in which a three-axis accelerometer is embedded. By pressurizing the chamber, the skin inflates, changing its sensitivity to the sliding movement on the skin’s surface. Based on the output responses of the accelerometer, the sensor system localizes the sliding motion. First, we present the idea, design, fabrication process, and the operation principle of our proposed sensor. Next, we created a numerical simulation model to investigate the dynamic changes of the accelerometer’s posture under sliding actions. Finally, experiments were conducted with various sliding conditions. By confirming the numerical simulation, dynamic analysis, and experimental results, we determined that the sensor system can detect the sliding movements, including the sliding directions, velocity, and localization of an object. We also point out the role of pressurization in the sensing system’s sensitivity under sliding movements, implying the ideal pressurization for it. We also discuss its limitations and applicability. This paper reflects our developed research in intelligent integration and soft morphological computation for soft tactile sensing systems.

Tactile Sensing Using Contact Resistance in MWNT/PDMS Composites

2012 ◽  
Vol 1410 ◽  
Author(s):  
J. Ian McKelvey ◽  
Arpad Kormendy ◽  
L.P. Felipe Chibante
Keyword(s):  
Contact Resistance ◽  
Single Layer ◽  
Tactile Sensor ◽  
Force Sensor ◽  
Fast Response ◽  
Tactile Sensing ◽  
Polydimethyl Siloxane ◽  
Sensor Material ◽  
Sensing Systems ◽  
Finger Pressure

ABSTRACTA carbon nanotube polymer composite has been used to develop a flexible multi-touch tactile sensor device. Rather than employing the inherent bulk piezoresistive properties of the composite, the contact resistance between polymer and electrode was exploited to achieve finger pressure measurement with fast response. We have synthesized a series of multi-walled nanotube (MWNT) silicone composites to test the feasibility of a force sensor based on the change in surface contact resistance as a function of applied force. A single layer MWNT/polydimethyl-siloxane (PDMS) composite in the range of 1.5-3.0 % w/w nanotubes was employed as a force sensor material in an array of electrodes. It was determined that sensors based on these materials are viable as tactile sensing systems for finger-touch forces in the range of 1-100 N.

scholarly journals Application of Traditional Graphic Elements Based on Fiber Bragg Grating Tactile Sensor Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jing Xu
Keyword(s):  
Fiber Bragg Grating ◽  
Electromagnetic Interference ◽  
Low Cost ◽  
High Sensitivity ◽  
Tactile Sensor ◽  
Sensor Technology ◽  
Bragg Grating ◽  
Tactile Sensing ◽  
External Interference ◽  
Sensing Technology

In today’s era, sensing technology has been very developed in the fields of machinery, medicine, safety monitoring, and so on. In order to be applicable to more fields, new sensing technologies are also evolving. Tactile and sensory states are two important indicators of human induction to external things. Based on the contact force information between fiber Bragg grating tactile sensor and human body, this paper uses a new fiber Bragg grating tactile sensing system to study the artistic elements of ink painting in modern graphic design. Subsequently, the structure and specific performances of the sensor experimental data are analyzed. With its small volume, low cost, high sensitivity, and good compatibility with the observed body, the new fiber Bragg grating tactile sensor will not be affected by external electromagnetic interference, various noise signals, complex integration, and other problems. According to the data observed by ink painters in this paper, the new tactile sensing technology of fiber Bragg grating can well control the external interference factors. In general, the technology is of great social and practical value.

STAND-UP MOTION SUPPORT BY A MOBILE MANIPULATOR SYSTEM WITH HIGH SPEED TACTILE SENSORS

2011 ◽  
Vol 08 (03) ◽  
pp. 181-195
Author(s):  
ZHAOXIAN XIE ◽  
HISASHI YAMAGUCHI ◽  
MASAHITO TSUKANO ◽  
AIGUO MING ◽  
MAKOTO SHIMOJO
Keyword(s):  
Fuzzy Logic ◽  
Feedback Control ◽  
Experimental Verification ◽  
High Speed ◽  
Center Of Pressure ◽  
Tactile Sensor ◽  
Mobile Manipulator ◽  
Tactile Sensing ◽  
Robot Arm ◽  
Tactile Sensors

As one of the home services by a mobile manipulator system, we are aiming at the realization of the stand-up motion support for elderly people. This work is charaterized by the use of real-time feedback control based on the information from high speed tactile sensors for detecting the contact force as well as its center of pressure between the assisted human and the robot arm. First, this paper introduces the design of the tactile sensor as well as initial experimental results to show the feasibility of the proposed system. Moreover, several fundamental tactile sensing-based motion controllers necessary for the stand-up motion support and their experimental verification are presented. Finally, an assist trajectory generation method for the stand-up motion support by integrating fuzzy logic with tactile sensing is proposed and demonstrated experimentally.

Representation and tactile sensing of 3-D objects by a gripper finger

Robotica ◽  
1983 ◽  
Vol 1 (4) ◽  
pp. 217-222 ◽  
Author(s):  
Gen-Ichiro Kinoshita
Keyword(s):  
Tactile Sensor ◽  
Large Displacement ◽  
Tactile Sensing ◽  
Sensor Element ◽  
Maximum Displacement

SUMMARYThe tactile sensor is constructed as a part of the finger of a parallel jaw hand; it is of the size of a finger and allows for a large displacement of the sensor element in response to force. The structure of the tactile sensor incorporates 20 successively and closely aligned elements, which allow for a 2.5 mm maximum displacement for each element. In the described experiments we present the capabilities of the tactile sensor. The tactile sensor has the functions of: 1) discriminating the shape of the partial surface of an object; and 2) tracing by finger on the surface along the profile of an object.

scholarly journals Hi-Speed Tactile Sensing for Array-type Tactile Sensor and Object Manipulation based on Tactile Information

2012 ◽  
Vol 25 (5) ◽  
pp. 117-125
Author(s):  
Wataru Fukui ◽  
Futoshi Kobayashi ◽  
Fumio Kojima ◽  
Hiroyuki Nakamoto ◽  
Tadashi Maeda ◽  
...  
Keyword(s):  
Tactile Sensor ◽  
Object Manipulation ◽  
Tactile Sensing ◽  
Tactile Information ◽  
Array Type

scholarly journals Smart Tactile Sensing Systems Based on Embedded CNN Implementations

Micromachines ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 103 ◽  
Author(s):  
Mohamad Alameh ◽  
Yahya Abbass ◽  
Ali Ibrahim ◽  
Maurizio Valle
Keyword(s):  
Classification Accuracy ◽  
State Of The Art ◽  
Tactile Sensing ◽  
Prosthetic Devices ◽  
Decoding Algorithms ◽  
Machine Learning Methods ◽  
Complex Information ◽  
Sensing Systems ◽  
Hardware Platforms ◽  
Embedded Implementation

Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 μ J. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices.

Mechanics-induced triple-mode anticounterfeiting and moving tactile sensing by simultaneously utilizing instantaneous and persistent mechanoluminescence

2019 ◽  
Vol 6 (10) ◽  
pp. 2003-2008 ◽  
Author(s):  
Zhidong Ma ◽  
Jinyu Zhou ◽  
Jiachi Zhang ◽  
Songshan Zeng ◽  
Hui Zhou ◽  
...  
Keyword(s):  
Tactile Sensor ◽  
Tactile Sensing ◽  
Triple Mode

An intriguing mechanics-induced triple-mode anticounterfeiting device and a moving tactile sensor were developed by simultaneously utilizing transient and persistent mechanoluminescence.

scholarly journals Magnetostrictive tactile sensor of detecting friction and normal force for object recognition

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142093232
Author(s):  
Bing Zhang ◽  
Bowen Wang ◽  
Yunkai Li ◽  
Shaowei Jin
Keyword(s):  
Recognition Rate ◽  
Tactile Sensor ◽  
Tactile Sensing ◽  
Dynamic Force ◽  
Force Output ◽  
Tactile Sensors ◽  
Tactile Information ◽  
Normal Contact ◽  
New Type ◽  
Learning Machine

Tactile information is valuable in determining properties of objects that are inaccessible from visual perception. A new type of tangential friction and normal contact force magnetostrictive tactile sensor was developed based on the inverse magnetostrictive effect, and the force output model has been established. It can measure the exerted force in the range of 0–4 N, and it has a good response to the dynamic force in cycles of 0.25–0.5 s. We present a tactile perception strategy that a manipulator with tactile sensors in its grippers manipulates an object to measure a set of tactile features. It shows that tactile sensing system can use these features and the extreme learning machine algorithm to recognize household objects—purely from tactile sensing—from a small training set. The complex matrixes show the recognition rate is up to 83%.

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