2A2-J02 Construction of Human finger type elastic tactile sensor for tactile sensation transmission system for tele-medical service

2009 ◽  
Vol 2009 (0) ◽  
pp. _2A2-J02_1-_2A2-J02_2
Author(s):  
Yusuke HIDAKA ◽  
Kaoru TASHIRO ◽  
Takashi MAENO ◽  
Masashi KONYO
Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7772
Author(s):  
Fumiya Ito ◽  
Kenjiro Takemura

The tactile sensation is an important indicator of the added value of a product, and it is thus important to be able to evaluate this sensation quantitatively. Sensory evaluation is generally used to quantitatively evaluate the tactile sensation of an object. However, statistical evaluation of the tactile sensation requires many participants and is, thus, time-consuming and costly. Therefore, tactile sensing technology, as opposed to sensory evaluation, is attracting attention. In establishing tactile sensing technology, it is necessary to estimate the tactile sensation of an object from information obtained by a tactile sensor. In this research, we developed a tactile sensor made of two-layer silicone rubber with two strain gauges in each layer and obtained vibration information as the sensor traced an object. We then extracted features from the vibration information using deep autoencoders, following the nature of feature extraction by neural firing due to vibrations perceived within human fingers. We also conducted sensory evaluation to obtain tactile scores for different words from participants. We finally developed a tactile sensation estimation model for each of the seven samples and evaluated the accuracy of estimating the tactile sensation of unknown samples. We demonstrated that the developed model can properly estimate the tactile sensation for at least four of the seven samples.


Robotica ◽  
2006 ◽  
Vol 24 (5) ◽  
pp. 595-602 ◽  
Author(s):  
Masahiro Ohka ◽  
Jyunichi Takayanagi ◽  
Takuya Kawamura ◽  
Yasunaga Mitsuya

Tactile sensing is advantageous for the acquisition of local, proximal information such as the contact condition between a finger and an object. This type of sensing, however, is not suited for recognizing an entire object that is easily recognized by vision. The objective of this paper is to ease the limitations experienced in tactile sensing by using both a neural model based on the human tactile sensation and a tactile-oriented associative memory model to enable a robot to recognize object contours. In the model, first the direction vectors belonging to segments of the object contour are obtained from a filtered tactile pattern of the simulated neurons' excitation. Second, the vectors are quantized by the chain-symbolizing method and stored for use in a memory matrix that accumulates matrix-products between the vector and its transposition. In the recalling process, complete vectors are remembered even if some input vector elements are missing. In the experiments, a robotic manipulator equipped with a tactile sensor traces five types of contours, these being a circle, a square, a triangle, a star, and a hexagon. After the robot recalls the complete contours, it is able to recognize a complete contour by just touching even a part of a contour.


2016 ◽  
Vol 2016.65 (0) ◽  
pp. _429-1_-_429-2_
Author(s):  
Takahito IMAI ◽  
Takuya KAWAMURA ◽  
Katutoshi OTSUBO ◽  
Hironao YAMADA

2020 ◽  
Vol 7 ◽  
Author(s):  
Daniel Fernandes Gomes ◽  
Zhonglin Lin ◽  
Shan Luo

Tactile sensing is an essential capability for a robot to perform manipulation tasks in cluttered environments. While larger areas can be assessed instantly with cameras, Lidars, and other remote sensors, tactile sensors can reduce their measurement uncertainties and gain information of the physical interactions between the objects and the robot end-effector that is not accessible via remote sensors. In this paper, we introduce the novel tactile sensor GelTip that has the shape of a finger and can sense contacts on any location of its surface. This contrasts to other camera-based tactile sensors that either only have a flat sensing surface, or a compliant tip of a limited sensing area, and our proposed GelTip sensor is able to detect contacts from all the directions, like a human finger. The sensor uses a camera located at its base to track the deformations of the opaque elastomer that covers its hollow, rigid, and transparent body. Because of this design, a gripper equipped with GelTip sensors is capable of simultaneously monitoring contacts happening inside and outside its grasp closure. Our extensive experiments show that the GelTip sensor can effectively localize these contacts at different locations of the finger body, with a small localization error of approximately 5 mm on average, and under 1 mm in the best cases. Furthermore, our experiments in a Blocks World environment demonstrate the advantages, and possibly a necessity, of leveraging all-around touch sensing in manipulation tasks. In particular, the experiments show that the contacts at different moments of the reach-to-grasp movements can be sensed using our novel GelTip sensor.


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