Relation between the electrical characteristics of CMC tactile sensor element and the hardness of deformable objects

2018 ◽  
Vol 2018.67 (0) ◽  
pp. 711
Author(s):  
Takahiro MORI ◽  
Takuya KAWAMURA ◽  
Katsutoshi OTSUBO ◽  
Hironao YAMADA
Keyword(s):  
Tactile Sensor ◽  
Deformable Objects ◽  
Electrical Characteristics ◽  
Sensor Element

Measurement of electrical characteristics when CMC tactile sensor element is deformed compressively

2018 ◽  
Vol 2018.67 (0) ◽  
pp. 710
Author(s):  
Satoshi KOBAYASHI ◽  
Takuya KAWAMURA ◽  
Katsutoshi OTSUBO ◽  
Hironao YAMADA
Keyword(s):  
Tactile Sensor ◽  
Electrical Characteristics ◽  
Sensor Element

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.

Design of a flexible tactile sensor for classification of rigid and deformable objects

2014 ◽  
Vol 62 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Alin Drimus ◽  
Gert Kootstra ◽  
Arne Bilberg ◽  
Danica Kragic
Keyword(s):  
Tactile Sensor ◽  
Deformable Objects

Force Characteristics for Fine Deformation of CMC Touch Sensor and Estimation of Force Variance Using Hybrid Tactile Sensor System

2012 ◽  
Vol 24 (3) ◽  
pp. 423-429
Author(s):  
Takuya Kawamura ◽  
◽  
Ko Nejigane ◽  
Kazuo Tani ◽  
Hironao Yamada
Keyword(s):  
Tactile Sensor ◽  
Force Sensor ◽  
Sensor System ◽  
Compression Force ◽  
Sensor Element ◽  
Silicon Rubber ◽  
Touch Sensor ◽  
Slight Movement ◽  
Force Characteristics

Having previously proposed a hybrid tactile sensor system consisting of a Carbon Micro-Coil (CMC) touch sensor and a force sensor, the authors have been developing a method of measuring deformation of micrometer order, force variance of 10 gram order, and compression force when an object touches a sensor element and moves slightly. In this paper, to measure the force variance for deformation of several micrometers using the CMC touch sensor, the force characteristics of the CMC touch sensor are investigated. The CMC sensor element is made of silicon rubber containing CMCs several micrometers in diameter. It is considered that the sensor element constitutes an LCR circuit, and the CMC touch sensor, deformed mechanically, produces signals due to the modification of the circuit. In the experiment detailed in this paper, to clarify the characteristics of the CMC sensor with respect to the parameters of force and deformation, the outputs of the CMC sensor and the force sensor for deformation in the range of 1 to 9 µm are sampled. As a result, it is found that the force characteristics of the CMC touch sensor are almost linear in terms of force variance within the range of 0 to 1 N, regardless of a compression force of less than 3 N. Finally, to evaluate the performance of the sensor system, force variance for a slight movement of an object touching the sensor element is estimated in an experiment.

Flexible Tactile Sensor Skin Using Wireless Sensor Elements Coupled with 2D Microwaves

2010 ◽  
Vol 22 (6) ◽  
pp. 784-789 ◽  
Author(s):  
Hiroyuki Shinoda ◽  
◽  
Hiromasa Chigusa ◽  
Yasutoshi Makino ◽  
◽  
...  
Keyword(s):  
Signal Transmission ◽  
Tactile Sensor ◽  
Electrical Contacts ◽  
Experimental Results ◽  
Wireless Sensor ◽  
Two Dimensional ◽  
Conductive Layer ◽  
Arc Length ◽  
Sensor Element ◽  
Rfid Tag

The stretchable sensor skin we propose uses microwaves propagating in a two-Dimensional Signal Transmission (2DST) sheet. A small tactile sensor chip with a pair of Resonant Proximity Connectors (RPCs) couples with 2D microwaves carrying signals. Chip operating power is also supplied by 2D microwaves. The RPC is a spiral electrode whose arc length is a quarter of the electromagnetic wavelength. Chip operating power is supplied by 2D microwaves. Sensor chips are connected to the 2DST sheet by RPCs without electrical contacts anywhere on the sheet. Resonance induced at the electrode reduces impedance between the connector and the conductive layer of the 2DST sheet, enabling sensor chips to be connected stably to the sheet. Experimental results on the RPC show the concept to be effective. We fabricated a 1-bit (touch detection) tactile sensor element consisting of a RFID-tag and RPCs, and confirmed in experiments that the sensor element operates in a stretchable 2DST sheet.

scholarly journals Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology

2020 ◽  
Vol 10 (12) ◽  
pp. 4088
Author(s):  
Andreas Verleysen ◽  
Thomas Holvoet ◽  
Remko Proesmans ◽  
Cedric Den Haese ◽  
Francis wyffels
Keyword(s):  
Reinforcement Learning ◽  
Low Cost ◽  
Tactile Sensor ◽  
The State ◽  
Learning Needs ◽  
Deformable Objects ◽  
Deformable Object ◽  
Reward Function ◽  
Rectangular Patch ◽  
Learning Agent

Deformable objects such as ropes, wires, and clothing are omnipresent in society and industry but are little researched in robotics research. This is due to the infinite amount of possible state configurations caused by the deformations of the deformable object. Engineered approaches try to cope with this by implementing highly complex operations in order to estimate the state of the deformable object. This complexity can be circumvented by utilizing learning-based approaches, such as reinforcement learning, which can deal with the intrinsic high-dimensional state space of deformable objects. However, the reward function in reinforcement learning needs to measure the state configuration of the highly deformable object. Vision-based reward functions are difficult to implement, given the high dimensionality of the state and complex dynamic behavior. In this work, we propose the consideration of concepts beyond vision and incorporate other modalities which can be extracted from deformable objects. By integrating tactile sensor cells into a textile piece, proprioceptive capabilities are gained that are valuable as they provide a reward function to a reinforcement learning agent. We demonstrate on a low-cost dual robotic arm setup that a physical agent can learn on a single CPU core to fold a rectangular patch of textile in the real world based on a learned reward function from tactile information.

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