scholarly journals Online Morphological Adaptation for Tactile Sensing Augmentation

2021 ◽  
Vol 8 ◽  
Author(s):  
Josie Hughes ◽  
Luca Scimeca ◽  
Perla Maiolino ◽  
Fumiya Iida
Keyword(s):  
Classification Accuracy ◽  
Object Classification ◽  
Tactile Sensor ◽  
Tactile Sensing ◽  
Morphological Adaptation ◽  
Sensor Structure ◽  
Morphology And Structure ◽  
Morphological Computation ◽  
Selection Of

Sensor morphology and structure has the ability to significantly aid and improve tactile sensing capabilities, through mechanisms such as improved sensitivity or morphological computation. However, different tactile tasks require different morphologies posing a challenge as to how to best design sensors, and also how to enable sensor morphology to be varied. We introduce a jamming filter which, when placed over a tactile sensor, allows the filter to be shaped and molded online, thus varying the sensor structure. We demonstrate how this is beneficial for sensory tasks analyzing how the change in sensor structure varies the information that is gained using the sensor. Moreover, we show that appropriate morphology can significantly influence discrimination, and observe how the selection of an appropriate filter can increase the object classification accuracy when using standard classifiers by up to 28%.

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.

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 Comparison of LSS-IV and LISS-III+LISS-IV merged data for classification of crops

2014 ◽  
Vol II-8 ◽  
pp. 101-105 ◽  
Author(s):  
R. Hebbar ◽  
M. V. R. Sesha Sai
Keyword(s):  
Image Quality ◽  
Data Fusion ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Principal Component ◽  
Pass Filter ◽  
Simultaneous Acquisition ◽  
Liss Iii ◽  
Image Quality Index ◽  
Selection Of

Resourcesat-1 satellite with its unique capability of simultaneous acquisition of multispectral images at different spatial resolutions (AWiFS, LISS-III and LISS-IV MX / Mono) has immense potential for crop inventory. The present study was carried for selection of suitable LISS-IV MX band for data fusion and its evaluation for delineation different crops in a multi-cropped area. Image fusion techniques namely intensity hue saturation (IHS), principal component analysis (PCA), brovey, high pass filter (HPF) and wavelet methods were used for merging LISS-III and LISS-IV Mono data. The merged products were evaluated visually and through universal image quality index, ERGAS and classification accuracy. The study revealed that red band of LISS-IV MX data was found to be optimal band for merging with LISS-III data in terms of maintaining both spectral and spatial information and thus, closely matching with multispectral LISS-IVMX data. Among the five data fusion techniques, wavelet method was found to be superior in retaining image quality and higher classification accuracy compared to commonly used methods of IHS, PCA and Brovey. The study indicated that LISS-IV data in mono mode with wider swath of 70 km could be exploited in place of 24km LISS-IVMX data by selection of appropriate fusion techniques by acquiring monochromatic data in the red band.

scholarly journals Artificial Tactile Sensor Structure for Surface Topography Through Sliding

2018 ◽  
Vol 23 (6) ◽  
pp. 2638-2649 ◽  
Author(s):  
Kwonsik Shin ◽  
Minkyung Sim ◽  
Eunmin Choi ◽  
Hyunchul Park ◽  
Ji-Woong Choi ◽  
...  
Keyword(s):  
Surface Topography ◽  
Tactile Sensor ◽  
Sensor Structure

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.

Object Classification and Grasp Planning Using Visual and Tactile Sensing

2016 ◽  
Vol 46 (7) ◽  
pp. 969-979 ◽  
Author(s):  
Fuchun Sun ◽  
Chunfang Liu ◽  
Wenbing Huang ◽  
Jianwei Zhang
Keyword(s):  
Object Classification ◽  
Tactile Sensing ◽  
Grasp Planning

scholarly journals Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

Sensors ◽  
2011 ◽  
Vol 11 (2) ◽  
pp. 1721-1743 ◽  
Author(s):  
Birsel Ayrulu-Erdem ◽  
Billur Barshan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Motion Classification ◽  
Artificial Neural ◽  
Leg Motion ◽  
Function Selection ◽  
Selection Of

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.

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