Classification of Prism Object Shapes Utilizing Tactile Spatiotemporal Differential Information Obtained from Grasping by Single-Finger Robot Hand with Soft Tactile Sensor Array

2007 ◽  
Vol 19 (1) ◽  
pp. 85-96 ◽  
Author(s):  
Kenshi Watanabe ◽  
◽  
Kenichi Ohkubo ◽  
Sumiaki Ichikawa ◽  
Fumio Hara ◽  
...  
Keyword(s):  
Binary Data ◽  
Sensor Array ◽  
Feature Vector ◽  
Sensor Arrays ◽  
Tactile Sensor ◽  
Single Finger ◽  
Cell Patterns ◽  
Object Shapes ◽  
Contact Switch

Our proposal involves classifying cylindrical objects by using soft tactile sensor arrays on a single five-link robotic finger. The front of each link is covered with semicircular silicone rubber with 235 small on-off switches. On-off data from switches obtained when an object is grasped is converted to a spatiotemporal matrix. Eight cells around the contact switch are useful in extracting local spatiotemporal contact physics, so the frequency of the 8-Cell patterns composed of binary data around the switch contacted is obtained for each object and used to form a contact-feature vector. This vector is obtained 10 times of experimental trial, corresponding to each object. Vectors are classified by the Mahalanobis distance for 12 objects - cylinders and regular polygonal prisms - resulting in 14 types of grasping (14 classes). Using 6 dimensional feature vectors, over 95% classification accuracy is obtained for 7 classes derived from 5 objects having one or two types of stable grasping.

Effect of sensor size in robotic tactile sensor arrays

Robotica ◽  
1988 ◽  
Vol 6 (4) ◽  
pp. 285-287 ◽  
Author(s):  
A. W. De Groot
Keyword(s):  
Sensor Array ◽  
Empirical Investigation ◽  
Sensor Arrays ◽  
Visual Image ◽  
Tactile Sensor ◽  
Suitable Choice ◽  
Pixel Size ◽  
Optimal Result ◽  
Physical Size ◽  
Fundamental Limitation

SUMMARYThe degree to which a binary tactile (or visual) image matches the original object is limited by the resolution of the sensor array. Given this fundamental limitation it is still possible to minimize the error in the image formed by the interconnection of the centers of activated sensors along the object's edge. This is achieved by a suitable choice of the physical size of each sensor within the limits of the pixel size. An empirical investigation shows that normally a sensor area of about 50% of the square of the resolution yields an optimal result.

scholarly journals Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2547 ◽  
Author(s):  
Tuo Gao ◽  
Yongchen Wang ◽  
Chengwu Zhang ◽  
Zachariah A. Pittman ◽  
Alexandra M. Oliveira ◽  
...  
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Single Type ◽  
Multivariate Techniques ◽  
Integrated Sensor ◽  
Resistance Change ◽  
Linear Discriminant ◽  
Lift Off

Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (ΔR/R0), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.

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