Comparative analysis of classification algorithms on tactile sensors

Becari, Wesley; Ruiz, Luciana da Silva; Evaristo, Bruno G. P.; Ramirez-Fernandez, Francisco J.

doi:10.1109/isce.2016.7797324

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…A low-cost tactile sensor is used to gather the desired data which is a remarkable advantage of our study; it successfully achieves the required tasks and proves its efficiency in collecting data as high-priced sensors such as iCub tactile sensor that performs the same tactile actions with nearly similar accuracy [7,8].…”

Section: Introductionmentioning

confidence: 79%

“…For understanding the information experienced by touch sensors, Artificial Intelligence (AI) algorithms have been applied which have shown to be effective in a variety of applications, including object shape recognition. For categorizing contacts, many machine learning classifiers have been constructed to analyze the collected data [7], the most often used ones are naive Bayes [9], Support Vector Machines (SVM) [10], and k-Nearest Neighbour (kNN) [9].…”

Section: Introductionmentioning

confidence: 99%

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“Tactile Object Recognition Robotic System Using Supervised Machine Learning”

Kamel

El-Sayed

Awad

et al. 2023

Journal of Advanced Engineering Trends

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This paper aims to perform shape estimation actions on 2D uniform objects such as Square, Circle, Triangle and Pentagon to predict the actual shape of the tested objects. A tactile fingertip has been used to construct the shape that encloses some pressure sensor elements (called Taxels). One advantage of this study is the taxel`s number used, which is fewer than any other types of tactile sensor in the previous related studies as well as the new proposed exploratory technique. Moreover, the collected datasets have been used as an input for the three different learning classification algorithms. k-Nearest Neighbors Classifier (KNN), Naïve Bayes and Support Vector Machine (SVM) have been implemented as supervised learning algorithms to recognize the desired object shape from the collected data. As a result, the best performance obtained with SVM is by using the Radial Basis Function (RBF) that gives an average of 96.3% accuracy in shape recognition. Not only that, another performance comparison is made by smalling the scanned area of the same tested objects; the square and circle shapes are explored in the new area because of its lower recognizing performance (94.85 % and 94.71% respectively). Thus, enhancing the accuracy to be 96.03% and 98.8% respectively which is a remarkable performance.

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Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

“Tactile Object Recognition Robotic System Using Supervised Machine Learning”

Kamel

El-Sayed

Awad

et al. 2023

Journal of Advanced Engineering Trends

View full text Add to dashboard Cite

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Integration of Robotic Vision and Tactile Sensing for Wire-Terminal Insertion Tasks

Gregorio

Zanella

Palli

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Twenty years ago, the field of Robotics was defined and galvanized by the creation of an IEEE Journal devoted to the topic. We now have the opportunity to do the same for the field of Automation. The IEEE voted in February 2003 to bifurcate the IEEE Transactions on Robotics and Automation, the top-cited archival publication on robotics. The renamed Transactions on Robotics will gain a sibling: the Transactions on Automation Science and Engineering. T-ASE will publish foundational research on Automation: scientific methods and technologies that improve efficiency, productivity, quality, and reliability, specifically for methods, machines, and systems operating in structured environments over long periods, and the explicit structuring of environments. Its coverage will go beyond Automation's roots in mass production and include many new applications areas, such as Biotechnology, pharmaceutical, and health care; Home, service, and retail; Construction, transportation, and security; Manufacturing, maintenance, and supply chains; and Food handling and processing. Research includes topics related to robots and intelligent machines/systems in structured environments and the explicit structuring of environments, and topics at the Operational/Enterprise levels such as System Modeling,

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A Survey on Intelligence Tools for Data Analytics

Singh

Gautam

Singhal

et al. 2021

Advances in Data Mining and Database Management

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The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.

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Comparative analysis of classification algorithms on tactile sensors

Cited by 3 publications

References 4 publications

“Tactile Object Recognition Robotic System Using Supervised Machine Learning”

“Tactile Object Recognition Robotic System Using Supervised Machine Learning”

Integration of Robotic Vision and Tactile Sensing for Wire-Terminal Insertion Tasks

A Survey on Intelligence Tools for Data Analytics

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