Position control of human-like robotic hand index finger using machine learning

The hand has been a great tool for humans with the advantage of dexterous function and power to do daily activities such as turning a doorknob, wearing clothes, using a screwdriver and picking up objects. These dexterous hand motions are achieved due to the biomechanics of hands and redundant mechan...

Full description

Saved in:

Bibliographic Details
Main Author:	Abdul Hafidz, Muhamad Hazwan
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93046/1/MuhammadHazwanAbdulMSKE2020.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.93046
record_format	uketd_dc
spelling	my-utm-ep.930462021-11-07T06:00:39Z Position control of human-like robotic hand index finger using machine learning 2020 Abdul Hafidz, Muhamad Hazwan TK Electrical engineering. Electronics Nuclear engineering The hand has been a great tool for humans with the advantage of dexterous function and power to do daily activities such as turning a doorknob, wearing clothes, using a screwdriver and picking up objects. These dexterous hand motions are achieved due to the biomechanics of hands and redundant mechanism of hand muscles. These muscles receive brain signals and react accordingly to perform different hand motion gestures when dealing with different objects. Sub-conscious mind in our brain instructs actuation of different extrinsic and intrinsic muscles in order to move our fingers to different positions for desired functions. The index finger plays an important role in performing hand gestures for human daily activities. This work presents position control of index finger of human-like robotic hand (HR Hand) using machine learning. The HR Hand is a replication of the human hands in terms of bones, ligaments, muscles, extensor mechanism, tendon and its pulley system. The muscles of the hand were fabricated using thin multifilament McKibben muscles following design of index finger by A.A.M. Faudzi. Motion of the HR Hand is captured using ViconTM motion capture to build a K-nearest neighbour (KNN) and Artificial Neural Network (ANN) model. This model is used to predict the combination of muscles used for fingertip position control. Hyperparameter optimization is done using grid search method to obtain better accuracy. The ANN model showed to have better accuracy of 71.36 % in predicting actuation of muscle class compared to the 65.38 % by KNN. The ANN model is applied in building a feed-forward controller and verified on the system with 2.7 cm steady state error. Using this model, future control of HR Hand is expected. 2020 Thesis http://eprints.utm.my/id/eprint/93046/ http://eprints.utm.my/id/eprint/93046/1/MuhammadHazwanAbdulMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135901 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Abdul Hafidz, Muhamad Hazwan Position control of human-like robotic hand index finger using machine learning
description	The hand has been a great tool for humans with the advantage of dexterous function and power to do daily activities such as turning a doorknob, wearing clothes, using a screwdriver and picking up objects. These dexterous hand motions are achieved due to the biomechanics of hands and redundant mechanism of hand muscles. These muscles receive brain signals and react accordingly to perform different hand motion gestures when dealing with different objects. Sub-conscious mind in our brain instructs actuation of different extrinsic and intrinsic muscles in order to move our fingers to different positions for desired functions. The index finger plays an important role in performing hand gestures for human daily activities. This work presents position control of index finger of human-like robotic hand (HR Hand) using machine learning. The HR Hand is a replication of the human hands in terms of bones, ligaments, muscles, extensor mechanism, tendon and its pulley system. The muscles of the hand were fabricated using thin multifilament McKibben muscles following design of index finger by A.A.M. Faudzi. Motion of the HR Hand is captured using ViconTM motion capture to build a K-nearest neighbour (KNN) and Artificial Neural Network (ANN) model. This model is used to predict the combination of muscles used for fingertip position control. Hyperparameter optimization is done using grid search method to obtain better accuracy. The ANN model showed to have better accuracy of 71.36 % in predicting actuation of muscle class compared to the 65.38 % by KNN. The ANN model is applied in building a feed-forward controller and verified on the system with 2.7 cm steady state error. Using this model, future control of HR Hand is expected.
format	Thesis
qualification_level	Master's degree
author	Abdul Hafidz, Muhamad Hazwan
author_facet	Abdul Hafidz, Muhamad Hazwan
author_sort	Abdul Hafidz, Muhamad Hazwan
title	Position control of human-like robotic hand index finger using machine learning
title_short	Position control of human-like robotic hand index finger using machine learning
title_full	Position control of human-like robotic hand index finger using machine learning
title_fullStr	Position control of human-like robotic hand index finger using machine learning
title_full_unstemmed	Position control of human-like robotic hand index finger using machine learning
title_sort	position control of human-like robotic hand index finger using machine learning
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93046/1/MuhammadHazwanAbdulMSKE2020.pdf
_version_	1747818631666860032

Position control of human-like robotic hand index finger using machine learning

Similar Items