Publication Type:

Conference Paper

Source:

Proceedings of Third International Conference on Innovative Computing Technology (INTECH 2013), IEEE, London (2013)

Abstract:

Target-oriented approaches have been commonly used in robotics. In 3D space, movement of a robotic arm depends on the target position which can either follow a forward or inverse kinematics approach to reach the target. Predicting the movement of a robotic arm requires prior learning through methods such as transformation matrices or other machine learning techniques. In this paper, we built an online robotic arm to extract movement datasets and have used machine learning algorithms to predict robotic arm articulation. For efficient training, small training datasets were used for learning purpose. Classification is used as a scheme to replace prediction-correction approach and to test whether the method can function as a replacement of usual forward kinematics schemes or predictor-corrector methods in directing a remotely controlled robotic articulator. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests linear SVM and Naïve Bayes algorithms as alternatives for computational intensive learning schemes while predicting articulator movement in laboratory environments.

Cite this Research Publication

Asha Vijayan, Chaitanya Medini, Hareesh Singanamala, Chaitanya Nutakki, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Classification of robotic arm movement using SVM and Naïve Bayes classifiers”, in Proceedings of Third International Conference on Innovative Computing Technology (INTECH 2013), London, 2013.

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
9th
RANK(INDIA):
NIRF 2017
150+
INTERNATIONAL
PARTNERS