Prediction of Joint Acceleration of 2 DOF Robot Manipulator Using Supervised Learning

Dr M. K. Satyarthi, Tirthankar Roy, Sourabh Anand
International Journal of Analytical, Experimental and Finite Element Analysis
Volume 9: Issue 2, June 2022, pp 20-25


Author's Information

Tirthankar Roy2 

Corresponding Author
2Student, Department of Robotics and Automation Engineering, USICT, Guru Gobind Singh Indraprastha University, Delhi, India

Dr M. K. Satyarthi1

1Assistant Professor, Department of Robotics and Automation Engineering, USICT, Guru Gobind Singh Indraprastha University, Delhi, India

Sourabh Anand3

3Research Scholar, Department of Mechanical and Automation Engineering, USICT, Guru Gobind Singh Indraprastha University, Delhi, India


Technical Article -- Peer Reviewed
Published online – 04 August 2022

Open Access article under Creative Commons License

Cite this article – Dr M. K. Satyarthi, Tirthankar Roy, Sourabh Anand “Prediction of Joint Acceleration of 2 DOF Robot Manipulator Using Supervised Learning”, International Journal of Analytical, Experimental and Finite Element Analysis, RAME Publishers, vol. 9, issue 2, pp. 20-25, June 2022.
https://doi.org/10.26706/ijaefea.2.9.arset1930


Abstract:-
Robo-analyzer (RA) is open-source software that uses a 3D representation of a robot manipulator to carry out various analytical studies. It was created primarily to assist instructors and students in getting started with robotics teaching and learning utilizing framework-based skeleton models or computer aided design (CAD) software designs of serial robots i.e., articulated robot. The RA software is used in this work to simulate and examine a two-degree of freedom (DOF) robot with two link and two revolute joints respectively. The joint length is kept constant at 0.2m, and the joint velocity is varied from 0 to 180 degrees per second. The two-link manipulator is permitted to carry out forward kinematics after generating and establishing the input parameters for the simulation of the 2DOF model, which results in simulating the joint acceleration values, and that is the primary prerequisite for the machine learning (ML) process. The model tends to deduce the relationship between the input and output parameters in this study, which further aids in the deduction of a linear relationship between the two parameters, especially input and output parameter i.e., link length coordinates, joint velocity, and joint acceleration. The experimentation was then carried out on the basis of RA data to apply linear regression machine learning technique (LRMLT), which will assist in the prediction of an output, namely joint acceleration. The model tends to pave way for future research which can be carried out for joint vibration which is solely based on the basis of the acceleration present at joint.
Index Terms:-
Robot manipulator, Forward kinematics, Supervised learning, Linear regression.
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