International Journal of Analytical, Experimental and Finite Element Analysis Volume 9, Issue 2, June 2022, pp 20 - 25 https://doi.org/10.26706/ijaefea.2.9.arset1930 20 Int. J. of Analytical, Experimental and Finite Element Analysis Dr M. K. Satyarthi 1 mksssrewa@ipu.ac.in Tirthankar Roy 2 tirthankar.00616418720@ipu.ac.in Sourabh Anand 3 sourabh.17216490020@ipu.ac.in 1 Assistant Professor, Department of Mechanical and Automation Engineering, USICT, Guru Gobind Singh Indraprastha University, Delhi, India 2 Student, Department of Robotics and Automation Engineering, USICT, Guru Gobind Singh Indraprastha University, Delhi, India 3 Research Scholar, Department of Mechanical and Automation Engineering, USICT, Guru Gobind Singh Indraprastha University, Delhi, India Prediction of Joint Acceleration of 2 DOF Robot Manipulator Using Supervised Learning 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 tocarry 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. Keywords— Robot manipulator, Forward kinematics, Supervised learning, Linear regression I. INTRODUCTION Robotics is a field which has achieved a steep growth in the last two decades, among the numerous kinds of robots; serial-linked robots arethe ones which has the most usage in diverse application in multiple industries mainly in aeronautics, healthcare domain and automobile industry etc. Hence monitoring throughout the years has led to this conclusion that the most partof the introductory courses on robotics focuses on the mechanics of the serial robots. The studies of robotics are generally not very instinctual to teach or grasp, as these topics involve knowledge from domain consisting of linear algebraic co-ordinate transformation, and fundamentals of mechanics. Taking consideration of the geometry, the architecture and the motion of the robot with the sole help of textbooks can be a difficult task. These criteria highlight the need of having robot learning/teaching software that can be used in conjunction with any robotics textbook. An excellent learning tool can improve the efficiency of the learning/teaching process. With less or no work required to develop, visualize, and simulate a robot model in a CAD environment, more time may be spent Research Paper – Peer Reviewed Published online – 04 August 2022 © 2022 RAME Publishers This is an open access article under the CC BY 4.0 International License https://creativecommons.org/licenses/by/4.0/ 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, 2022. https://doi.org/10.26706/ijaefea.2.9.arset1930