Determination of inverse kinematic solutions for a 3 Degree of Freedom Parallel Manipulator using Machine Learning
| Autor: | Mark Joy, T K Sunil Kumar, Mervin Joe Thomas, Mithun M. Sanjeev, A. P. Sudheer |
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| Rok vydání: | 2020 |
| Předmět: |
Polynomial regression
business.industry Parallel manipulator Inverse Kinematics Machine learning computer.software_genre 01 natural sciences 010305 fluids & plasmas Random forest Computer Science::Robotics Support vector machine 0103 physical sciences Linear regression Artificial intelligence Algebraic number 010306 general physics business computer Mathematics |
| Zdroj: | 2020 IEEE Students Conference on Engineering & Systems (SCES). |
| DOI: | 10.1109/sces50439.2020.9236725 |
| Popis: | As the complexity of parallel manipulator increases, it becomes difficult to model it kinematically. Performing the inverse kinematic analysis using the algebraic approach for such manipulators are also a cumbersome task and nearly impossible for some cases. This paper presents the determination of the inverse kinematic solutions for a $3-\underline{\mathrm{P}}\text{SS}$ (P-Prismatic, S-Spherical) parallel manipulator using various Machine Learning approaches such as Multiple Linear Regression, Multi-Variate Polynomial Regression, Support Vector Regression, Decision Tree Regression and Random Forest Regression. The P has been underlined to indicate the active joint in the mechanism. The results are presented and compared with the experimental and analytical results to prove the efficiency of the proposed approach. |
| Databáze: | OpenAIRE |
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