An Application of Decission Tree for Modeling the Direct Kinematic Solution of 5R Planar Parallel Manipulator
DOI:
https://doi.org/10.21063/jtm.2021.v11.i1.61-73Kata Kunci:
Direct kinematic solution, 5R planar parallel manipulator, closed-form solution, decision tree algorithm, hyperparameter tuningAbstrak
This article addressed a machine learning approach for determining a solution model for the direct kinematic problems of parallel manipulators. A 5R planar parallel manipulator was utilized for that approach because it had the solution in the closed form. Initially, a dataset was created from an inverse kinematic solution of the manipulator for one of its assembly modes. Then, this dataset was fed as the input (the joint space) and the output (the platform space) for modeling the direct kinematic solution of the manipulator using one of the machine learning algorithms, which was the decision tree. To avoid overfitting during the training, hyperparameter tuning was employed to find the best parameters for the decision tree model, which was later called the best model. Hence, the best model can be validated by using the closed form solution. If the best model failed to model the direct kinematic solution in the validation, remodeling had to be performed and executed the same steps again. For remodeling, the training dataset consisted of all assembly modes of the manipulator. Consequently, the best model after remodeling was able to present the direct kinematic solutions for all possible input domains. Unfortunately, around 5% of solutions shown a higher deviation which had to be investigated in the future.
Referensi
X. Wu and Z. Xie, “Forward kinematics analysis of a novel 3-DOF parallel manipulator,” vol. 26, pp. 18–24, 2019, doi: 10.24200/sci.2018.20740.
K. Ibrahim, A. A. Ramadan, M. Fanni, Y. Kobayashi, A. Abo-ismail, and M. G. Fujie, “Development of a new 4-DOF endoscopic parallel manipulator based on screw theory for laparoscopic surgery,” Mechatronics, vol. 28, pp. 4–17, 2015, doi: 10.1016/j.mechatronics.2015.02.006.
R. F. Abo-shanab, “An Efficient Method for Solving the Direct Kinematics of Parallel Manipulators Following a Trajectory,” vol. 2, no. 3, pp. 228–233, 2014, doi: 10.12720/joace.2.3.228-233.
X. Zhijiang, L. Huan, and S. Daiping, “Forward kinematics of 3-RPS parallel mechanism based on a continuous ant colony algorithm,” China Mech. Eng., vol. 26, no. 6, pp. 799–803, 2015.
U. G. Algorithms et al., “Optimal Forward Kinematics Modeling of Stewart Manipulator,” Jordan J. Mech. Ind. Eng., vol. 3, no. 4, pp. 280–293, 2009.
E. J. Lopez, D. Servin De La Mora-Pulido, R. Servin De La Mora-Pulido, F. J. Ochoa-Estrella, M. Acosta Flores, and G. Luna-Sandoval, “Modeling in Two Configurations of a 5R 2-DoF Planar Parallel Mechanism and Solution to the Inverse Kinematic Modeling Using Artificial Neural Network,” IEEE Access, vol. 9, pp. 68583–68594, 2021, doi: 10.1109/ACCESS.2021.3073402.
A. H. Elsheikh, E. A. Showaib, and A. E. M. Asar, “Artificial Neural Network Based Forward Kinematics Solution for Planar Parallel Manipulators Passing through Singular Configuration,” Adv. Robot. Autom., vol. 2, no. 2, pp. 1–6, 2013, doi: 10.4172/2168-9695.1000106.
H. Faraji, “Solving the Forward Kinematics Problem in Parallel Manipulators Using Neural Network,” in Proceedings of the 2017 The 5th International Conference on Control, Mechatronics and Automation, 2017, pp. 23–29.
H. Sadjadian, T. D, and A. Fatehi, “Neural Networks Approaches for Computing the Forward Kinematics of a Redundant Parallel Manipulator,” Int. J. Comput. Inf. Eng., vol. 2, no. 5, pp. 1664–1671, 2008.
M. J. Thomas et al., “Determination of inverse kinematic solutions for a 3 degree of freedom parallel manipulator using machine learning,” 2020 IEEE Students’ Conf. Eng. Syst. SCES 2020, no. November, pp. 0–5, 2020, doi: 10.1109/SCES50439.2020.9236725.
M. J. Thomas, M. M. Sanjeev, A. P. Sudheer, and J. M.L, “Comparative study of various machine learning algorithms and Denavit–Hartenberg approach for the inverse kinematic solutions in a 3-PPSS parallel manipulator,” Ind. Rob., vol. 47, no. 5, pp. 683–695, 2020, doi: 10.1108/IR-11-2019-0233.
P. Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data. New York: Cambridge University Press, 2012.
Adriyan, “Analisis Kinematika dan Singularitas Manipulator Paralel Bidang 2 DOF dengan Rantai Kinematik Paralelogram Simetris,” J. Tek. Mesin, vol. 10, no. 2, pp. 79–87, 2020.
X. J. Liu and J. J. Wang, Parallel Kinematics: Type, Kinematics, and Optimal Design. Heidelberg: Springer, 2014.
S. L. Brunton and J. N. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge: Cambridge University Press, 2019.
C. R. Harris et al., “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, 2020, doi: 10.1038/s41586-020-2649-2.
T. pandas development team, “pandas-dev/pandas: Pandas.” Zenodo, Feb. 2020, doi: 10.5281/zenodo.3509134.
W. McKinney, “Data Structures for Statistical Computing in Python,” in Proceedings of the 9th Python in Science Conference, 2010, pp. 56–61, doi: 10.25080/Majora-92bf1922-00a.
F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
J. D. Hunter, “Matplotlib: A 2D graphics environment,” Comput. Sci. Eng., vol. 9, no. 3, pp. 90–95, 2007, doi: 10.1109/MCSE.2007.55.
M. L. Waskom, “seaborn: statistical data visualization,” J. Open Source Softw., vol. 6, no. 60, p. 3021, 2021, doi: 10.21105/joss.03021.
T. Kluyver et al., “Jupyter Notebooks—a publishing format for reproducible computational workflows,” in Positioning and Power in Academic Publishing: Players, Agents and Agendas, 2016, pp. 87–90, doi: 10.3233/978-1-61499-649-1-87.
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