Artificial neural network for the extraction of dynamic parameters of robots from incomplete information of their movement
Keywords:
Neural network, Robots, Dynamic parameters
Abstract
The artificial neural networks are suitable for processing incomplete data to achieve the desired output. The acquisition system of the manipulator robots takes quantified samples of the position; therefore, it is not possible to execute deterministic algorithms of parameter extraction in a reasonable time. State of the art describes algorithms based on the assumption that the motion signals are not quantified, and the first and second derivatives of the position are sampled instead of estimated. In this paper, a trained neural network-based extraction parameter algorithm for a determined robot is proposed to reduce the robot characterization time. Also, with the proposed methodology is possible to extract the parameters of the same kind of robot used for training the neural network.
References
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Pohlman, J. T., & Leitner, D. W. (2003). A Comparison of Ordinary Least Squares and Logistic Regression. The Ohio Journal of Science, 103(5), 118–125.
Qin, Z., Baron, L., & Birglen, L. (2010). A new approach to the dynamic parameter identification of robotic manipulators. Robotica, 28(4). https://doi.org/10.1017/S0263574709990233
Soh, J., & Wu, X. (2017). An FPGA-Based Unscented Kalman Filter for System-On-Chip Applications. IEEE Transactions on Circuits and Systems II: Express Briefs, 64(4). https://doi.org/10.1109/TCSII.2016.2565730
Su, H., Qi, W., Yang, C., Sandoval, J., Ferrigno, G., & Momi, E. de. (2020). Deep Neural Network Approach in Robot Tool Dynamics Identification for Bilateral Teleoperation. IEEE Robotics and Automation Letters, 5(2). https://doi.org/10.1109/LRA.2020.2974445
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Wang, S., Shao, X., Yang, L., & Liu, N. (2020). Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3012196
Wu, R.-T., & Jahanshahi, M. R. (2019). Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification. Journal of Engineering Mechanics, 145(1). https://doi.org/10.1061/(ASCE)EM.1943-7889.0001556
Xiao, W., Dunford, W. G., Palmer, P. R., & Capel, A. (2007). Application of Centered Differentiation and Steepest Descent to Maximum Power Point Tracking. IEEE Transactions on Industrial Electronics, 54(5). https://doi.org/10.1109/TIE.2007.899922
You, B., Qiao, M., Xu, J., & Liu, D. (2012). Low-Speed Control of Heavy-Load Transfer Robot with Long Telescopic Boom Based on Stribeck Friction Model. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/432129
Berzal, F. (2018). Redes Neuronales y Deep Learning. Ikor.
Chávez-Olivares, C., Reyes-Cortés, F., González-Galván, E., Mendoza-Gutierrez, M., & Bonilla-Gutierrez, I. (2012). Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot. International Journal of Advanced Robotic Systems, 9(5). https://doi.org/10.5772/52190
Devore, J. L. (2011). Probability and Statistics for Engineering and the Sciences. Cengage learning.
Ding, Y., Zhao, B. Y., & Wu, B. (2014). Structural System Identification with Extended Kalman Filter and Orthogonal Decomposition of Excitation. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/987694
Hu, J., & Xiong, R. (2018). Contact Force Estimation for Robot Manipulator Using Semiparametric Model and Disturbance Kalman Filter. IEEE Transactions on Industrial Electronics, 65(4). https://doi.org/10.1109/TIE.2017.2748056
Huynh, H. N., Assadi, H., Rivière-Lorphèvre, E., Verlinden, O., & Ahmadi, K. (2020). Modelling the dynamics of industrial robots for milling operations. Robotics and Computer-Integrated Manufacturing, 61. https://doi.org/10.1016/j.rcim.2019.101852
Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1). https://doi.org/10.1115/1.3662552
Lin, F.-J., Teng, L.-T., Chen, C.-Y., & Chang, C.-K. (2008). Robust RBFN Control for Linear InductionMotor Drive Using FPGA. IEEE Transactions on Power Electronics, 23(4). https://doi.org/10.1109/TPEL.2008.924604
Liu, N., Li, L., Hao, B., Yang, L., Hu, T., Xue, T., Wang, S., & Shao, X. (2020). Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications. Complexity, 2020. https://doi.org/10.1155/2020/9053715
Ogata, K. (1996). Sistemas de control en tiempo discreto. Pearson educacion.
Pohlman, J. T., & Leitner, D. W. (2003). A Comparison of Ordinary Least Squares and Logistic Regression. The Ohio Journal of Science, 103(5), 118–125.
Qin, Z., Baron, L., & Birglen, L. (2010). A new approach to the dynamic parameter identification of robotic manipulators. Robotica, 28(4). https://doi.org/10.1017/S0263574709990233
Soh, J., & Wu, X. (2017). An FPGA-Based Unscented Kalman Filter for System-On-Chip Applications. IEEE Transactions on Circuits and Systems II: Express Briefs, 64(4). https://doi.org/10.1109/TCSII.2016.2565730
Su, H., Qi, W., Yang, C., Sandoval, J., Ferrigno, G., & Momi, E. de. (2020). Deep Neural Network Approach in Robot Tool Dynamics Identification for Bilateral Teleoperation. IEEE Robotics and Automation Letters, 5(2). https://doi.org/10.1109/LRA.2020.2974445
Swevers, J., Ganseman, C., Tukel, D. B., de Schutter, J., & van Brussel, H. (1997). Optimal robot excitation and identification. IEEE Transactions on Robotics and Automation, 13(5). https://doi.org/10.1109/70.631234
Swevers, J., Verdonck, W., & de Schutter, J. (2007). Dynamic Model Identification for Industrial Robots. IEEE Control Systems, 27(5). https://doi.org/10.1109/MCS.2007.904659
Taylor, J. R. (2013). Mecánica clásica. Reverté.
Wang, S., Shao, X., Yang, L., & Liu, N. (2020). Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3012196
Wu, R.-T., & Jahanshahi, M. R. (2019). Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification. Journal of Engineering Mechanics, 145(1). https://doi.org/10.1061/(ASCE)EM.1943-7889.0001556
Xiao, W., Dunford, W. G., Palmer, P. R., & Capel, A. (2007). Application of Centered Differentiation and Steepest Descent to Maximum Power Point Tracking. IEEE Transactions on Industrial Electronics, 54(5). https://doi.org/10.1109/TIE.2007.899922
You, B., Qiao, M., Xu, J., & Liu, D. (2012). Low-Speed Control of Heavy-Load Transfer Robot with Long Telescopic Boom Based on Stribeck Friction Model. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/432129
How to Cite
Carreón Díaz de León, C. L., Vergara-Limon, S., Gonzalez-Calleros, J. M., & Vargas-Treviño , M. A. D. (2021). Artificial neural network for the extraction of dynamic parameters of robots from incomplete information of their movement. Revista Colombiana De Computación, 22(2), 37–47. https://doi.org/10.29375/25392115.4298
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Published
2021-12-01
Section
Article of scientific and technological research
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