Modelado de un cuello robótico blando mediante aprendizaje automático

Nicole A. Continelli; Luis Fernando Nagua Cuenca; Concepción A. Monje; Carlos  Balaguer

doi:10.4995/riai.2023.18752

Autores/as

Nicole A. Continelli Universidad Carlos III de Madrid
Luis F. Nagua Universidad Carlos III de Madrid
Concepción A. Monje Universidad Carlos III de Madrid
Carlos Balaguer Universidad Carlos III de Madrid

DOI:

https://doi.org/10.4995/riai.2023.18752

Palabras clave:

robótica blanda, curvatura constante (CC), aprendizaje automático, red neuronal, perceptrón multicapa (MLP), funcion de activación

Resumen

En este trabajo se aborda el problema del modelado de un cuello robótico blando mediante el uso de diferentes arquitecturas de redes neuronales, estudiando la influencia en los resultados del número de capas de cada red y de su correspondiente función de activación. Se emplearan las funciones de activación Tangente Hiperbólica (TANH) y Unidad Lineal Exponencial (ELU). Los modelos obtenidos se compararan con un modelo basado en Perceptron Multicapa (MLP) de parámetros optimizados, así como
con el modelo cinemático analítico del cuello. Los resultados experimentales obtenidos demostraran la ventaja del empleo de las técnicas de aprendizaje automático para el modelado de sistemas altamente no lineales como el del cuello robótico blando, cuya característica elástica dificulta la formulación de un modelo analítico robusto.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Nicole A. Continelli, Universidad Carlos III de Madrid

Departamento de Ingeniería de Sistemas y Automática, RoboticsLab

Luis F. Nagua, Universidad Carlos III de Madrid

Departamento de Ingeniería de Sistemas y Automática, RoboticsLab

Concepción A. Monje, Universidad Carlos III de Madrid

Departamento de Ingeniería de Sistemas y Automática, RoboticsLab

Carlos Balaguer, Universidad Carlos III de Madrid

Departamento de Ingeniería de Sistemas y Automática, RoboticsLab

Citas

Becerra, Y., Arbulu, M., Soto, S., Martinez, F., 2019. A comparison among the denavit-hartenberg, the screw theory, and the iterative methods to solve inverse kinematics for assistant robot arm. In: International Conference on Swarm Intelligence. Springer, pp. 447-457. https://doi.org/10.1007/978-3-030-26369-0_42

Brownlee, J., Mastery, M. L., 2017. Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow Using Keras. Machine Learning Mastery. URL: https://books.google.es/books?id=eJw2nQAACAAJ

Clevert, D.-A., Unterthiner, T., Hochreiter, S., 2015. Fast and accurate deep network learning by exponential linear units (elus).URL: https://arxiv.org/abs/1511.07289 DOI: 10.48550/ARXIV.1511.07289

Continelli, N., Nagua, L., Monje, C. A., Balaguer, C., 2022. Identificaci'on de un cuello robótico blando mediante aprendizaje automático. In: Jornadas de Robótica, Educación y Bioingeniería, pp. 124-130.

Copaci, D., Muñoz, J., González, I., Monje, C. A., Moreno, L., 2020. SMAdriven soft robotic neck: Design, control and validation. IEEE Access 8, 199492-199502. https://doi.org/10.1109/ACCESS.2020.3035510

Gholamy, A., Kreinovich, V., Kosheleva, O., 2018. Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation. International Journal of Intelligent Technologies and Applied Statistics 11 (2), 105-111.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014. Generative adversarial nets. Advances in neural information processing systems 27.

Goodfellow, I. J., Bengio, Y., Courville, A., 2016. Deep Learning. MIT Press, Cambridge, MA, USA.

Hernández-Vicen, J., Martínez, S., Balaguer, C., 2021. Principios básicos para el desarrollo de una aplicaci'on de bi-manipulación de cajas por un robot humanoide. Revista Iberoamericana de Automática e Informática Industrial 18 (2), 129-137. https://doi.org/10.4995/riai.2020.13097

Jin, L., Li, S., Yu, J., He, J., 2018. Robot manipulator control using neural networks: A survey. Neurocomputing 285, 23-34. https://doi.org/10.1016/j.neucom.2018.01.002

Jiokou Kouabon, K., Melingui, A., Lakhal, O., Kom, M., Merzouki, R., 2020. A learning framework to inverse kinematics of redundant manipulators. IFACPapersOnLine 53 (2), 9912-9917. https://doi.org/10.1016/j.ifacol.2020.12.2699

Jones, B. A., Walker, I. D., 2006. Practical kinematics for real-time implementation of continuum robots. IEEE Transactions on Robotics 22 (6), 1087- 1099. https://doi.org/10.1109/TRO.2006.886268

Köker, R., C¸ akar, T., Sari, Y., 2014. A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators. Engineering with Computers 30 (4), 641-649. https://doi.org/10.1007/s00366-013-0313-2

Mena, L., Monje, C. A., Nagua, L., Muñoz, J., Balaguer, C., 2020. Test bench for evaluation of a soft robotic link. Frontiers in Robotics and AI 7, 27. https://doi.org/10.3389/frobt.2020.00027

Muñoz, J., Monje, C. A., Nagua, L. F., Balaguer, C., 2020. A graphical tuning method for fractional order controllers based on iso-slope phase curves. ISA transactions 105, 296-307. https://doi.org/10.1016/j.isatra.2020.05.045

Nagua, L., Monje, C. A., Muñoz, J., Balaguer, C., 2018a. Design and performance validation of a cable-driven soft robotic neck. In: Proc. Actas de las Jornadas Nacionales de Robótica. pp. 1-5. URL: http://hdl.handle.net/10016/30567

Nagua, L., Muñoz, J., Monje, C. A., Balaguer, C., 2018b. A first approach to a proposal of a soft robotic link acting as a neck. Actas de las XXXIX Jornadas de Automática, Badajoz, 5-7 de Septiembre de 2018. https://doi.org/10.17979/spudc.9788497497565.0522

Nori, F., Jamone, L., Sandini, G., Metta, G., 2007. Accurate control of a humanlike tendon-driven neck. In: 2007 7th IEEE-RAS International Conference on Humanoid Robots. IEEE, pp. 371-378. https://doi.org/10.1109/ICHR.2007.4813896

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P.,Weiss, R., Dubourg, V., Vanderplas, J., Passos,A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikitlearn: Machine learning in Python. Journal of Machine Learning Research 12, 2825-2830.

Perlich, C., 2010. Learning curves in machine learning. In: Encyclopedia of Machine Learning. p. 5.

Reinecke, J., Deutschmann, B., Fehrenbach, D., 2016. A structurally flexible humanoid spine based on a tendon-driven elastic continuum. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 4714-4721. https://doi.org/10.1109/ICRA.2016.7487672

Relaño, C., Muñoz, J., Monje, C. A., Martínez, S., González, D., 2022. Modeling and control of a soft robotic arm based on a fractional order control approach. Fractal and Fractional 7 (1), 8. https://doi.org/10.3390/fractalfract7010008

Segota, S. B., Andeli'c, N., Mrzljak, V., Lorencin, I., Kuric, I., Car, Z., 2021. Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator. International Journal of Advanced Robotic Systems 18 (4), 1729881420925283. https://doi.org/10.1177/1729881420925283

Sharma, S., Sharma, S., Athaiya, A., 2017. Activation functions in neural networks. towards data science 6 (12), 310-316. https://doi.org/10.33564/IJEAST.2020.v04i12.054

Siciliano, B., Khatib, O., Kr¨oger, T., 2008. Springer handbook of robotics. Vol. 200. Springer. https://doi.org/10.1007/978-3-540-30301-5

Thuruthel, T. G., Falotico, E., Renda, F., Laschi, C., 2017. Learning dynamic models for open loop predictive control of soft robotic manipulators. Bioinspiration & biomimetics 12 (6), 066003. https://doi.org/10.1088/1748-3190/aa839f

Tran, L., Zhang, Z., Yeo, S., Sun, Y., Yang, G., 2011. Control of a cable-driven 2-dof joint module with a flexible backbone. In: 2011 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (Student). IEEE, pp. 150-155. https://doi.org/10.1109/STUDENT.2011.6089343

Wang, X., Liu, X., Chen, L., Hu, H., 2021. Deep-learning damped least squares method for inverse kinematics of redundant robots. Measurement 171, 108821. https://doi.org/10.1016/j.measurement.2020.108821

Webster III, R. J., Jones, B. A., 2010. Design and kinematic modeling of constant curvature continuum robots: A review. The International Journal of Robotics Research 29 (13), 1661-1683. https://doi.org/10.1177/0278364910368147

Zaki, M. J., Meira, Jr, W., 2020. Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd Edition. Cambridge University Press. https://doi.org/10.1017/9781108564175

Zou, J., Han, Y., So, S.-S., 2009. Overview of artificial neural networks. Artificial neural networks: methods and applications, 14-22. https://doi.org/10.1007/978-1-60327-101-1_2