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Robot Manipulator Applied to Bottle Filling Processes: An Approach in the Teaching-Learning Process

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Proceedings of the Future Technologies Conference (FTC) 2023, Volume 1 (FTC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 813))

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Abstract

In this paper, a position-based visual servo control scheme for a bottle packaging process using the ScorBot-ER-4U robot manipulator is proposed. The control scheme considers the eye-to-hand configuration and is based on the kinematic model of the manipulator robot and the perspective projection model of the vision camera (Pinhole Model). The proposed scheme was evaluated on a virtual environment developed in the Unity3D graphics engine; and experimentally with the ScorBot-ER-4U manipulator robot and the ZED 2 stereo vision camera. Finally, it is concluded that the results obtained by simulation and experimentally show that the control errors converge to zero asymptotically.

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References

  1. Zheng, T., Ardolino, M., Bacchetti, A., Perona, M.: The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review. Int. J. Prod. Res. 59(6), 1922–1954 (2021). https://doi.org/10.1080/00207543.2020.1824085

    Article  Google Scholar 

  2. Burova, A., et al.: Utilizing VR and Gaze tracking to develop AR solutions for industrial maintenance. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13. ACM, Honolulu (2020). https://doi.org/10.1145/3313831.3376405

  3. Andaluz, V.H., et al.: Unity3D-MatLab simulator in real time for robotics applications. In: Paolis, L.T.D., Mongelli, A. (eds.) Augmented Reality, Virtual Reality, and Computer Graphics, pp. 246–263. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40621-3_19

    Chapter  Google Scholar 

  4. Guzmán-Duque, A.: Adaptación de los universitarios a plataformas digitales y el uso de simuladores gerenciales a partir del COVID-19. Company Games Bus. Simul. Acad. J. 2(1), 87–94 (2022)

    Google Scholar 

  5. Lai, N.Y.G., Wong, K.H., Yu, L.J., Kang, H.S.: Virtual Reality (VR) in engineering education and training: a bibliometric analysis. In: Proceedings of the 2020 the 2nd World Symposium on Software Engineering, pp. 161–165. ACM, Chengdu (2020). https://doi.org/10.1145/3425329.3425360

  6. Ipiales, J.S., Araque, E.J., Andaluz, V.H., Naranjo, C.A.: Virtual training system for the teaching-learning process in the area of industrial robotics. Electronics 12(4), 974 (2023). https://doi.org/10.3390/electronics12040974

    Article  Google Scholar 

  7. Andaluz, V., Carelli, R., Salinas, L., Toibero, J.M., Roberti, F.: Visual control with adaptive dynamical compensation for 3D target tracking by mobile manipulators. Mechatronics 22(4), 491–502 (2012). https://doi.org/10.1016/j.mechatronics.2011.09.013

    Article  Google Scholar 

  8. ZED 2 - AI Stereo Camera. https://www.stereolabs.com/zed-2/. Accedido 13 de junio de 2023

  9. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520. IEEE, Salt Lake City (2018). https://doi.org/10.1109/CVPR.2018.00474

  10. Antonio, U.C.L.: Diseño e implementación de un prototipo de un sistema de automatización de llenado y distribución de botellas, p. 273

    Google Scholar 

  11. Ortiz, J.S., Palacios-Navarro, G., Andaluz, V.H., Guevara, B.S.: Virtual reality-based framework to simulate control algorithms for robotic assistance and rehabilitation tasks through a standing wheelchair. Sensors 21(15), 5083 (2021). https://doi.org/10.3390/s21155083

    Article  Google Scholar 

  12. Ruiz, R.J., Saravia, J.L., Andaluz, V.H., Sánchez, J.S.: Virtual training system for unmanned aerial vehicle control teaching–learning processes. Electronics 11(16), 2613 (2022). https://doi.org/10.3390/electronics11162613

    Article  Google Scholar 

  13. Andaluz, V.H., Carvajal, C.P., Pérez, J.A., Proaño, L.E.: Kinematic nonlinear control of aerial mobile manipulators. In: Huang, Y.A., Hao, W., Liu, H., Yin, Z. (eds.) Intelligent Robotics and Applications, pp. 740–749. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65298-6_66

    Chapter  Google Scholar 

  14. Soria, C., Roberti, F., Carelli, R., Sebastián, J.M.: Control servo visual de un robot manipulador tipo scara basado en pasividad (2006)

    Google Scholar 

  15. Castillo, D.A., Geovanny, W.: Percepción 3D y planificación de trayectorias basado en una combinación de lidar 2D y visión para vehículos terrestres no tripulados (2018)

    Google Scholar 

  16. Sergiyenko, O., Flores-Fuentes, W., Mercorelli, P. (eds.): Machine Vision and Navigation. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22587-2

    Book  Google Scholar 

  17. García, M.A., Cárdenas, A., Rendón, J.M., Maya Méndez, M.: Una Plataforma de Control Basado en Visión para la Rehabilitación de Robots Manipuladores de Tipo Industrial. Computación y Sistemas 12(4), 409–420 (2009)

    Google Scholar 

  18. Andaluz, V.H., Pérez, J.A., Carvajal, C.P., Ortiz, J.S.: Virtual environment for teaching and learning robotics applied to industrial processes. In: Paolis, L.TDe., Bourdot, P. (eds.) Augmented Reality, Virtual Reality, and Computer Graphics: 6th International Conference, AVR 2019, Santa Maria al Bagno, Italy, June 24–27, 2019, Proceedings, Part II, pp. 442–455. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25999-0_36

    Chapter  Google Scholar 

  19. Ávila, M.A.O., Arancibia, J.A.G.: Sistema de localización autónoma para robots móviles basado en fusión de sensores propioceptivos. Revista Politécnica 11(21), 75–84 (2015)

    Google Scholar 

  20. Kumar, R., Chand, P.: Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u (2015). https://doi.org/10.1109/ICARA.2015.7081175

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Acknowledgments

The authors would like to thank the Universidad de las Fuerzas Armadas ESPE, and to the ARSI Research Group for their support in developing this work.

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Correspondence to Jessica S. Ortiz .

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Guilcazo, C.P., Nuñez, J.S., Ortiz, J.S., Andaluz, V.H. (2023). Robot Manipulator Applied to Bottle Filling Processes: An Approach in the Teaching-Learning Process. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 1. FTC 2023. Lecture Notes in Networks and Systems, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-031-47454-5_4

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