Fundamentos de robótica aplicada: Una guía integrada sobre control, programación y entrenamiento
DOI:
https://doi.org/10.47606/ACVEN/PH0383Keywords:
Aprendizaje, Tipos de robots, Métodos de control, Programación, Entrenamiento, Manipulación robótica, Navegación autónomaAbstract
La robótica aplicada se ha convertido en una herramienta transversal en la investigación, la manufactura y la automatización. Sin embargo, su adopción por parte de profesionales no especializados en ingeniería se ve limitada por la complejidad técnica y la rápida evolución de sus paradigmas. Este artículo de revisión ofrece un marco conceptual accesible que permite a los no especialistas comprender, evaluar y aplicar sistemas robóticos. La discusión se organiza en tres pilares: la taxonomía de plataformas (manipuladores, robots móviles y humanoides), los métodos de control (cinemática, dinámica y planificación de trayectorias) y los paradigmas de programación y entrenamiento (desde arquitecturas como ROS hasta técnicas de imitación y aprendizaje por refuerzo). Se enfatiza la integración del control clásico, la programación estructurada y el aprendizaje automático en arquitecturas híbridas que logran comportamientos robustos y adaptables. De este modo, el artículo funciona como una guía educativa y estratégica que orienta en la selección, integración y operación de robots para resolver problemas reales e impulsar la innovación en múltiples dominios.
Downloads
References
Ahmad, A., & Muhammad Ali, B. (2016). Software architectures for robotic systems: A systematic mapping study. Journal of Systems and Software, 122, 16–39. https://doi.org/10.1016/j.jss.2016.08.039 DOI: https://doi.org/10.1016/j.jss.2016.08.039
Andreasson, H., Grisetti, G., Stoyanov, T., & Pretto, A. (2023). Software architectures for mobile robots. In Encyclopedia of Robotics (pp. 1–11). Springer. https://doi.org/10.48550/arXiv.1703.06907 DOI: https://doi.org/10.1007/978-3-642-41610-1_160-1
Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M., & Yoshida, C. (2009). Cognitive developmental robotics: A survey. IEEE Transactions on Autonomous Mental Development, 1(1), 12–34. https://doi.org/10.1109/TAMD.2009.2021702 DOI: https://doi.org/10.1109/TAMD.2009.2021702
Argall, B., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5), 469–483. https://doi.org/10.1016/j.robot.2008.10.024 DOI: https://doi.org/10.1016/j.robot.2008.10.024
Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94–111. https://doi.org/10.1016/j.biosystemseng.2016.06.014 DOI: https://doi.org/10.1016/j.biosystemseng.2016.06.014
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., & Erhan, D. (2017). Domain separation networks. Advances in Neural Information Processing Systems, 30, 343–351. https://doi.org/10.48550/arXiv.1608.06019
Craig, J. (2005). Introduction to robotics: Mechanics and control (3rd ed.). Pearson.
Coursera. (27 de agosto de 2025). Robotics Foundations I-Robot Modelling. https://www.coursera.org/learn/robotics-foundations-robot-modelling
De Santis, A., Siciliano, B., De Luca, A., & Bicchi, A. (2008). An atlas of physical human–robot interaction. Mechanism and Machine Theory, 43(3), 253–270. https://doi.org/10.1016/j.mechmachtheory.2007.03.003 DOI: https://doi.org/10.1016/j.mechmachtheory.2007.03.003
Devin, C., Abbeel, P., Darrell, T., & Levine, S. (2018). Deep object-centric representations for generalizable robot learning. IEEE International Conference on Robotics and Automation (ICRA). Brisbane, QLD, Australia. https://doi.org/10.1109/ICRA.2018.8461196 DOI: https://doi.org/10.1109/ICRA.2018.8461196
Dormido, S. (2006). Advanced PID control. IEEE Control Systems Magazine, 26(1), 98–101. https://doi.org/10.1109/MCS.2006.1580160 DOI: https://doi.org/10.1109/MCS.2006.1580160
Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine, 13(2), 99–110. https://doi.org/10.1109/MRA.2006.1638022 DOI: https://doi.org/10.1109/MRA.2006.1638022
Falconi, R., Melchiorri, C., Biagiotti, L., & Macchelli, A. (2006). Roboticad: A Matlab© toolbox for robot manipulators. IFAC Proceedings Volumes, 39(15), 431–436. https://doi.org/10.3182/20060906-3-IT-1901.00073 DOI: https://doi.org/10.3182/20060906-3-IT-2910.00073
Featherstone, R. (2008). Rigid body dynamics algorithms. Springer. https://doi.org/10.1007/978-1-4899-7560-7 DOI: https://doi.org/10.1007/978-1-4899-7560-7
Ghazal, M., Al-Ghadhanfari, M. y Waisi, N. (2024). Simulation of autonomous navigation of turtlebot robot system based on robot operating system. Bulletin of Electrical Engineering and Informatics, 13(2), 1238-1244. https://doi.org/10.11591/eei.v13i2.6419 DOI: https://doi.org/10.11591/eei.v13i2.6419
Hirai, K., Hirose, M., Haikawa, Y., & Takenaka, T. (1998). The development of Honda humanoid robot. Proceedings - IEEE International Conference on Robotics and Automation, 2, 1321–1326. Leuven, Belgium. https://doi.org/10.1109/ROBOT.1998.677288 DOI: https://doi.org/10.1109/ROBOT.1998.677288
Hu, F., Qu, F., Li, J., & Zhao, H. (2022). Real-time research based on ARM-based robot EtherCAT master system. International Symposium on Control Engineering and Robotics. Changsha, China. https://doi.org/10.1109/ISCER55570.2022.00009 DOI: https://doi.org/10.1109/ISCER55570.2022.00009
Ijspeert, A., Nakanishi, J., & Schaal, S. (2002). Movement imitation with nonlinear dynamical systems in humanoid robots. IEEE International Conference on Robotics and Automation (ICRA). Washington, DC, USA. https://doi.org/10.1109/ROBOT.2002.1014739
ISO. (2025). ISO/TS 15066:2016 robots and robotic devices—Collaborative robots. https://www.iso.org/standard/62996.html
Javaid, M., Haleem, A., Singh, R., Rab, S., & Suman, R. (2022). Significant applications of Cobots in the field of manufacturing, 2, 222–233. https://doi.org/10.1016/j.cogr.2022.10.001 DOI: https://doi.org/10.1016/j.cogr.2022.10.001
Kelly, J., & Sukhatme, G. (2011). Visual-inertial sensor fusion: Localization, mapping and sensor-to-sensor self-calibration. The International Journal of Robotics Research, 30(1), 56–79. https://doi.org/10.1177/0278364910382802 DOI: https://doi.org/10.1177/0278364910382802
Kober, J., Bagnell, J., & Peters, J. (2001). A historical perspective of robotics toward the future. Journal of Robotics and Mechatronics, 13(3), 299–313. https://doi.org/10.20965/jrm.2001.p0299 DOI: https://doi.org/10.20965/jrm.2001.p0299
Kober, J., Bagnell, J., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), 1238–1274. https://doi.org/10.1177/0278364913495721 DOI: https://doi.org/10.1177/0278364913495721
Kofer, D., Bergner, C., Deuerlein, C., Schmidt-Vollus, R., & Heß, P. (2021). Human–robot-collaboration: Innovative processes, from research to series standard. Procedia CIRP, 97, 98–103. https://doi.org/10.1016/j.procir.2020.09.185 DOI: https://doi.org/10.1016/j.procir.2020.09.185
Koenig, N., & Howard, A. (2004). Design and use paradigms for Gazebo, an open-source multi-robot simulator. IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendai, Japan. https://doi.org/10.1109/IROS.2004.1389727
Li, P., An, Z., Abrar, S., & Zhou, L. (2025). Large language models for multi-robot systems: A survey. arXiv. https://arxiv.org/pdf/2502.03814v1
Liu, Z., Liu, Q., Xu, W., Wang, L., & Zhou, Z. (2022). Robot learning towards smart robotic manufacturing: A review. Robotics and Computer-Integrated Manufacturing, 77, 102360. https://doi.org/10.1016/j.rcim.2022.102360 DOI: https://doi.org/10.1016/j.rcim.2022.102360
Lynch, K., & Park, F. C. (2017). Modern robotics: Mechanics, planning, and control. Cambridge University Press. https://api.semanticscholar.org/CorpusID:69542521 DOI: https://doi.org/10.1017/9781316661239
Macchelli, A., & Melchiorri, C. (2002). A real-time control system for industrial robots and control applications based on real-time Linux. IFAC Proceedings Volumes, 3(1), 55–60. https://doi.org/10.3182/20020721-6-ES-1901.00821 DOI: https://doi.org/10.3182/20020721-6-ES-1901.00821
Macenski, S., Foote, T., Gerkey, B., Lalancette, C., & Woodall, W. (2022). Robot operating system 2: Design, architecture, and uses in the wild. Science Robotics, 7(66), eabm607. https://doi.org/10.1126/scirobotics.abm607 DOI: https://doi.org/10.1126/scirobotics.abm6074
Macenski, S., Moore, T., Lu, D., Merzlyakov, A. y Ferguson, M. (2023). From the desks of ROS maintainers: A survey of modern & capable mobile robotics algorithms in the robot operating system 2. Robotics and Autonomous Systems, 168, 104493. https://doi.org/10.1016/j.robot.2023.104493 DOI: https://doi.org/10.1016/j.robot.2023.104493
Merlet, J. P. (2006). Parallel robots. Springer.
Newcombe, R., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., & Davison, A. (2011). KinectFusion: Real-time dense surface mapping and tracking. 10th IEEE International Symposium on Mixed and Augmented Reality. Basel, Switzerland. https://doi.org/10.1109/ISMAR.2011.6092378 DOI: https://doi.org/10.1109/ISMAR.2011.6162880
Peng, X., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018). Sim-to-real transfer of robotic control policies. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Brisbane, QLD, Australia. https://doi.org/10.1109/IROS.2018.8460528 DOI: https://doi.org/10.1109/ICRA.2018.8460528
Pratt, G., & Manzo, J. (2013). The DARPA Robotics Challenge [Competitions]. IEEE Robotics & Automation Magazine, 20(2), 7–12. https://doi.org/10.1109/MRA.2013.2255424 DOI: https://doi.org/10.1109/MRA.2013.2255424
Pratt, G., & Williamson, M. (1995). Series elastic actuators. Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1, 399–406. Pittsburgh, PA, USA. https://doi.org/10.1109/IROS.1995.525827 DOI: https://doi.org/10.1109/IROS.1995.525827
Quigley, M., Conley, K., Gerkey, B., & Faust, J. (2009). ROS: An open-source robot operating system. ICRA Workshop on Open Source Software, 3(3.2), 5. http://lars.mec.ua.pt/public/LAR%20Projects/BinPicking/2016_RodrigoSalgueiro/LIB/ROS/icraoss09-ROS.pdf
Rankin, A., Maimone, M., Biesiadecki, J., Patel, N., Levine, D., & Toupet, O. (2021). Mars curiosity rover mobility trends during the first 7 years. Journal of Field Robotics, 38(5), 759–800. https://doi.org/10.1002/rob.22011 DOI: https://doi.org/10.1002/rob.22011
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv. https://doi.org/10.48550/arXiv.1707.06347
Schaal, S. (1999). Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences, 3(6), 233–242. https://doi.org/10.1016/S1364-6613(99)01327-3 DOI: https://doi.org/10.1016/S1364-6613(99)01327-3
Sentis, L., & Khatib, O. (2005). Synthesis of whole-body behaviors through hierarchical control of behavioral primitives. International Journal of Humanoid Robotics, 2(4), 505–518. https://doi.org/10.1142/S0219843605000594 DOI: https://doi.org/10.1142/S0219843605000594
Siciliano, B., & Khatib, O. (Eds.). (2016). Springer handbook of robotics. Springer. https://doi.org/10.1007/978-3-319-32552-1 DOI: https://doi.org/10.1007/978-3-319-32552-1
Srinivas, S., Ramachandiran, S., & Rajendran, S. (2022). Autonomous robot-driven deliveries: A review of recent developments and future directions. Transportation Research Part E: Logistics and Transportation Review, 165, 102834. https://doi.org/10.1016/j.tre.2022.102834 DOI: https://doi.org/10.1016/j.tre.2022.102834
Spong, M., Hutchinson, S., & Vidyasagar, M. (2019). Robot modeling and control. Wiley.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. https://mitpress.mit.edu/9780262039246/reinforcement-learning/
Sucan, I., Moll, M., & Kavraki, L. (2012). The open motion planning library. IEEE Robotics & Automation Magazine, 19(4), 72–82. https://doi.org/10.1109/MRA.2012.2205651 DOI: https://doi.org/10.1109/MRA.2012.2205651
Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. MIT Press. https://mitpress.mit.edu/9780262201629/probabilistic-robotics/
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017). Domain randomization for transferring deep neural networks from simulation to the real world. arXiv. https://doi.org/10.48550/arXiv.1703.06907 DOI: https://doi.org/10.1109/IROS.2017.8202133
Tomo, J., Domjan, M., & Orehovacki, T. (2024). Intelligent robotics—A systematic review of emerging technologies and trends. Electronics, 13(3), 542. https://doi.org/10.3390/electronics13030542 DOI: https://doi.org/10.3390/electronics13030542
Van den Berg, J., Snoeyink, J., Lin, M., & Manocha, D. (2009). Centralized path planning for multiple robots: Optimal decoupling into sequential plans. Robotics: Science and Systems V, University of Washington. Seattle, USA. https://doi.org/10.15607/RSS.2009.V.018 DOI: https://doi.org/10.15607/RSS.2009.V.018
Vukobratovic, M., & Stepanenko, J. (1972). On the stability of anthropomorphic systems. Mathematical Biosciences, 15(1), 1–37. https://doi.org/10.1016/0025-5564(72)90061-2 DOI: https://doi.org/10.1016/0025-5564(72)90061-2
Zhang, D., Wang, J., Jing, Y., & Shen, A. (2024). The impact of robotics on STEM education: Facilitating cognitive and interdisciplinary advancements. Applied and Computational Engineering, 69(1), 7–12. https://doi.org/10.54254/2755-2721/69/20241433 DOI: https://doi.org/10.54254/2755-2721/69/20241433
Zhou, Z., Song, J., Xie, X., Shu, Z., Ma, L., Liu, D., Yin, J., & See, S. (2024). Towards building AI-CPS with NVIDIA Isaac Sim: An industrial benchmark and case study for robotics manipulation. ICSE-SEIP '24: Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice, 263–274. https://doi.org/10.1145/3639477.3639740 DOI: https://doi.org/10.1145/3639477.3639740
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Yuri Merizalde Zamora, Moroslav Alulema Cuestab, Tyron Alcivar Reynac, Joel Barba Salazard

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


