Cinemática inversa rápida de robots blandos utilizando el algoritmo TRR
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12253Palabras clave:
Tecnología robótica, Robots manipuladores, Robótica y mecatrónica, Mecatrónica, Metodologías de diseño, Redes neuronalesResumen
A la hora de abordar el modelo cinemático inverso de robots blandos, los algoritmos de optimización son la mejor opción a nivel de precisión, facilidad de implementación y manejo de restricciones, pero no suelen lograr soluciones en tiempos suficientemente reducidos. Este artículo propone la utilización del algoritmo TRR (Trust Region Reflective), metodología de minimización basada en aproximar la función objetivo en regiones de tamaño variable. El desarrollo se prueba sobre un simulador del robot PETER y, posteriormente, sobre el propio manipulador, comparándose en ambos casos con una red neuronal entrenada para modelar la cinemática inversa del robot. Los errores promedio obtenidos en ambas situaciones son de 0.22 mm y 14 mm para el TRR y de 1.27 mm y 15 mm para la red. Aunque la precisión de las pruebas sobre el robot real tiene aun margen de recorrido, se demuestra como el algoritmo presentado es capaz de ofrecer soluciones precisas en bajos tiempos de ejecución.
Referencias
Aristidou, A., Lasenby, J., 2011. FABRIK: A fast, iterative solver for the Inverse Kinematics problem. Graphical Models 73, 243–260. URL: http://dx.doi.org/10.1016/j.gmod.2011.05.003, doi:10.1016/j.gmod.2011.05.003.
Bern, J.M., Rus, D., 2021. Soft IK with stiffness control. 2021 IEEE 4th International Conference on Soft Robotics, RoboSoft 2021 , 465-471 doi:10.1109/RoboSoft51838.2021.9479195.
Bern, J.M., Schnider, Y., Banzet, P., Kumar, N., Coros, S., 2020. Soft Robot Control with a Learned Differentiable Model. 2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020 , 417-423 doi:10.1109/RoboSoft48309.2020.9116011.
Bhalkikar, A., Lokesh, S., Ashwin, K.P., 2024. Kinematic models for Cabledriven Continuum Robots with multiple segments and varying cable offsets. Mechanism and Machine Theory 200, 105701.
Bianchi, D., Campinoti, G., Comitini, C., Laschi, C., Rizzo, A., Sabatini, A.M., Falotico, E., 2024. SoftSling: A Soft Robotic Arm Control Strategy to Throw Objects with Circular Run-ups. IEEE Robotics and Automation Letters PP, 1–8. doi:10.1109/LRA.2024.3442535.
Blanco, K., Navas, E., Rodriguez-Nieto, D., Emmi, L., Fernandez, R., 2024. Soft Bellow-Based 3D Printed Robot for in-Pipe Inspection Applications. 2024 7th Iberian Robotics Conference, ROBOT 2024 doi:10.1109/ROBOT61475.2024.10797416.
Branch, M.A., Coleman, T.F., Li, Y., 1999. A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems. SIAM Journal on Scientific Computing 21, 1–23. URL: https://doi.org/10.1137/S1064827595289108, doi:10.1137/S1064827595289108.
Chiang, S.S., Yang, H., Skorina, E., Onal, C.D., 2021. SLInKi: State Lattice based Inverse Kinematics - A Fast, Accurate, and Flexible IK Solver for Soft Continuum Robot Manipulators, in: IEEE International Conference on Automation Science and Engineering, IEEE. pp. 1871–1877. doi:10.1109/CASE49439.2021.9551686.
García-Samartín, J.F., del Cerro, J., Barrientos, A., 2024a. Inverse kinematic modelling with shape control of a soft robot using genetic algorithms, in: Jornadas de Automática. URL: https://doi.org/10.17979/ja-cea.2024.45.10968, doi:10.17979/ja-cea.2024.45.10968.
García-Samartín, J.F., Charles, M., del Cerro, J., Barrientos, A., 2024b. PETER : a Soft Pneumatic Manipulator with High Resistance and Load Capacity, in: 2024 7th Iberian Robotics Conference (ROBOT), Madrid. pp. 1–6. doi:10.1109/ROBOT61475.2024.10796857.
García-Samartín, J.F., Molina-Gómez, R., Barrientos, A., 2024c. Model-Free Control of a Soft Pneumatic Segment. Biomimetics 9. doi:https://doi.org/10.3390/biomimetics9030127.
García-Samartín, J.F., Rieker, A., Barrientos, A., 2024d. Design, Manufacturing, and Open-Loop Control of a Soft Pneumatic Arm. Actuators 13. URL: https://www.mdpi.com/2076-0825/13/1/36, doi:10.3390/act13010036.
Kaviri, M., Fesharaki, A.J., Sadeghnejad, S., 2023. Soft robotics in medical applications: State of the art, challenges, and recent advances, in: Boubaker, O. (Ed.), Medical and Healthcare Robotics. Academic Press. Medical Robots and Devices: New Developments and Advances. chapter 2, pp. 25–61. URL: https://www.sciencedirect.com/science/article/pii/B9780443184604000093, doi:https://doi.org/10.1016/B978-0-443-18460-4.00009-3.
Keyvanara, M., Goshtasbi, A., Kuling, I.A., 2023. A Geometric Approach towards Inverse Kinematics of Soft Extensible Pneumatic Actuators Intended for Trajectory Tracking. Sensors 23, 1–16. doi:10.3390/s23156882.
Liu, J., Song, Z., Lu, Y., Yang, H., Chen, X., Duo, Y., Chen, B., Kong, S., Shao, Z., Gong, Z., Wang, S., Ding, X., Yu, J., Wen, L., 2024. An Underwater Robotic System with a Soft Continuum Manipulator for Autonomous Aquatic Grasping. IEEE/ASME Transactions on Mechatronics 29, 1007–1018. doi:10.1109/TMECH.2023.3321054.
Martín, A., Barrientos, A., Del Cerro, J., 2018. The natural-CCD algorithm, a novel method to solve the inverse kinematics of hyper-redundant and soft robots. Soft Robotics 5, 242–257. doi:10.1089/soro.2017.0009.
Navas, E., Fernández, R., Sepúlveda, D., Armada, M., Gonzalez-De-santos, P., 2021. Soft grippers for automatic crop harvesting: A review. Sensors 21. doi:10.3390/s21082689.
Seleem, I.A., El-Hussieny, H., Ishii, H., 2023. Imitation-based Path Planning and Nonlinear Model Predictive Control of a Multi-Section Continuum Robots. Journal of Intelligent and Robotic Systems: Theory and Applications 108. doi:10.1007/s10846-023-01811-8.
Seyidoğlu, B., Rafsanjani, A., 2024. A textile origami snake robot for rectilinear locomotion. Device 2. doi:10.1016/j.device.2023.100226.
Shan, Y., Zhao, Y.,Wang, H., Dong, L., Pei, C., Jin, Z., Sun, Y., Liu, T., 2024. Variable stiffness soft robotic gripper: design, development, and prospects. Bioinspiration and Biomimetics 1. doi:10.1088/1748-3190/ad0b8c.
Sun, W., Akashi, N., Kuniyoshi, Y., Nakajima, K., 2022. Physics-Informed Recurrent Neural Networks for Soft Pneumatic Actuators. IEEE Robotics and Automation Letters 7, 6862–6869. doi:10.1109/LRA.2022.3178496.
Terrile, S., Miguelañez, J., Barrientos, A., 2021. A Soft Haptic Glove Actuated with Shape Memory Alloy and Flexible Stretch Sensors. Sensors 21. doi:10.3390/s21165278.
Thuruthel, T.G., Falotico, E., Renda, F., Laschi, C., 2019. Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators. IEEE Transactions on Robotics 35, 127–134. doi:10.1109/TRO.2018.2878318.
Wagaa, N., Kallel, H., Mellouli, N., 2023. Analytical and deep learning approaches for solving the inverse kinematic problem of a high degrees of freedom robotic arm. Engineering Applications of Artificial Intelligence 123, 106301. URL: https://doi.org/10.1016/j.engappai.2023.106301, doi:10.1016/j.engappai.2023.106301.
Wan, Z., Sun, Y., Qin, Y., Skorina, E.H., Gasoto, R., Luo, M., Fu, J., Onal, C.D., 2023. Design, Analysis, and Real-Time Simulation of a 3D Soft Robotic Snake. Soft Robotics 10, 258–268. doi:10.1089/soro.2021.0144.
Wu, M., Zhang, Y., Wu, X., Li, Z., Chen, W., Hao, L., 2023. Reviewof Modeling and Control Methods of Soft Robots Based on Machine Learning. Chinese Control Conference, CCC 2023-July, 4318–4323. doi:10.23919/CCC58697.2023.10240787.
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Derechos de autor 2025 Jorge Francisco García Samartín, Mar Martín-Díaz, Jaime del Cerro, Antonio Barrientos
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
