Modelado cinemático inverso con control de forma de un robot blando mediante algoritmos genéticos
Barra lateral del artículo
Contenido principal del artículo
Resumen
Uno de los principales problemas que está encontrando la robótica blanda y, en parte, frenando su auge, es la dificultad para modelar con precisión la cinemática inversa de estos manipuladores. Su carácter redundante hace compleja esta tarea y, en multitud de ocasiones, las técnicas de aprendizaje automático precisan de un número de muestras difícilmente alcanzable. Se presenta aquí un algoritmo genético que, a partir del modelo cinemático directo, fácilmente obtenible, logra notables resultados, tanto en términos de precisión como de tiempo. En concreto se ha conseguido, al aplicarlo sobre un robot neumático modular, un error de 0.9 mm con tiempos de ejecución de 12 s. La metodología desarrollada permite, además, gestionar las redundancias y elegir la pose que se desea que el robot adopte, pudiendo recibir como entrada, además de las coordenadas del extremo, la posición deseada de cuantos módulos intermedios se precise. Esto abre la puerta a posibles aplicaciones de interés, como la teleoperación de manipuladores blandos mediante realidad virtual.
Palabras clave:
Detalles del artículo
Citas
Alkhodary, A., Gur, B., 2022. Kinematics Transformer : Solving The Inverse Modeling Problem of Soft Robots using Transformers, unpublished.
Almanzor, E., Ye, F., Shi, J., Thuruthel, T. G., Wurdemann, H. A., Iida, F., 2023. Static Shape Control of Soft Continuum Robots Using Deep Visual Inverse Kinematic Models. IEEE Transactions on Robotics 39 (4), 2973–2988. DOI: 10.1109/TRO.2023.3275375 DOI: https://doi.org/10.1109/TRO.2023.3275375
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 DOI: https://doi.org/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 DOI: https://doi.org/10.1109/RoboSoft48309.2020.9116011
Bhagat, S., Banerjee, H., Tse, Z. T. H., Ren, H., 2019. Deep reinforcement learning for soft, flexible robots: Brief review with impending challenges. Robotics 8 (1), 1–36. DOI: 10.3390/robotics8010004 DOI: https://doi.org/10.3390/robotics8010004
Centurelli, A., Arleo, L., Rizzo, A., Tolu, S., Laschi, C., Falotico, E., 2022. Closed-Loop Dynamic Control of a Soft Manipulator Using Deep Reinforcement Learning. IEEE Robotics and Automation Letters 7 (2), 4741–
DOI: 10.1109/LRA.2022.3146903 DOI: https://doi.org/10.1109/LRA.2022.3146903
Cerrillo, D., Barrientos, A., Del Cerro, J., 2022. Kinematic Modelling for Hyper-Redundant Robots—A Structured Guide. Mathematics 10 (16). DOI: 10.3390/math10162891 DOI: https://doi.org/10.3390/math10162891
Chi, Y., Zhao, Y., Hong, Y., Li, Y., Yin, J., 2024. A Perspective on Miniature Soft Robotics: Actuation, Fabrication, Control, and Applications. Advanced Intelligent Systems 6 (2). DOI: 10.1002/aisy.202300063 DOI: https://doi.org/10.1002/aisy.202300063
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 DOI: https://doi.org/10.1109/CASE49439.2021.9551686
Cianchetti, M., Laschi, C., Menciassi, A., Dario, P., 2018. Biomedical applications of soft robotics. Nature Reviews Materials 3 (6), 143–153. DOI: 10.1038/s41578-018-0022-y DOI: https://doi.org/10.1038/s41578-018-0022-y
Fang, G., Tian, Y., Yang, Z. X., Geraedts, J. M., Wang, C. C., 2022. Efficient Jacobian-Based Inverse KinematicsWith Sim-to-Real Transfer of Soft Robots by Learning. IEEE/ASME Transactions on Mechatronics, 1–11. DOI: 10.1109/TMECH.2022.3178303 DOI: https://doi.org/10.1109/TMECH.2022.3178303
García-Samartín, J. F., Barrientos, A., 2023. Kinematic Modelling of a 3RRR Planar Parallel Robot Using Genetic Algorithms and Neural Networks. Machines 11 (10), 1–26. DOI: 10.3390/machines11100952 DOI: https://doi.org/10.3390/machines11100952
García-Samartín, J. F., Molina-Gómez, R., Barrientos, A., 2024a. Model-Free Control of a Soft Pneumatic Segment. Biomimetics 9 (127). DOI: https://doi.org/10.3390/ biomimetics9030127ín, J. F., Rieker, A., Barrientos, A., 2024b. Design, Manufacturing, and Open-Loop Control of a Soft Pneumatic Arm. Actuators 13 (1). DOI: 10.3390/act13010036 DOI: https://doi.org/10.3390/act13010036
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 (15), 1–16. DOI: 10.3390/s23156882 DOI: https://doi.org/10.3390/s23156882
Lee, C.-T., Chang, J.-Y. J., 2021. A Workspace-Analysis-Based Genetic Algorithm for Solving Inverse Kinematics of a Multi-Fingered Anthropomorphic Hand. Applied Sciences 11 (6). DOI: 10.3390/app11062668 DOI: https://doi.org/10.3390/app11062668
Li, G., Wong, T. W., Shih, B., Guo, C., Wang, L., Liu, J., Wang, T., Liu, X., Yan, J.,Wu, B., Yu, F., Chen, Y., Liang, Y., Xue, Y.,Wang, C., He, S.,Wen, L., Tolley, M. T., Zhang, A. M., Laschi, C., Li, T., 2023. Bioinspired soft robots for deep-sea exploration. Nature Communications 14 (1), 1–10. DOI: 10.1038/s41467-023-42882-3 DOI: https://doi.org/10.1038/s41467-023-42882-3
Liu, H., Liu, M., Jiang, Y., Zhang, X., 2023. Research on obstacle avoidance planning of soft robotic arm based on the idea of cutting-edge growth. In: 2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC). IEEE, pp. 35–40. DOI: 10.1109/RAIIC59453.2023.10281195 DOI: https://doi.org/10.1109/RAIIC59453.2023.10281195
Manti, M., Pratesi, A., Falotico, E., Cianchetti, M., Laschi, C., 2016. Soft assistive robot for personal care of elderly people. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and
Biomechatronics 2016-July, 833–838. DOI: 10.1109/BIOROB.2016.7523731 DOI: https://doi.org/10.1109/BIOROB.2016.7523731
Montenegro-Bravo, J. S., Ruiz-Fl´orez, J. D., Romero-Ante, J. D., Manrique-C´ordoba, J., Vivas Alb´an, O. A., Sabater-Navarro, J. M., jul. 2023. Generador 3d de trayectorias libres de colisiones para un manipulador ur3e con pinza blanda. Revista Iberoamericana de Autom´atica e Inform´atica industrial 21 (1), 52–61. DOI: 10.4995/riai.2023.19332 DOI: https://doi.org/10.4995/riai.2023.19332
Nazeer, M. S., Laschi, C., Falotico, E., 2024. RL-based Adaptive Controller for High Precision Reaching in a Soft Robot Arm. IEEE Transactions on Robotics 40, 2498–2512. DOI: 10.1109/TRO.2024.3381558 DOI: https://doi.org/10.1109/TRO.2024.3381558
Nguyen, C. C., Thai, M. T., Hoang, T. T., Davies, J., Phan, P. T., Zhu, K., Wu, L., Brodie, M. A., Tsai, D., Ha, Q. P., Phan, H. P., Lovell, N. H., Nho Do, T., 2023. Development of a soft robotic catheter for vascular intervention
surgery. Sensors and Actuators A: Physical 357 (November 2022), 114380. DOI: 10.1016/j.sna.2023.114380 DOI: https://doi.org/10.1016/j.sna.2023.114380
Terrile, S., Arg¨uelles, M., Barrientos, A., 2021. Comparison of Different Technologies for Soft Robotics Grippers. Sensors 21 (9). DOI: 10.3390/s21093253 DOI: https://doi.org/10.3390/s21093253
Trivedi, D., Rahn, C. D., Kier, W. M., Walker, I. D., 2008. Soft robotics: Biological inspiration, state of the art, and future research. Applied Bionics and Biomechanics 5 (3), 99–117. DOI: 10.1080/11762320802557865 DOI: https://doi.org/10.1155/2008/520417
Wang, J., Zhao, Y., Zhang, X., Li, Z., Yao, W., 2024. Model-Free Intelligent Control for Space Soft Robotic Manipulators. Space: Science & Technology 4, 1–13. DOI: 10.34133/space.0120 DOI: https://doi.org/10.34133/space.0120
Wang, P., Tang, Z., Xin, W., Xie, Z., Guo, S., Laschi, C., 2022. Design and Experimental Characterization of a Push-Pull Flexible Rod-Driven Soft- Bodied Robot. IEEE Robotics and Automation Letters 7 (4), 1–8. DOI: https://doi.org/10.1109/LRA.2022.3189435
Yang, C., Xu, H., Li, X., Yu, F., 2022. Kinematic modeling and solution of rigid-flexible and variable-diameter underwater continuous manipulator with load. Robotica 10, 1020–1035. DOI: 10.1017/S0263574721000989 DOI: https://doi.org/10.1017/S0263574721000989
Zhang, Z., Wang, S., Meng, D., Wang, X., Liang, B., 2021. Soft-CCD Algorithm for Inverse Kinematics of Soft Continuum Manipulators. IEEE International Conference on Intelligent Robots and Systems, 639–644. DOI: 10.1109/IROS51168.2021.9635921 DOI: https://doi.org/10.1109/IROS51168.2021.9635921