Nuevo enfoque en la navegación de robots mediante Matsuoka CPG

Nuestra propuesta introduce una aplicación novedosa del algoritmo Generador de Patrones Centrales (CPG) de Matsuoka en la robótica bioinspirada, centrada en la locomoción acuática. Adaptando el algoritmo al modelo de Matsuoka, facilitamos su conversión de forma continua a discreta, asegurando su com...

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Autores:: Loyola, Oscar
Ortiz Cuadros, Jose David
Reyes Bozo, Lorenzo
Vidal Rojas, Juan Carlos

Tipo de recurso:: Article of journal

Fecha de publicación:: 2024

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: spa

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network_acronym_str	RCUC2
network_name_str	REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG
dc.title.translated.eng.fl_str_mv	New approach in robot navigation trough Matsuoka CPG neuron
title	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG
spellingShingle	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG CPG Bio-inspired Robotics Nonlinear Control Matsuoka Oscillator CPG Bio-inspired Robotics Nonlinear control Matsuoka Oscillator
title_short	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG
title_full	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG
title_fullStr	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG
title_full_unstemmed	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG
title_sort	Nuevo enfoque en la navegación de robots mediante Matsuoka CPG
dc.creator.fl_str_mv	Loyola, Oscar Ortiz Cuadros, Jose David Reyes Bozo, Lorenzo Vidal Rojas, Juan Carlos
dc.contributor.author.spa.fl_str_mv	Loyola, Oscar Ortiz Cuadros, Jose David Reyes Bozo, Lorenzo Vidal Rojas, Juan Carlos
dc.subject.eng.fl_str_mv	CPG Bio-inspired Robotics Nonlinear Control Matsuoka Oscillator
topic	CPG Bio-inspired Robotics Nonlinear Control Matsuoka Oscillator CPG Bio-inspired Robotics Nonlinear control Matsuoka Oscillator
dc.subject.spa.fl_str_mv	CPG Bio-inspired Robotics Nonlinear control Matsuoka Oscillator
description	Nuestra propuesta introduce una aplicación novedosa del algoritmo Generador de Patrones Centrales (CPG) de Matsuoka en la robótica bioinspirada, centrada en la locomoción acuática. Adaptando el algoritmo al modelo de Matsuoka, facilitamos su conversión de forma continua a discreta, asegurando su compatibilidad con procesadores digitales para el control robótico en tiempo real. El método demuestra una mejora considerable, con una tasa de rendimiento aproximadamente un 50% superior a la alcanzada con algoritmos tradicionales como la Modulación de Ancho de Pulso (PWM). El refinamiento del algoritmo de control en sistemas robóticos bioinspirados permite una replicación de movimientos similares a los de los peces más cercana a los comportamientos naturales. Este artículo explora en detalle la arquitectura del algoritmo CPG modificado para satisfacer los requisitos de procesamiento discreto sin perder la fluidez del movimiento biomimético. Presentamos evidencia empírica de la superioridad de nuestro algoritmo sobre las estrategias de control convencionales, examinando la capacidad mejorada del robot para imitar patrones de natación precisos y eficientes. Significativamente, la aplicación de este algoritmo reduce los errores temporales y proporciona una mejora notable en el rendimiento robótico bajo el agua. Nuestros hallazgos señalan una dirección prometedora para la investigación futura y la aplicación en el campo de la natación robótica, sugiriendo un cambio de paradigma en el enfoque del movimiento robótico inspirado en sistemas biológicos.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-10-21 19:36:40 2024-12-13T08:30:14Z
dc.date.available.none.fl_str_mv	2024-10-21 19:36:40 2024-12-13T08:30:14Z
dc.date.issued.none.fl_str_mv	2024-10-21
dc.type.spa.fl_str_mv	Artículo de revista
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dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.ispartofjournal.spa.fl_str_mv	Inge CuC
dc.relation.references.spa.fl_str_mv	Ay, Mustafa and Korkmaz, Deniz and Ozmen Koca, Gonca and Bal, Cafer and Akpolat, Zuhtu Hakan and Bingol, Mustafa Can. Mechatronic design and manufacturing of the intelligent robotic fish for bio-inspired swimming modes. Electronics, 2018, 7(7), 118, doi:10.3390/electronics7070118. 2. Bal, Cafer and Koca, Gonca Ozmen and Korkmaz, Deniz and Akpolat, Zuhtu Hakan and Ay, Mustafa. CPG-based autonomous swimming control for multi-tasks of a biomimetic robotic fish. Ocean Engineering, 2019, 189, https://doi.org/10.1016/j.oceaneng.2019.106334. 3. Cao, Yong and Lu, Yang and Cai, Yueri and Bi, Shusheng and Pan, Guang. CPG-fuzzy-based control of a cownose-ray-like fish robot. Industrial Robot: the international journal of robotics research and application, 2019, 779-791, DOI 10.1108/IR-02-2019-0029. 4. Dipankar Bhattacharya, Leo K. Cheng, Steven Dirven and Weiliang Xu. Artificial Intelligence Approach to the Trajectory Generation and Dynamics of a Soft Robotic Swallowing Simulator. Advances in Intelligent Systems and Computing, 2019, 751, 3-16, https://doi.org/10.1007/978-3-319-78452-6_1. 5. Duraisamy, Palmani and Sidharthan, Rakesh Kumar and Santhanakrishnan, Manigandan Nagarajan. Design, modeling, and control of biomimetic fish robot: a review. Journal of Bionic Engineering, 2019, 16, 967-993, DOI: https://doi.org/10.1007/s42235-019-0111-7. 6. Fang, FC and Xu, WL and Lin, KC and Alam, Fakhrul and Potgieter, Johan. Matsuoka neuronal oscillator for traffic signal control using agent-based simulation. Procedia Computer Science, 2013,19,389-395, doi: 10.1016/j.procs.2013.06.053. 7. Garcia-Saura, Carlos. Central pattern generators for the control of robotic systems. arXiv preprint arXiv:1509.02417, 2015. 8. Grillner, Sten. Biological pattern generation: the cellular and computational logic of networks in motion. Neuron, 2006, 52(5), 751-766. DOI 10.1016/j.neuron.2006.11.008. 9. Lee, Chiwon and Kim, Myungjoon and Kim, Yoon Jae and Hong, Nhayoung and Ryu, Seungwan and Kim, H Jin and Kim, Sungwan. Soft robot review. International Journal of Control, Automation and Systems, 2017, 15(1), 3-15, http://dx.doi.org/10.1007/s12555-016-0462-3. 10. Liu, Xuan and Gasoto, Renato and Jiang, Ziyi and Onal, Cagdas and Fu, Jie. Learning to locomote with artificial neural-network and cpg-based control in a soft snake robot. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, 7758-7765. 11. Lu, Qiang and Zhang, Zhaochen and Yue, Chao. The programmable CPG model based on Matsuoka oscillator and its application to robot locomotion. International Journal of Modeling, Simulation, and Scientific Computing, 2020, 11(3), 2050018. DOI: 10.1142/S179396232050018X. 12. Matsuoka, Kiyotoshi. Analysis of a neural oscillator. Biological cybernetics, 2011, 104(4), 297-304. 13. Matsuoka, Kiyotoshi. Mechanisms of frequency and pattern control in the neural rhythm generators. Biological cybernetics, 1987, 56(5), 345-353. 14. Matsuoka, Kiyotoshi. Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological cybernetics, 1985, 52(6), 367-376. 15. Ozmen Koca, Gonca and Bal, Cafer and Korkmaz, Deniz and Bingol, Mustafa Can and Ay, Mustafa and Akpolat, Zuhtu Hakan and Yetkin, Seda. Three-dimensional modeling of a robotic fish based on real carp locomotion. Applied Sciences, 2018, 8(2), 180. doi:10.3390/app8020180. 16. Özdemi̇r, Mahmut Temel and Öztürk, Dursun. Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control. Energies, 2017, 10(12), 2134. DOI: 10.3390/en10122134. 17. Scaradozzi, David and Palmieri, Giacomo and Costa, Daniele and Pinelli, Antonio. BCF swimming locomotion for autonomous underwater robots: a review and a novel solution to improve control and efficiency. Ocean Engineering, 2017, 130, 437-453. DOI: http://dx.doi.org/10.1016/j.oceaneng.2016.11.055. 18. Soni, Yogendra Kumar and Bhatt, Rajesh. BF-PSO optimized PID controller design using ISE, IAE, IATE and MSE error criteria. International Journal of Advanced Research in Computer Engineering \& Technology (IJARCET), 2013, 2(7), 2333-2336. 19. Wang, Ming and Yu, JunZhi and Tan, Min and Zhang, JianWei. Multimodal swimming control of a robotic fish with pectoral fins using a CPG network. Chinese science bulletin, 2012, 57(10), 1209-1216. DOI: 10.1007/s11434-012-5005-6. 20. Wang, Wei and Dai, Xia and Li, Liang and Gheneti, Banti H and Ding, Yang and Yu, Junzhi and Xie, Guangming. Three-dimensional modeling of a fin-actuated robotic fish with multimodal swimming. IEEE/ASME Transactions on Mechatronics, 2018, 23(4), 1641-1652. DOI: 10.1109/TMECH.2018.2848220. 21. Wang, Wei and Gu, Dongbing and Xie, Guangming. Autonomous optimization of swimming gait in a fish robot with multiple onboard sensors. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 49(5), 891-903. DOI: 10.1109/TSMC.2017.2683524. 22. Wang, Yong and Xue, Xihui and Chen, Baifan. Matsuoka’s CPG with desired rhythmic signals for adaptive walking of humanoid robots. IEEE transactions on cybernetics, 2018, 50(2),613-626. 23. Xie, Fengran and Du, Ruxu. Central pattern generator control for a biomimetic robot fish in maneuvering. 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018, 268-273. 24. Xie, Fengran and Zhong, Yong and Du, Ruxu and Li, Zheng. Central pattern generator (CPG) control of a biomimetic robot fish for multimodal swimming. Journal of Bionic Engineering, 2019, 16(2), 222-234, DOI: https://doi.org/10.1007/s42235-019-0019-2. 25. Xu, WL and Fang, F Clara and Bronlund, J and Potgieter, J. Generation of rhythmic and voluntary patterns of mastication using Matsuoka oscillator for a humanoid chewing robot. Mechatronics, 2009, 19(2), 205-217, doi:10.1016/j.mechatronics.2008.08.003. 26. Yen, Wei-Kuo and Sierra, Daniel Martinez and Guo, Jenhwa. Controlling a robotic fish to swim along a wall using hydrodynamic pressure feedback. IEEE Journal of Oceanic Engineering, 2018, 43(2), 369-380. DOI: 10.1109/JOE.2017.2785698. 27. Yu, Junzhi and Chen, Shifeng and Wu, Zhengxing and Chen, Xingyu and Wang, Ming. Energy analysis of a CPG-controlled miniature robotic fish. Journal of Bionic Engineering, 2018, 15(2), 260-269, DOI: https://doi.org/10.1007/s42235-018-0020-1. 28. Yu, Junzhi and Tan, Min and Chen, Jian and Zhang, Jianwei. A survey on CPG-inspired control models and system implementation. IEEE transactions on neural networks and learning systems, 2013, 25(3), 441-456. DOI: 10.1109/TNNLS.2013.2280596. 29. Yu, Junzhi and Wang, Ming and Dong, Huifang and Zhang, Yanlu and Wu, Zhengxing. Motion control and motion coordination of bionic robotic fish: A review. Journal of Bionic Engineering, 2018, 15(4), 579-598. DOI: https://doi.org/10.1007/s42235-018-0048-2. 30. Yu, Junzhi and Wu, Zhengxing and Wang, Ming and Tan, Min. CPG network optimization for a biomimetic robotic fish via PSO. IEEE transactions on neural networks and learning systems, 2015, 27(9), 1962-1968. DOI: 10.1109/TNNLS.2015.2459913. 31. Zhang, Xue and Pan, Tianle and Heung, Ho Lam and Chiu, Philip Wai Yan and Li, Zheng. A biomimetic soft robot for inspecting pipeline with significant diameter variation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, 7486-7491. 32. Zhou, Chao and Tan, Min and Gu, Nong and Cao, Zhiqiang and Wang, Shuo and Wang, Long. The design and implementation of a biomimetic robot fish. International journal of advanced robotic systems, 2008, 5(2), 185-19
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spelling	Loyola, OscarOrtiz Cuadros, Jose DavidReyes Bozo, LorenzoVidal Rojas, Juan Carlos2024-10-21 19:36:402024-12-13T08:30:14Z2024-10-21 19:36:402024-12-13T08:30:14Z2024-10-210122-6517https://hdl.handle.net/11323/13906https://doi.org/10.17981/ingecuc.20.2.2024.0810.17981/ingecuc.20.2.2024.082382-4700Nuestra propuesta introduce una aplicación novedosa del algoritmo Generador de Patrones Centrales (CPG) de Matsuoka en la robótica bioinspirada, centrada en la locomoción acuática. Adaptando el algoritmo al modelo de Matsuoka, facilitamos su conversión de forma continua a discreta, asegurando su compatibilidad con procesadores digitales para el control robótico en tiempo real. El método demuestra una mejora considerable, con una tasa de rendimiento aproximadamente un 50% superior a la alcanzada con algoritmos tradicionales como la Modulación de Ancho de Pulso (PWM). El refinamiento del algoritmo de control en sistemas robóticos bioinspirados permite una replicación de movimientos similares a los de los peces más cercana a los comportamientos naturales. Este artículo explora en detalle la arquitectura del algoritmo CPG modificado para satisfacer los requisitos de procesamiento discreto sin perder la fluidez del movimiento biomimético. Presentamos evidencia empírica de la superioridad de nuestro algoritmo sobre las estrategias de control convencionales, examinando la capacidad mejorada del robot para imitar patrones de natación precisos y eficientes. Significativamente, la aplicación de este algoritmo reduce los errores temporales y proporciona una mejora notable en el rendimiento robótico bajo el agua. Nuestros hallazgos señalan una dirección prometedora para la investigación futura y la aplicación en el campo de la natación robótica, sugiriendo un cambio de paradigma en el enfoque del movimiento robótico inspirado en sistemas biológicos.Our proposal introduces a novel application of Matsuoka's Central Pattern Generator (CPG) algorithm to bio-inspired robotics, with an emphasis on aquatic locomotion. By adapting the algorithm based on Matsuoka's model, we enable its translation from continuous to discrete form, ensuring its compatibility with digital processors for real-time robotic control. The method demonstrates a remarkable improvement, exhibiting a performance rate approximately 50% higher than that achieved with traditional algorithms such as Pulse Width Modulation (PWM). The refinement of the control algorithm within bio-inspired robotic systems allows for a replication of fish-like movements more closely aligned with natural behaviors. This paper delves into the CPG algorithm's architecture, which has been modified to address discrete processing requirements without sacrificing the fluidity of biomimetic motion. We present empirical evidence showing our algorithm's superiority to conventional control strategies by examining the robot's enhanced capability to mimic accurate and efficient swimming patterns. Notably, the application of this algorithm reduces temporal errors and delivers a marked enhancement in underwater robotic performance. Our findings indicate a promising direction for future research and application in the field of robotic swimming, suggesting a paradigm shift in the approach to robotic movement inspired by biological systems.application/pdfspaUniversidad de la CostaInge CuC - 2024http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/5279CPGBio-inspiredRoboticsNonlinear ControlMatsuoka OscillatorCPGBio-inspiredRoboticsNonlinear controlMatsuoka OscillatorNuevo enfoque en la navegación de robots mediante Matsuoka CPGNew approach in robot navigation trough Matsuoka CPG neuronArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge CuCAy, Mustafa and Korkmaz, Deniz and Ozmen Koca, Gonca and Bal, Cafer and Akpolat, Zuhtu Hakan and Bingol, Mustafa Can. Mechatronic design and manufacturing of the intelligent robotic fish for bio-inspired swimming modes. Electronics, 2018, 7(7), 118, doi:10.3390/electronics7070118. 2. Bal, Cafer and Koca, Gonca Ozmen and Korkmaz, Deniz and Akpolat, Zuhtu Hakan and Ay, Mustafa. CPG-based autonomous swimming control for multi-tasks of a biomimetic robotic fish. Ocean Engineering, 2019, 189, https://doi.org/10.1016/j.oceaneng.2019.106334. 3. Cao, Yong and Lu, Yang and Cai, Yueri and Bi, Shusheng and Pan, Guang. CPG-fuzzy-based control of a cownose-ray-like fish robot. Industrial Robot: the international journal of robotics research and application, 2019, 779-791, DOI 10.1108/IR-02-2019-0029. 4. Dipankar Bhattacharya, Leo K. Cheng, Steven Dirven and Weiliang Xu. Artificial Intelligence Approach to the Trajectory Generation and Dynamics of a Soft Robotic Swallowing Simulator. Advances in Intelligent Systems and Computing, 2019, 751, 3-16, https://doi.org/10.1007/978-3-319-78452-6_1. 5. Duraisamy, Palmani and Sidharthan, Rakesh Kumar and Santhanakrishnan, Manigandan Nagarajan. Design, modeling, and control of biomimetic fish robot: a review. Journal of Bionic Engineering, 2019, 16, 967-993, DOI: https://doi.org/10.1007/s42235-019-0111-7. 6. Fang, FC and Xu, WL and Lin, KC and Alam, Fakhrul and Potgieter, Johan. Matsuoka neuronal oscillator for traffic signal control using agent-based simulation. Procedia Computer Science, 2013,19,389-395, doi: 10.1016/j.procs.2013.06.053. 7. Garcia-Saura, Carlos. Central pattern generators for the control of robotic systems. arXiv preprint arXiv:1509.02417, 2015. 8. Grillner, Sten. Biological pattern generation: the cellular and computational logic of networks in motion. Neuron, 2006, 52(5), 751-766. DOI 10.1016/j.neuron.2006.11.008. 9. Lee, Chiwon and Kim, Myungjoon and Kim, Yoon Jae and Hong, Nhayoung and Ryu, Seungwan and Kim, H Jin and Kim, Sungwan. Soft robot review. International Journal of Control, Automation and Systems, 2017, 15(1), 3-15, http://dx.doi.org/10.1007/s12555-016-0462-3. 10. Liu, Xuan and Gasoto, Renato and Jiang, Ziyi and Onal, Cagdas and Fu, Jie. Learning to locomote with artificial neural-network and cpg-based control in a soft snake robot. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, 7758-7765. 11. Lu, Qiang and Zhang, Zhaochen and Yue, Chao. The programmable CPG model based on Matsuoka oscillator and its application to robot locomotion. International Journal of Modeling, Simulation, and Scientific Computing, 2020, 11(3), 2050018. DOI: 10.1142/S179396232050018X. 12. Matsuoka, Kiyotoshi. Analysis of a neural oscillator. Biological cybernetics, 2011, 104(4), 297-304. 13. Matsuoka, Kiyotoshi. Mechanisms of frequency and pattern control in the neural rhythm generators. Biological cybernetics, 1987, 56(5), 345-353. 14. Matsuoka, Kiyotoshi. Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological cybernetics, 1985, 52(6), 367-376. 15. Ozmen Koca, Gonca and Bal, Cafer and Korkmaz, Deniz and Bingol, Mustafa Can and Ay, Mustafa and Akpolat, Zuhtu Hakan and Yetkin, Seda. Three-dimensional modeling of a robotic fish based on real carp locomotion. Applied Sciences, 2018, 8(2), 180. doi:10.3390/app8020180. 16. Özdemi̇r, Mahmut Temel and Öztürk, Dursun. Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control. Energies, 2017, 10(12), 2134. DOI: 10.3390/en10122134. 17. Scaradozzi, David and Palmieri, Giacomo and Costa, Daniele and Pinelli, Antonio. BCF swimming locomotion for autonomous underwater robots: a review and a novel solution to improve control and efficiency. Ocean Engineering, 2017, 130, 437-453. DOI: http://dx.doi.org/10.1016/j.oceaneng.2016.11.055. 18. Soni, Yogendra Kumar and Bhatt, Rajesh. BF-PSO optimized PID controller design using ISE, IAE, IATE and MSE error criteria. International Journal of Advanced Research in Computer Engineering \& Technology (IJARCET), 2013, 2(7), 2333-2336. 19. Wang, Ming and Yu, JunZhi and Tan, Min and Zhang, JianWei. Multimodal swimming control of a robotic fish with pectoral fins using a CPG network. Chinese science bulletin, 2012, 57(10), 1209-1216. DOI: 10.1007/s11434-012-5005-6. 20. Wang, Wei and Dai, Xia and Li, Liang and Gheneti, Banti H and Ding, Yang and Yu, Junzhi and Xie, Guangming. Three-dimensional modeling of a fin-actuated robotic fish with multimodal swimming. IEEE/ASME Transactions on Mechatronics, 2018, 23(4), 1641-1652. DOI: 10.1109/TMECH.2018.2848220. 21. Wang, Wei and Gu, Dongbing and Xie, Guangming. Autonomous optimization of swimming gait in a fish robot with multiple onboard sensors. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 49(5), 891-903. DOI: 10.1109/TSMC.2017.2683524. 22. Wang, Yong and Xue, Xihui and Chen, Baifan. Matsuoka’s CPG with desired rhythmic signals for adaptive walking of humanoid robots. IEEE transactions on cybernetics, 2018, 50(2),613-626. 23. Xie, Fengran and Du, Ruxu. Central pattern generator control for a biomimetic robot fish in maneuvering. 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018, 268-273. 24. Xie, Fengran and Zhong, Yong and Du, Ruxu and Li, Zheng. Central pattern generator (CPG) control of a biomimetic robot fish for multimodal swimming. Journal of Bionic Engineering, 2019, 16(2), 222-234, DOI: https://doi.org/10.1007/s42235-019-0019-2. 25. Xu, WL and Fang, F Clara and Bronlund, J and Potgieter, J. Generation of rhythmic and voluntary patterns of mastication using Matsuoka oscillator for a humanoid chewing robot. Mechatronics, 2009, 19(2), 205-217, doi:10.1016/j.mechatronics.2008.08.003. 26. Yen, Wei-Kuo and Sierra, Daniel Martinez and Guo, Jenhwa. Controlling a robotic fish to swim along a wall using hydrodynamic pressure feedback. IEEE Journal of Oceanic Engineering, 2018, 43(2), 369-380. DOI: 10.1109/JOE.2017.2785698. 27. Yu, Junzhi and Chen, Shifeng and Wu, Zhengxing and Chen, Xingyu and Wang, Ming. Energy analysis of a CPG-controlled miniature robotic fish. Journal of Bionic Engineering, 2018, 15(2), 260-269, DOI: https://doi.org/10.1007/s42235-018-0020-1. 28. Yu, Junzhi and Tan, Min and Chen, Jian and Zhang, Jianwei. A survey on CPG-inspired control models and system implementation. IEEE transactions on neural networks and learning systems, 2013, 25(3), 441-456. DOI: 10.1109/TNNLS.2013.2280596. 29. Yu, Junzhi and Wang, Ming and Dong, Huifang and Zhang, Yanlu and Wu, Zhengxing. Motion control and motion coordination of bionic robotic fish: A review. Journal of Bionic Engineering, 2018, 15(4), 579-598. DOI: https://doi.org/10.1007/s42235-018-0048-2. 30. Yu, Junzhi and Wu, Zhengxing and Wang, Ming and Tan, Min. 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International journal of advanced robotic systems, 2008, 5(2), 185-19220https://revistascientificas.cuc.edu.co/ingecuc/article/download/5279/5479Núm. 2 , Año 2024 : (Julio-Diciembre)OREORE.xmltext/xml2634https://repositorio.cuc.edu.co/bitstreams/414c814e-799b-4fc6-a6f3-a9c337e6b658/download2c5084b0bff2e2e759863bb844acf31fMD5111323/13906oai:repositorio.cuc.edu.co:11323/139062024-12-13 03:30:14.141http://creativecommons.org/licenses/by-nc-nd/4.0Inge CuC - 2024metadata.onlyhttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.co

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