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All issues Volume 413 (2025) MATEC Web Conf., 413 (2025) 04001 References
Open Access
Issue
MATEC Web Conf.
Volume 413, 2025
International Conference on Measurement, AI, Quality and Sustainability (MAIQS 2025)
Article Number 04001
Number of page(s) 6
Section Artificial Intelligence and Robotics
DOI https://doi.org/10.1051/matecconf/202541304001
Published online 01 October 2025
  1. J. Song, Research on Multi-Mode Gait Simulation and Control Algorithms for Quadruped Robots, Master Thesis, Jilin University, 2024. [Google Scholar]
  2. C. Park, J. Pan, & D. Manocha. Real-time optimization-based planning in dynamic environments using GPUs, In Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 610(2013), pp.4090-4097 [Google Scholar]
  3. M. Hernando, M. Alonso, C. Prados, et al., Behavior-based control architecture for legged- and-climber robots. Appl. Sci. 11, 9547 (2021). https://doi.org/10.3390/app11209547 [Google Scholar]
  4. R. Zhang, Q. Lv, J. Li, et al., A reinforcement learning method for human-robot collaboration in assembly tasks. Robot. Comput.-Integr. Manuf . 73, 102227 (2022). https://doi.org/10.1016/j.rcim.2021.102227 [Google Scholar]
  5. S. Grillner, A. El Manira, Current principles of motor control, with special reference to vertebrate locomotion. Physiol. Rev. 100, 273–320 (2019). https://doi.org/10.1152/physrev.00015.2019 [Google Scholar]
  6. X. Zhang, Biological-inspired Rhythmic Motion & Environmental Adaptability for Quadruped Robot, Ph.D. thesis, Tsinghua University (2004). [Google Scholar]
  7. L. Bai, H. Hu, X. Chen, et al., CPG-based gait generation of the curved-leg hexapod robot with smooth gait transition. Sensors. 19, 3705 (2019). https://doi.org/10.3390/s19173705 [Google Scholar]
  8. R. Tong, C. Qiu, Z. Wu, et al., NA-CPG: A robust and stable rhythm generator for robot motion control. Biomimetic Intell. Robot. 2, 100075 (2022). https://doi.org/10.1016/j.birob.2022.100075 [Google Scholar]
  9. S. Aoi, K. Tsuchiya, Stability analysis of a simple walking model driven by an oscillator with a phase reset using sensory feedback. IEEE Trans. Robot . 22, 391–397 (2006). https://doi.org/10.1109/TRO.2006.870671 [Google Scholar]
  10. Y. Zhu, Y. Wu, Q. Liu, et al., A backward control based on σ-Hopf oscillator with decoupled parameters for smooth locomotion of bio-inspired legged robot. Robot. Auton. Syst. 106, 165–178 (2018). https://doi.org/10.1016/j.robot.2018.05.009 [Google Scholar]
  11. A. J. Ijspeert, Central pattern generators for locomotion control in animals and robots: a review. Neural Networks. 21, 642–653 (2008). https://doi.org/10.1016/j.neunet.2008.03.014 [Google Scholar]
  12. H. Xu, S. Gan, J. Ren, B. Wang, Y. Jin, Gait CPG Adjustment for a Quadruped Robot Based on Hopf Oscillator. J. Syst. Simul. 29, 3092-3099(2017). https://doi.org/10.16182/j.issn1004731x.joss.201712021 [Google Scholar]
  13. K. Matsuoka, Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics. 52, 367–376 (1985). https://doi.org/10.1007/BF00449593 [Google Scholar]
  14. J. Yu, M. Tan, J. Chen, et al., A survey on CPG- inspired control models and system implementation. IEEE Trans. Neural Networks Learn. Syst. 25, 441–456 (2013). https://doi.org/10.1109/TNNLS.2013.2280596 [Google Scholar]
  15. C. Y. Duan, H. Y. Wei, P. F. Yan, et al., A Review of Motion Control Methods for Rigid Quadruped and Hexapod Robots. Technol. Ind. 24, 218225(2024). https://doi.org/10.3969/j.issn.1671-1807.2024.18.033 [Google Scholar]
  16. C. Liu, L. Xia, C. Zhang, et al., Multi-Layered CPG for Adaptive Walking of Quadruped Robots. J Bionic Eng. 15, 341–355(2018). https://doi.org/10.1007/s42235-018-0026-8 [Google Scholar]
  17. C. Bal, Neural coupled central pattern generator based smooth gait transition of a biomimetic hexapod robot. Neurocomputing, 420, 210–226 (2021). https://doi.org/10.1016/j.neucom.2020.07.114 [Google Scholar]
  18. K. Minassian, U. S. Hofstoetter, F. Dzeladini, et al., The human central pattern generator for locomotion: does it exist and contribute to walking?. Neuroscientist, 23, 649-663(2017). https://doi.org/10.1177/1073858417699790 [Google Scholar]
  19. C. Liu, D. Wang, E. D. Goodman. et al. Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators. J Bionic Eng. 13, 572–584(2016). https://doi.org/10.1016/S1672-6529(16)60329-3 [Google Scholar]
  20. C. Liu, W. Geng, C. Zhang, et al. Adaptive Locomotion Control of Humanoid Robot Based on Self-Learning CPG. Acta Autom. Sin. 47, 2170- 2181(2021). https://doi.org/10.16383/j.aas.c190087 [Google Scholar]
  21. J. Duysens, A. Forner-Cordero. A controller perspective on biological gait control: Reflexes and central pattern generators. Annu. Rev. Control. 48, 392-400(2019). https://doi.org/10.1016/j.arcontrol.2019.04.004 [Google Scholar]
  22. J. Sun, Parameters Optimization of CPG for Hexapod Robot based on Multi-objective Genetic Algorithm, Master Thesis, China Jiliang University, 2019 [Google Scholar]
  23. M. Wang, H. Dong, X. Li, et al. Control and optimization of a bionic robotic fish through a combination of CPG model and PSO. Neurocomputing, 337, 144-152(2019). https://doi.org/10.1016/j.neucom.2019.01.062 [Google Scholar]
  24. Y. Wang, X. Xue, B. Chen. Matsuoka’s CPG with desired rhythmic signals for adaptive walking of humanoid robots. IEEE Trans. Cybern. 50, 613626(2018). https://doi.org/10.1109/TCYB.2018.2870145 [Google Scholar]
  25. X. Liu, R. Gasoto, Z. Jiang, et al., Learning to locomote with artificial neural-network and CPG- based control in a soft snake robot, in Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, October 25-29(2020), pp. 77587765 [Google Scholar]

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