Journal of Bionic Engineering

, Volume 15, Issue 2, pp 341–355 | Cite as

Multi-Layered CPG for Adaptive Walking of Quadruped Robots

  • Chengju Liu
  • Li Xia
  • Changzhu Zhang
  • Qijun Chen


This work concerns biped adaptive walking control on slope terrains with online trajectory generation. In terms of the lack of satisfactory performances of the traditional simplified single-layered Central Pattern Generator (CPG) model in engineering applications where robots face unknown environments and access feedback, this paper presents a Multi-Layered CPG (ML-CPG) model based on a half-center CPG model. The proposed ML-CPG model is used as the underlying low-level controller for a quadruped robot to generate adaptive walking patterns. Rhythm-generation and pattern formation interneurons are modeled to promptly generate motion rhythm and patterns for motion sequence control, while motoneurons are modeled to control the output strength of the joint in real time according to feedback. Referring to the motion control mechanisms of animals, a control structure is built for a quadruped robot. Multi-sensor models abstracted from the neural reflexes of animals are involved in all the layers of neurons through various feedback paths to achieve adaptability as well as the coordinated motion control of a robot’s limbs. The simulation experiments verify the effectiveness of the presented ML-CPG and multi-layered reflexes strategy.


Multi-Layered CPG (ML-CPG) biological reflex quadruped robot adaptive walking 


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This work was supported by the National Natural Science Foundation of China (Grant Nos. U1713211 and 61673300)).


  1. [1]
    Beer R D, Chiel H J, Gallagher J C. Evolution and analysis of model CPGs for walking: II. General principles and individual variability. Journal of Computational Neuroscience, 1999, 7, 119–147.Google Scholar
  2. [2]
    Hooper S L. Central pattern generators. Current Biology, 2000, 10, R176–R177.CrossRefGoogle Scholar
  3. [3]
    Wu Q D, Liu C J, Zhang J Q, Chen Q J. Survey of locomotion control of legged robots inspired by biological concept. Science in China Series F: Information Sciences, 2009, 52, 1715–1729.CrossRefzbMATHGoogle Scholar
  4. [4]
    Liu C, Chen Q, Wang D. CPG-inspired workspace trajectory generation and adaptive locomotion control for quadruped robots. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 41, 867–880.CrossRefGoogle Scholar
  5. [5]
    Liu C, Wang D, Chen Q. Central pattern generator inspired control for adaptive walking of biped robots. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 43, 1206–1215.CrossRefGoogle Scholar
  6. [6]
    Zhong G L, Chen L, Jiao Z D, Deng H. Locomotion control and gait planning of a novel hexapod robot using biomimetic neurons. IEEE Transactions on Control Systems Technology, 2018, 26, 624–636.CrossRefGoogle Scholar
  7. [7]
    Yang K. Dynamic model and CPG network generation of the underwater self-reconfigurable robot. Advanced Robotics, 2016, 30, 925–937.CrossRefGoogle Scholar
  8. [8]
    Liu C, Wang D, Goodman E D, Chen Q J. Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators. Journal of Bionic Engineering, 2016, 13, 572–584.CrossRefGoogle Scholar
  9. [9]
    Yu J, Wu Z, Wang M, Tan M. CPG network optimization for a biomimetic robotic fish via PSO. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27, 1962–1968.MathSciNetCrossRefGoogle Scholar
  10. [10]
    Hodgkin A L, Huxley A F. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. Journal of Physiology, 1952, 116, 449–472.CrossRefGoogle Scholar
  11. [11]
    Wang H, Yu Y G, Wang S, Yu J. Bifurcation analysis of a two-dimensional simplified Hodgkin–Huxley model exposed to external electric fields. Neural Computing and Applications, 2014, 24, 37–44.CrossRefGoogle Scholar
  12. [12]
    FitzHugh R. Impulses and physiological state in theoretical models of nerve membrane. Biophysical Journal, 1961, 1, 445–466.CrossRefGoogle Scholar
  13. [13]
    Nagumo J, Arimoto S, Yoshizawa S. An active pulse transmission line simulating nerve axon. Proceedings of the IRE, 1962, 50, 2061–2070.CrossRefGoogle Scholar
  14. [14]
    Morris C, Lecar H. Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 1981, 35, 193–213.CrossRefGoogle Scholar
  15. [15]
    Matsuoka K. Mechanism of frequency and pattern control in the neural rhythm generators. Biological Cybernetics, 1987, 56, 345–353.CrossRefGoogle Scholar
  16. [16]
    Taga G, Yamaguehi Y, Shimizu H. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics, 1991, 65, 147–159.CrossRefzbMATHGoogle Scholar
  17. [17]
    Fukuoka Y, Kimura H. Dynamic locomotion of a biomorphic quadruped ‘Tekken’ robot using various gaits: Walk, trot, free-gait and bound. Applied Bionics and Biomechanics, 2009, 6, 1–9.CrossRefGoogle Scholar
  18. [18]
    Zhang X L, Mingcheng E, Zeng X Y, Zheng H J. Adaptive walking of a quadrupedal robot based on layered biological reflexes. Chinese Journal of Mechanical Engineering, 2012, 25, 654–664.CrossRefGoogle Scholar
  19. [19]
    Acebrón J A, Bonilla L L, Vicente C J P, Ritort F, Spigler R. The Kuramoto model: A simple paradigm for synchronization phenomena. Reviews of Modern Physics, 2005, 77, 137–185.CrossRefGoogle Scholar
  20. [20]
    Hopfield J J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 1982, 79, 2554–2558.MathSciNetCrossRefzbMATHGoogle Scholar
  21. [21]
    Manzoor S, Choi Y. A unified neural oscillator model for various rhythmic locomotions of snake-like robot. Neurocomputing, 2016, 173, 1112–1123.CrossRefGoogle Scholar
  22. [22]
    Brown T G. On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system. The Journal of Physiology, 1914, 48, 18–46.CrossRefGoogle Scholar
  23. [23]
    Perret C, Cabelguen J M, Orsal D. Analysis of the pattern of activity in “Knee Flexor” motoneurons during locomotion in the cat. In: Gurfinkel V S, Ioffe M E, Massion J, Roll J P, eds., Stance and Motion, Springer, Boston, USA, 1988, 133–141.CrossRefGoogle Scholar
  24. [24]
    Burke R E, Degtyarenko A M, Simon E S. Patterns of locomotor drive to motoneurons and last-order interneurons: Clues to the structure of the CPG. Journal of Neurophysiology, 2001, 86, 447–462.CrossRefGoogle Scholar
  25. [25]
    Wang T, Guo W, Li M, Zha F, Sun L. CPG control for biped hopping robot in unpredictable environment. Journal of Bionic Engineering, 2012, 9, 29–38.CrossRefGoogle Scholar
  26. [26]
    Manoonpong P, Pasemann F, Wörgötter F. Sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines. Robotics and Autonomous Systems, 2008, 56, 265–288.CrossRefGoogle Scholar
  27. [27]
    Noble F K, Potgieter J, Xu W L. Modelling and simulations of a central pattern generator controlled, antagonistically actuated limb joint. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Anchorage, USA, 2011, 2898–2903.Google Scholar
  28. [28]
    Nassour J, Hénaff P, Benouezdou F, Cheng G. Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots. Biological Cybernetics, 2014, 108, 291–303.CrossRefGoogle Scholar
  29. [29]
    McCrea D A, Rybak I A. Organization of mammalian locomotor rhythm and pattern generation. Brain Research Reviews, 2008, 57, 134–146.CrossRefGoogle Scholar
  30. [30]
    Hopfield J J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 1982, 79, 2554–2558.MathSciNetCrossRefzbMATHGoogle Scholar
  31. [31]
    Yang H, Dillon T. Exponential stability and oscillation of Hopfield graded response neural network. IEEE Transactions on Neural Networks, 1994, 5, 719–729.CrossRefGoogle Scholar

Copyright information

© Jilin University 2018

Authors and Affiliations

  • Chengju Liu
    • 1
  • Li Xia
    • 1
  • Changzhu Zhang
    • 1
  • Qijun Chen
    • 1
  1. 1.School of Electronics and Information EngineeringTongji UniversityShanghaiChina

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