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Humanoid adaptive locomotion control through a bioinspired CPG-based controller

Published online by Cambridge University Press:  22 June 2021

Chenpeng Yao Open the ORCID record for Chenpeng Yao [Opens in a new window] ,
Chengju Liu ,
Li Xia ,
Ming Liu  and
Qijun Chen
Chenpeng Yao
Affiliation:
Department of Control Science and Engineering, Tongji University, Shanghai201804, China Tongji artificial intelligence (Suzhou) Research Institute, Suzhou215000, China
Chengju Liu*
Affiliation:
Department of Control Science and Engineering, Tongji University, Shanghai201804, China Tongji artificial intelligence (Suzhou) Research Institute, Suzhou215000, China
Li Xia
Affiliation:
Department of Control Science and Engineering, Tongji University, Shanghai201804, China
Ming Liu
Affiliation:
Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong999077, China
Qijun Chen
Affiliation:
Department of Control Science and Engineering, Tongji University, Shanghai201804, China
*
*Corresponding author. Email: liuchengju@tongji.edu.cn
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Abstract

To achieve adaptive gait planning of humanoid robots, a hierarchical central pattern generator (H-CPG) model with a basic rhythmic signal generation layer and a pattern formation layer is proposed to modulate the center of mass (CoM) and the online foot trajectory. The entrainment property of the CPG is exploited for adaptive walking in the absence of a priori knowledge of walking conditions, and the sensory feedback is applied to modulate the generated trajectories online to improve walking adaptability and stability. The developed control strategy is verified using a humanoid robot on sloped terrain and shows good performance.

Keywords

Humanoid robotHierarchical central pattern generator (H-CPG)Adaptive locomotionWorkspace trajectory modulationBionic control
Type
Reply
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 762 - 779
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Grillner, S., “Biological pattern generation: The cellular and computational logic of networks in motion,” Neuron. 52(5), 751–766 (2006).CrossRefGoogle ScholarPubMed
Ijspeert, A. J., “Central pattern generators for locomotion control in animals and robots: A review,” Neural Networks 21(4), 642–653 (2008).CrossRefGoogle ScholarPubMed
Wu, Q., Liu, C., Zhang, J. and Chen, Q., “Survey of locomotion control of legged robots inspired by biological concept,” Sci. Ser. F Inf. Sci. 52(10), 1715–1729 (2009).Google Scholar
Yu, J., Tan, M., Chen, J. and Zhang, J., “A survey on CPG-inspired control models and system implementation,” IEEE Trans. Neural Networks Learn. Syst. 25(3), 441–456 (2013).CrossRefGoogle Scholar
Ryczko, D., KnÜsel, J., Crespi, A., Lamarque, S., Mathou, A., Ijspeert, A. J. and Cabelguen, J.-M., “Flexibility of the axial central pattern generator network for locomotion in the salamander,” J. Neurophysiol. 113(6), 1921–1940 (2015).CrossRefGoogle ScholarPubMed
Wang, Z., Gao, Q. and Zhao, H., “CPG-inspired locomotion control for a snake robot basing on nonlinear oscillators,” J. Intell. Rob. Syst. 85(2), 209–227 (2017).CrossRefGoogle Scholar
Wu, X. and Ma, S., “Neurally controlled steering for collision-free behavior of a snake robot,” IEEE Trans. Control Syst. Technol. 21(6), 2443–2449 (2013).CrossRefGoogle Scholar
Yu, J., Wu, Z., Wang, M. and Tan, M., “CPG network optimization for a biomimetic robotic fish via PSO,” IEEE Trans. Neural Networks Learn. Syst. 27(9), 1962–1968 (2015).CrossRefGoogle ScholarPubMed
Yu, J., Wang, C. and Xie, G., “Coordination of multiple robotic fish with applications to underwater robot competition,” IEEE Trans. Ind. Electr. 63(2), 1280–1288 (2015).CrossRefGoogle Scholar
Wang, T., Hu, Y. and Liang, J., “Learning to swim: A dynamical systems approach to mimicking fish swimming with CPG,” Robotica 31(3), 361 (2013).CrossRefGoogle Scholar
Zhang, X., Gong, J. and Yao, Y., “Effects of head and tail as swinging appendages on the dynamic walking performance of a quadruped robot,” Robotica 34(12), 2878–2891 (2016).CrossRefGoogle Scholar
Zhong, G., Chen, L., Jiao, Z., Li, J. and Deng, H., “Locomotion control and gait planning of a novel hexapod robot using biomimetic neurons,” IEEE Trans. Control Syst. Technol. 26(2), 624–636 (2017).CrossRefGoogle Scholar
Fukui, T., Fujisawa, H., Otaka, K. and Fukuoka, Y., “Autonomous gait transition and galloping over unperceived obstacles of a quadruped robot with CPG modulated by vestibular feedback,” Rob. Auto. Syst. 111, 1–19 (2019).CrossRefGoogle Scholar
Ma, Z., Liang, Y. and Tian, H., “Research on Gait Planning Algorithm of Quadruped Robot Based on Central Pattern Generator,” 2020 39th Chinese Control Conference (CCC) (2020) pp. 3948–3953.Google Scholar
Wang, X., Liu, H., Liang, B., Wang, X. and Yang, J., “Locomotion Control for Quadruped Robot Combining Central Pattern Generators with Virtual Model Control,” 2019 IEEE 15th International Conference on Control and Automation (ICCA) (2019) pp. 399–404.Google Scholar
Zhang, D., Wu, J., Zhu, Q., Xiong, R., Zhang, Y., Zhu, Y. and Xu, J., “A Fuzzy Control Method Based on Central Pattern Generator for Quadrupedrobots,” 2019 Chinese Automation Congress (CAC) (2019) pp. 861–866.Google Scholar
Lele, A. S., Fang, Y., Ting, J. and Raychowdhury, A., “Learning to walk: Bio-mimetic hexapod locomotion via reinforcement based spiking central pattern generation,” IEEE J. Emerging Sel. Top. Circ. Syst. 10(4), 1–1 (2020).Google Scholar
Huang, W., Chew, C.-M. and Hong, G.-S., “A coordination-based CPG structure for 3d walking control,” Robotica 31(5), 777 (2013).CrossRefGoogle Scholar
Saputra, A. A., Botzheim, J., Sulistijono, I. A. and Kubota, N., “Biologically inspired control system for 3-d locomotion of a humanoid biped robot,” IEEE Trans. Syst. Man Cybern. Syst. 46(7), 898–911 (2015).CrossRefGoogle Scholar
Shachykov, A., Shuliak, O. and Hnaff, P., “Closed-Loop Central Pattern Generator Control of Human Gaits in Opensim Simulator,” 2019 International Joint Conference on Neural Networks (IJCNN) (2019) pp. 1–8.Google Scholar
Hong, Y.-D., Park, C.-S. and Kim, J.-H., “Stable bipedal walking with a vertical center-of-mass motion by an evolutionary optimized central pattern generator,” IEEE Trans. Ind. Electr. 61(5), 2346–2355 (2013).CrossRefGoogle Scholar
Juang, C.-F. and Yeh, Y.-T., “Multiobjective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks,” IEEE Trans. Cybern. 48(6), 1910–1922 (2017).CrossRefGoogle ScholarPubMed
Wang, Y., Xue, X. and Chen, B., “Matsuoka’s CPG with desired rhythmic signals for adaptive walking of humanoid robots,” IEEE Trans. Cybern. 50(2), 613–626 (2018).CrossRefGoogle ScholarPubMed
Liu, C., Wang, D., Goodman, E. D. and Chen, Q., “Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators,” J. Bionic Eng. 13(4), 572–584 (2016).CrossRefGoogle Scholar
Taga, G., Yamaguchi, Y. and Shimizu, H., “Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment,” Biol. Cybern. 65(3), 147–159 (1991).CrossRefGoogle ScholarPubMed
Fu, C., Tan, F. and Chen, K., “A simple walking strategy for biped walking based on an intermittent sinusoidal oscillator,” Robotica 28(6), 869 (2010).CrossRefGoogle Scholar
Fukuoka, Y. and Akama, J., “Dynamic bipedal walking of a dinosaur-like robot with an extant vertebrate’s nervous system,” Robotica 32(6), 851 (2014).CrossRefGoogle Scholar
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,” J. Physiol. 48(1), 18 (1914).CrossRefGoogle Scholar
Klein, T. J. and Lewis, M. A., “A physical model of sensorimotor interactions during locomotion,” J. Neural Eng. 9(4), 046011 (2012).CrossRefGoogle ScholarPubMed
Perret, C., Cabelguen, J.-M. and Orsal, D., “Analysis of the Pattern of Activity in “Knee Flexor” Motoneurons During Locomotion in the Cat,” In: Stance and Motion (Springer, 1988) pp. 133–141.CrossRefGoogle Scholar
Burke, R., Degtyarenko, A. and Simon, E., “Patterns of locomotor drive to motoneurons and last-order interneurons: Clues to the structure of the CPG,” J. Neurophysiol. 86(1), 447–462 (2001).CrossRefGoogle Scholar
McCrea, D. A. and Rybak, I. A., “Organization of mammalian locomotor rhythm and pattern generation,” Brain Res. Rev. 57(1), 134–146 (2008).CrossRefGoogle ScholarPubMed
Wang, T., Guo, W., Li, M., Zha, F. and Sun, L., “CPG control for biped hopping robot in unpredictable environment,” J. Bionic Eng. 9(1), 29–38 (2012).CrossRefGoogle Scholar
Auddy, S., Magg, S. and Wermter, S., “Hierarchical Control for Bipedal Locomotion Using Central Pattern Generators and Neural Networks,” 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (2019) pp. 13–18.Google Scholar
Endo, G., Morimoto, J., Matsubara, T., Nakanishi, J. and Cheng, G., “Learning CPG-based biped locomotion with a policy gradient method: Application to a humanoid robot,” Int. J. Rob. Res. 27(2), 213–228 (2008).CrossRefGoogle Scholar
Fu, C., Wang, J., Chen, K., Yu, Z. and Huang, Q., “A walking control strategy combining global sensory reflex and leg synchronization,” Robotica 34(5), 973 (2016).CrossRefGoogle Scholar
AndrÉ, J., Teixeira, C., Santos, C. P. and Costa, L., “Adapting biped locomotion to sloped environments,” J. Intell. Rob. Syst. 80(3–4), 625–640 (2015).CrossRefGoogle Scholar
Liu, C., Chen, Q. and Wang, D., “CPG-inspired workspace trajectory generation and adaptive locomotion control for quadruped robots,” IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(3), 867–880 (2011).Google ScholarPubMed
Liu, C., Wang, D. and Chen, Q., “Central pattern generator inspired control for adaptive walking of biped robots,” IEEE Trans. Syst. Man Cybern. Syst. 43(5), 1206–1215 (2013).Google Scholar
Nassour, J., HÉnaff, P., Benouezdou, F. and Cheng, G., “Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots,” Biol. Cybern. 108(3), 291–303 (2014).CrossRefGoogle ScholarPubMed
Tran, D. H., Hamker, F. and Nassour, J., “A humanoid robot learns to recover perturbation during swinging motion,” IEEE Trans. Syst. Man Cybern. Syst. 50(10), 3701–3712 (2018).CrossRefGoogle Scholar
Mokhtari, M., Taghizadeh, M. and Mazare, M., “Hybrid adaptive robust control based on CPG and ZMP for a lower limb exoskeleton,” Robotica 39(2), 181–199 (2021).CrossRefGoogle Scholar
Liu, C., Xia, L., Zhang, C. and Chen, Q., “Multi-layered CPG for adaptive walking of quadruped robots,” J. Bionic Eng. 15(2), 341–355 (2018).CrossRefGoogle Scholar

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