Skip to main content
SpringerLink
Log in

Synthesis of Reference Trajectories for Humanoid Robot Supported by Genetic Algorithm

  • Chapter
  • First Online:
Nature-Inspired Methods for Metaheuristics Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 16))

  • 707 Accesses

Abstract

This work presents biologically inspired method of gait generation. It uses the reference to the periodic signals generated by biological Central Pattern Generator (CPG). The coupled oscillators with correction functions are used to produce leg joint trajectories. The human gait is used as the reference pattern. The features of generated gait are compared to the human walk. The example illustrates well the profit offered by the optimization using genetic algorithm. The problem would be impossible to solve using traditional approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barricelli NA (1954) Esempi numerici di processi di evoluzione. Methods 6:45–68

    MathSciNet  Google Scholar 

  2. Barricelli NA (1957) Symbiogenetic evolution processes realized by artificial methods. Methods 9:143–182

    Google Scholar 

  3. Bay JS, Hemami H (1987) Modeling of a neural pattern generator with coupled nonlinear oscillators. IEEE Trans Biomed Eng BME-34(4):297–306

    Article  Google Scholar 

  4. Buchli J, Righetti L, Ijspeert AJ (2006) Engineering entrainment and adaptation in limit circle systems. From bilogical inspiration to application in robotics. Biol Cybern 95:645–664

    Article  Google Scholar 

  5. Crosby JL (1973) Computer simulation in genetics. Wiley, London

    MATH  Google Scholar 

  6. Fogel DB (ed) (1998) Evolutionary computation: the fossil record. IEEE, New York

    MATH  Google Scholar 

  7. Fraser A (1957) Simulation of genetic systems by automatic digital computers. I. Introduction. Aust J Biol Sci 10:492–499

    Article  Google Scholar 

  8. Fraser A, Burnell D (1970) Computer models in genetics. McGraw-Hill, New York

    Google Scholar 

  9. Harada K, Yoshida E, Yokoi K (eds) (2010) Motion planning for humanoid robots. Springer

    MATH  Google Scholar 

  10. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT (reprint)

    Google Scholar 

  11. Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots; a review. Preprint of Neural Networks 21/4:642–653

    Article  Google Scholar 

  12. Kulic D, Venture G, Yamane K, Demircan E, Mizuuchi I, Mombaur K (2016) Anthropomorphic movement analysis and synthesis: a survey of methods and applications. IEEE Trans Robot 32(4):776–795

    Article  Google Scholar 

  13. Nakanishi J, Morimoto J, Endo G, Cheng G, Schaal S, Kawato M (2004) Learning from demonstration and adaptation of biped locomotion. Robot Auton Syst 47:79–91

    Article  Google Scholar 

  14. Ouezdou FB, Konno A et al (2002) ROBIAN biped project – a tool for the analysis of the human-being locomotion system. In: Proceedings of the 5th international conference on climbing and walking robots

    Google Scholar 

  15. Santos CP, Juan NA, Moreno C (2017) Biped locomotion control through a biomimetic CPG-based controller. J Intell Robotic Syst 85(1):47–70

    Article  Google Scholar 

  16. Turing AM (1950) Computing machinery and intelligence. Mind LIX(238):433–460

    Article  MathSciNet  Google Scholar 

  17. Vaughan ChL, Davis BL, O’Connor JC (1992) Dynamics of human gait. Champaign: Human Kinetics Publishers

    Google Scholar 

  18. Vukobratovic M, Borovac B (2004) Zero-moment point – thirty five years of its life. Int J HR 1(1):157–173

    Google Scholar 

  19. Vukobratovic M, Stepanenko Y (1972) On the stability of anthropomorphic systems. Math Biosci 15:1–37

    Article  Google Scholar 

  20. Winter DA (1991) Biomechanics and motor control of human gait: normal, elderly and pathological. University of Waterloo, Ontario

    Google Scholar 

  21. Zielinska T (1996) Coupled oscillators utilized as gait rhythm generators of a two-legged walking machine. Biol Cybern 74:263–273

    Article  Google Scholar 

  22. Zielinska T, Chew ChM, Kryczka P, Jargilo T (2009) Robot gait synthesis using the scheme of human motion skills development. Mech Mach Theory 44(3):541–558

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Power and Aerospace Engineering, Warsaw University of Technology, Warsaw, Poland

    Teresa Zielinska

Authors
  1. Teresa Zielinska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Teresa Zielinska .

Editor information

Editors and Affiliations

  1. Ecole Central de Nantes, Nantes cedex 3, France

    Fouad Bennis

  2. Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Rajib Kumar Bhattacharjya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zielinska, T. (2020). Synthesis of Reference Trajectories for Humanoid Robot Supported by Genetic Algorithm. In: Bennis, F., Bhattacharjya, R. (eds) Nature-Inspired Methods for Metaheuristics Optimization. Modeling and Optimization in Science and Technologies, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-26458-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26458-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26457-4

  • Online ISBN: 978-3-030-26458-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation