Advertisement

Biological Cybernetics

, Volume 112, Issue 5, pp 495–508 | Cite as

Application of chaos in a recurrent neural network to control in ill-posed problems: a novel autonomous robot arm

  • Seiji Kuwada
  • Tomoya Aota
  • Kengo Uehara
  • Shigetoshi Nara
Original Article
  • 47 Downloads

Abstract

Inspired by a viewpoint that complex/chaotic dynamics would play an important role in biological systems including the brain, chaotic dynamics introduced in a recurrent neural network was applied to robot control in ill-posed situations. By computer experiments we show that a model robot arm without an advanced visual processing function can catch a target object and bring it to a set position under ill-posed situations (e.g., in the presence of unknown obstacles). The key idea in these works is adaptive switching of a system parameter (connectivity) between a chaos regime and attractor regime in a neural network model, which generates, depending on environmental circumstances, either chaotic motions or definite motions corresponding to embedded attractors. The adaptive switching results in useful functional motions of the robot arm. These successful experiments indicate that chaotic dynamics is potentially useful for practical engineering control applications. In addition, this novel autonomous arm system is implemented in a hardware robot arm that can avoid obstacles and reach for a target in a situation where the robot can get only rough target information, including uncertainty, by means of a few sensors, as indicated in the appendix, A1 and A2.

Keywords

Robot arm Adaptive control Functional chaos Recurrent neural network Ill-posed problem 

Notes

Acknowledgements

This work was supported in part by Grant-in-Aid #26280093 of the Ministry of Education of Science, Sports & Culture of the Japanese government and by the Cooperative Research Program of the Network Joint Research Center for Materials and Devices.

Supplementary material

Supplementary material 1 (wmv 1854 KB)

Supplementary material 2 (wmv 842 KB)

Supplementary material 3 (wmv 582 KB)

References

  1. Aihara K, Takabe T, Toyoda M (1990) Chaotic neural networks. Phys Lett A 114:333–340CrossRefGoogle Scholar
  2. Anderson JA, Rosenfeld E (eds) (1988) NEUROCOMPUTING. The MIT Press, CambridgeGoogle Scholar
  3. Anderson JA, Rosenfeld E (eds) (1990) NEUROCOMPUTING 2. The MIT Press, CambridgeGoogle Scholar
  4. Arhem P, Blomberg C, Liljenström H (2000) Disorder versus order in brain functioning—essays in theoretical neurophysics. World Scientific Publ. Co, LondonCrossRefGoogle Scholar
  5. Babloyantz A, Destexhe A (1986) Low-dimensional chaos in an instance of epilepsy. Proc Natl Acad Sci USA 83:3513–3517CrossRefPubMedCentralPubMedGoogle Scholar
  6. Fujii H, Itoh H, Ichinose N, Tsukada M (1996) Dynamical cell assembly hypothesis—theoretical possibility of spatiotemporal coding in the cortex. Neural Netw 9:1303–1350CrossRefPubMedCentralPubMedGoogle Scholar
  7. Hayashi H, Ishizuka S, Ohta M, Hirakawa K (1982) Chaotic behavior in the Onchidium giant neuron under sinusoidal stimulation. Phys Lett A 88:435–438CrossRefGoogle Scholar
  8. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558CrossRefPubMedCentralPubMedGoogle Scholar
  9. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci USA 81:3088–3092CrossRefPubMedCentralPubMedGoogle Scholar
  10. Huber F, Thorson H (1985) Cricket auditory communication. Sci Am 253:60–68CrossRefGoogle Scholar
  11. Kaneko K, Tsuda I (2003) Chaotic itinerancy. Chaos 13(3):926–936CrossRefPubMedCentralPubMedGoogle Scholar
  12. Kuroiwa J, Nara S, Aihara K (1999) Functional possibility of chaotic behaviour in a single chaotic neuron model for dynamical signal processing elements. In: 1999 IEEE international conference on systems, man, and cybernetics (SMC’99), Tokyo, October, 1999, vol 1, p 290Google Scholar
  13. Li Y, Kurata S, Morita S, Shimizu S, Munetaka D, Nara S (2008) Application of chaotic dynamics in a recurrent neural network to control: hardware implementation into a novel autonomous roving robot. Biol Cybern 99:185–196CrossRefPubMedCentralPubMedGoogle Scholar
  14. Liljenström H (1995) Autonomous learning with complex dynamics. Int J Intell Syst 10:119–153CrossRefGoogle Scholar
  15. Nara S (2003) Can potentially useful dynamics to solve complex problems emerge from constrained chaos and/or chaotic itinerancy? Chaos 13(3):1110–1121CrossRefPubMedCentralPubMedGoogle Scholar
  16. Nara S, Davis P (1992) Chaotic wandering and search in a cycle memory neural network. Prog Theor Phys 88:845–855CrossRefGoogle Scholar
  17. Nara S, Davis P (1997) Learning feature constraints in a chaotic neural memory. Phys Rev E 55:826–830CrossRefGoogle Scholar
  18. Nara S, Davis P, Kawachi M, Totuji H (1993) Memory search using complex dynamics in a recurrent neural network model. Neural Netwo 6:963–973CrossRefGoogle Scholar
  19. Nara S, Davis P, Kawachi M, Totuji H (1995) Chaotic memory dynamics in a recurrent neural network with cycle memories embedded by pseudo-inverse method. Int J Bifurc Chaos Appl Sci Eng 5:1205–1212CrossRefGoogle Scholar
  20. Nicolelis MAL (2001) Actions from thoughts. Nature 409:403–407CrossRefPubMedCentralPubMedGoogle Scholar
  21. Physiome (2012) http://www.physiome.jp/index.html. It should be noted that, generally speaking, Platform sites in web-system is often not permanent but rather improved and/or changed occasionally. So, readers should be careful when they access via internet
  22. Skarda CA, Freeman WJ (1987) How brains make chaos in order to make sense of the world. Behav Brain Sci 10:161–195CrossRefGoogle Scholar
  23. Soma K, Mori R, Sato R, Furumai N, Nara S (2015) Simultaneous multichannel signal transfers via chaos in a recurrent neural network. Neural Comput 27:1083–1101CrossRefPubMedCentralPubMedGoogle Scholar
  24. Suemitsu Y, Nara S (2004) A solution for two-dimensional mazes with use of chaotic dynamics in a recurrent neural network model. Neural Comput 16(9):1943–1957CrossRefPubMedCentralPubMedGoogle Scholar
  25. Suemitsu Y, Nara S (2005) Emergence of unstable itinerant orbits in a recurrent neural network model. Phys Lett A 344(2):220–228CrossRefGoogle Scholar
  26. Tsuda I (1991) Chaotic itinerancy as a dynamical basis of Hermeneutics in brain and mind. World Futures 32:167–184CrossRefGoogle Scholar
  27. Tsuda I (2001) Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. Behav Brain Sci 24(5):793–847CrossRefPubMedCentralPubMedGoogle Scholar
  28. Yao Y, Freeman WJ (1990) Model of biological pattern recognition with spatially chaotic dynamics. Neural Netw 3:153–170CrossRefGoogle Scholar
  29. Yoshinaka R, Kawashima M, Nabeta K, Li Y, Nara S (2012) Adaptive control of robot systems with simple rules using chaotic dynamics in quasi-layered recurrent neural networks. In: Madani K, Correia AD, Rosa A, Filipe J (eds) Computational intelligence. Springer, Berlin Heidelberg, pp 287–305CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Electronic Engineering, Graduate School of Natural Science and TechnologyOkayama UniversityKita-kuJapan

Personalised recommendations